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r=a.pendingProps,u=r.revealOrder,m=r.tail;if(ra(e,a,r.children,i),r=Te.current,(r&2)!==0)r=r&1|2,a.flags|=128;else{if(e!==null&&(e.flags&128)!==0)e:for(e=a.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&ym(e,i,a);else if(e.tag===19)ym(e,i,a);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===a)break e;for(;e.sibling===null;){if(e.return===null||e.return===a)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Me(Te,r),(a.mode&1)===0)a.memoizedState=null;else switch(u){case"forwards":for(i=a.child,u=null;i!==null;)e=i.alternate,e!==null&&zs(e)===null&&(u=i),i=i.sibling;i=u,i===null?(u=a.child,a.child=null):(u=i.sibling,i.sibling=null),el(a,!1,u,i,m);break;case"backwards":for(i=null,u=a.child,a.child=null;u!==null;){if(e=u.alternate,e!==null&&zs(e)===null){a.child=u;break}e=u.sibling,u.sibling=i,i=u,u=e}el(a,!0,i,null,m);break;case"together":el(a,!1,null,null,void 0);break;default:a.memoizedState=null}return a.child}function Ks(e,a){(a.mode&1)===0&&e!==null&&(e.alternate=null,a.alternate=null,a.flags|=2)}function an(e,a,i){if(e!==null&&(a.dependencies=e.dependencies),_n|=a.lanes,(i&a.childLanes)===0)return null;if(e!==null&&a.child!==e.child)throw Error(s(153));if(a.child!==null){for(e=a.child,i=Mn(e,e.pendingProps),a.child=i,i.return=a;e.sibling!==null;)e=e.sibling,i=i.sibling=Mn(e,e.pendingProps),i.return=a;i.sibling=null}return a.child}function hb(e,a,i){switch(a.tag){case 3:fm(a),yt();break;case 5:Ec(a);break;case 1:ca(a.type)&&xs(a);break;case 4:To(a,a.stateNode.containerInfo);break;case 10:var r=a.type._context,u=a.memoizedProps.value;Me(Ms,r._currentValue),r._currentValue=u;break;case 13:if(r=a.memoizedState,r!==null)return r.dehydrated!==null?(Me(Te,Te.current&1),a.flags|=128,null):(i&a.child.childLanes)!==0?bm(e,a,i):(Me(Te,Te.current&1),e=an(e,a,i),e!==null?e.sibling:null);Me(Te,Te.current&1);break;case 19:if(r=(i&a.childLanes)!==0,(e.flags&128)!==0){if(r)return vm(e,a,i);a.flags|=128}if(u=a.memoizedState,u!==null&&(u.rendering=null,u.tail=null,u.lastEffect=null),Me(Te,Te.current),r)break;return null;case 22:case 23:return a.lanes=0,pm(e,a,i)}return an(e,a,i)}var Am,al,xm,Lm;Am=function(e,a){for(var i=a.child;i!==null;){if(i.tag===5||i.tag===6)e.appendChild(i.stateNode);else if(i.tag!==4&&i.child!==null){i.child.return=i,i=i.child;continue}if(i===a)break;for(;i.sibling===null;){if(i.return===null||i.return===a)return;i=i.return}i.sibling.return=i.return,i=i.sibling}},al=function(){},xm=function(e,a,i,r){var u=e.memoizedProps;if(u!==r){e=a.stateNode,qn(Fa.current);var m=null;switch(i){case"input":u=jr(e,u),r=jr(e,r),m=[];break;case"select":u=q({},u,{value:void 0}),r=q({},r,{value:void 0}),m=[];break;case"textarea":u=zr(e,u),r=zr(e,r),m=[];break;default:typeof u.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=ys)}Er(i,r);var k;i=null;for(j in u)if(!r.hasOwnProperty(j)&&u.hasOwnProperty(j)&&u[j]!=null)if(j==="style"){var v=u[j];for(k in v)v.hasOwnProperty(k)&&(i||(i={}),i[k]="")}else j!=="dangerouslySetInnerHTML"&&j!=="children"&&j!=="suppressContentEditableWarning"&&j!=="suppressHydrationWarning"&&j!=="autoFocus"&&(d.hasOwnProperty(j)?m||(m=[]):(m=m||[]).push(j,null));for(j in r){var x=r[j];if(v=u?.[j],r.hasOwnProperty(j)&&x!==v&&(x!=null||v!=null))if(j==="style")if(v){for(k in v)!v.hasOwnProperty(k)||x&&x.hasOwnProperty(k)||(i||(i={}),i[k]="");for(k in x)x.hasOwnProperty(k)&&v[k]!==x[k]&&(i||(i={}),i[k]=x[k])}else i||(m||(m=[]),m.push(j,i)),i=x;else j==="dangerouslySetInnerHTML"?(x=x?x.__html:void 0,v=v?v.__html:void 0,x!=null&&v!==x&&(m=m||[]).push(j,x)):j==="children"?typeof x!="string"&&typeof x!="number"||(m=m||[]).push(j,""+x):j!=="suppressContentEditableWarning"&&j!=="suppressHydrationWarning"&&(d.hasOwnProperty(j)?(x!=null&&j==="onScroll"&&je("scroll",e),m||v===x||(m=[])):(m=m||[]).push(j,x))}i&&(m=m||[]).push("style",i);var j=m;(a.updateQueue=j)&&(a.flags|=4)}},Lm=function(e,a,i,r){i!==r&&(a.flags|=4)};function Li(e,a){if(!ze)switch(e.tailMode){case"hidden":a=e.tail;for(var i=null;a!==null;)a.alternate!==null&&(i=a),a=a.sibling;i===null?e.tail=null:i.sibling=null;break;case"collapsed":i=e.tail;for(var r=null;i!==null;)i.alternate!==null&&(r=i),i=i.sibling;r===null?a||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function na(e){var a=e.alternate!==null&&e.alternate.child===e.child,i=0,r=0;if(a)for(var u=e.child;u!==null;)i|=u.lanes|u.childLanes,r|=u.subtreeFlags&14680064,r|=u.flags&14680064,u.return=e,u=u.sibling;else for(u=e.child;u!==null;)i|=u.lanes|u.childLanes,r|=u.subtreeFlags,r|=u.flags,u.return=e,u=u.sibling;return e.subtreeFlags|=r,e.childLanes=i,a}function fb(e,a,i){var r=a.pendingProps;switch(Lo(a),a.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return na(a),null;case 1:return ca(a.type)&&As(),na(a),null;

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m=a.memoizedProps;switch(r[Va]=a,r[pi]=m,e=(a.mode&1)!==0,i){case"dialog":je("cancel",r),je("close",r);break;case"iframe":case"object":case"embed":je("load",r);break;case"video":case"audio":for(u=0;u<ui.length;u++)je(ui[u],r);break;case"source":je("error",r);break;case"img":case"image":case"link":je("error",r),je("load",r);break;case"details":je("toggle",r);break;case"input":tu(r,m),je("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!m.multiple},je("invalid",r);break;case"textarea":ru(r,m),je("invalid",r)}Er(i,m),u=null;for(var k in m)if(m.hasOwnProperty(k)){var v=m[k];k==="children"?typeof v=="string"?r.textContent!==v&&(m.suppressHydrationWarning!==!0&&bs(r.textContent,v,e),u=["children",v]):typeof v=="number"&&r.textContent!==""+v&&(m.suppressHydrationWarning!==!0&&bs(r.textContent,v,e),u=["children",""+v]):d.hasOwnProperty(k)&&v!=null&&k==="onScroll"&&je("scroll",r)}switch(i){case"input":un(r),su(r,m,!0);break;case"textarea":un(r),lu(r);break;case"select":case"option":break;default:typeof m.onClick=="function"&&(r.onclick=ys)}r=u,a.updateQueue=r,r!==null&&(a.flags|=4)}else{k=u.nodeType===9?u:u.ownerDocument,e==="http://www.w3.org/1999/xhtml"&&(e=du(i)),e==="http://www.w3.org/1999/xhtml"?i==="script"?(e=k.createElement("div"),e.innerHTML="<script><\/script>",e=e.removeChild(e.firstChild)):typeof r.is=="string"?e=k.createElement(i,{is:r.is}):(e=k.createElement(i),i==="select"&&(k=e,r.multiple?k.multiple=!0:r.size&&(k.size=r.size))):e=k.createElementNS(e,i),e[Va]=a,e[pi]=r,Am(e,a,!1,!1),a.stateNode=e;e:{switch(k=Rr(i,r),i){case"dialog":je("cancel",e),je("close",e),u=r;break;case"iframe":case"object":case"embed":je("load",e),u=r;break;case"video":case"audio":for(u=0;u<ui.length;u++)je(ui[u],e);u=r;break;case"source":je("error",e),u=r;break;case"img":case"image":case"link":je("error",e),je("load",e),u=r;break;case"details":je("toggle",e),u=r;break;case"input":tu(e,r),u=jr(e,r),je("invalid",e);break;case"option":u=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},u=q({},r,{value:void 0}),je("invalid",e);break;case"textarea":ru(e,r),u=zr(e,r),je("invalid",e);break;default:u=r}Er(i,u),v=u;for(m in v)if(v.hasOwnProperty(m)){var x=v[m];m==="style"?mu(e,x):m==="dangerouslySetInnerHTML"?(x=x?x.__html:void 0,x!=null&&uu(e,x)):m==="children"?typeof x=="string"?(i!=="textarea"||x!=="")&&Ut(e,x):typeof x=="number"&&Ut(e,""+x):m!=="suppressContentEditableWarning"&&m!=="suppressHydrationWarning"&&m!=="autoFocus"&&(d.hasOwnProperty(m)?x!=null&&m==="onScroll"&&je("scroll",e):x!=null&&K(e,m,x,k))}switch(i){case"input":un(e),su(e,r,!1);break;case"textarea":un(e),lu(e);break;case"option":r.value!=null&&e.setAttribute("value",""+Ae(r.value));break;case"select":e.multiple=!!r.multiple,m=r.value,m!=null?it(e,!!r.multiple,m,!1):r.defaultValue!=null&&it(e,!!r.multiple,r.defaultValue,!0);break;default:typeof u.onClick=="function"&&(e.onclick=ys)}switch(i){case"button":case"input":case"select":case"textarea":r=!!r.autoFocus;break e;case"img":r=!0;break e;default:r=!1}}r&&(a.flags|=4)}a.ref!==null&&(a.flags|=512,a.flags|=2097152)}return na(a),null;case 6:if(e&&a.stateNode!=null)Lm(e,a,e.memoizedProps,r);else{if(typeof r!="string"&&a.stateNode===null)throw Error(s(166));if(i=qn(bi.current),qn(Fa.current),Ss(a)){if(r=a.stateNode,i=a.memoizedProps,r[Va]=a,(m=r.nodeValue!==i)&&(e=ka,e!==null))switch(e.tag){case 3:bs(r.nodeValue,i,(e.mode&1)!==0);break;case 5:e.memoizedProps.suppressHydrationWarning!==!0&&bs(r.nodeValue,i,(e.mode&1)!==0)}m&&(a.flags|=4)}else r=(i.nodeType===9?i:i.ownerDocument).createTextNode(r),r[Va]=a,a.stateNode=r}return na(a),null;case 13:if(Ce(Te),r=a.memoizedState,e===null||e.memoizedState!==null&&e.memoizedState.dehydrated!==null){if(ze&&ba!==null&&(a.mode&1)!==0&&(a.flags&128)===0)Sc(),yt(),a.flags|=98560,m=!1;else if(m=Ss(a),r!==null&&r.dehydrated!==null){if(e===null){if(!m)throw Error(s(318));if(m=a.memoizedState,m=m!==null?m.dehydrated:null,!m)throw Error(s(317));m[Va]=a}else yt(),(a.flags&128)===0&&(a.memoizedState=null),a.flags|=4;na(a),m=!1}else za!==null&&(ml(za),za=null),m=!0;if(!m)return a.flags&65536?a:null}return(a.flags&128)!==0?(a.lanes=i,a):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(a.child.flags|=8192,(a.mode&1)!==0&&(e===null||(Te.current&1)!==0?qe===0&&(qe=3):hl())),a.updateQueue!==null&&(a.flags|=4),na(a),null);case 4:return Lt(),al(e,a),e===null&&ci(a.stateNode.containerInfo),na(a),null;case 10:return jo(a.type._context),na(a),null;case 17:return ca(a.type)&&As(),na(a),null;case 19:if(Ce(Te),m=a.memoizedState,m===null)return na(a),null;if(r=(a.flags&128)!==0,k=m.rendering,k===null)if(r)Li(m,!1);else{if(qe!==0||e!==null&&(e.flags&128)!==0)for(e=a.child;e!==null;){if(k=zs(e),k!==null){for(a.flags|=128,Li(m,!1),r=k.updateQueue,r!==null&&(a.updateQueue=r,a.flags|=4),a.subtreeFlags=0,r=i,i=a.child;i!==null;)m=i,e=r,m.flags&=14680066,k=m.alternate,k===null?(m.childLanes=0,m.lanes=e,m.child=null,m.subtreeFlags=0,m.memoizedProps=null,m.memoizedState=null,m.updateQueue=null,m.dependencies=null,m.stateNode=null):(m.childLanes=k.childLanes,m.lanes=k.lanes,m.child=k.child,m.subtreeFlags=0,m.deletions=null,m.memoizedProps=k.memoizedProps,m.memoizedState=k.memoizedState,m.updateQueue=k.updateQueue,m.type=k.type,e=k.dependencies,m.dependencies=e===null?null:{lanes:e.lanes,firstContext:e.firstContext}),i=i.sibling;return 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42 * @license lucide-react v0.487.0 - ISC
43 *
44 * This source code is licensed under the ISC license.
45 * See the LICENSE file in the root directory of this source tree.
46 */const Jb=n=>n.replace(/([a-z0-9])([A-Z])/g,"$1-$2").toLowerCase(),Zb=n=>n.replace(/^([A-Z])|[\s-_]+(\w)/g,(t,s,o)=>o?o.toUpperCase():s.toLowerCase()),sp=n=>{const t=Zb(n);return t.charAt(0).toUpperCase()+t.slice(1)},Eg=(...n)=>n.filter((t,s,o)=>!!t&&t.trim()!==""&&o.indexOf(t)===s).join(" ").trim();/**
47 * @license lucide-react v0.487.0 - ISC
48 *
49 * This source code is licensed under the ISC license.
50 * See the LICENSE file in the root directory of this source tree.
51 */var Xb={xmlns:"http://www.w3.org/2000/svg",width:24,height:24,viewBox:"0 0 24 24",fill:"none",stroke:"currentColor",strokeWidth:2,strokeLinecap:"round",strokeLinejoin:"round"};/**
52 * @license lucide-react v0.487.0 - ISC
53 *
54 * This source code is licensed under the ISC license.
55 * See the LICENSE file in the root directory of this source tree.
56 */const Yb=T.forwardRef(({color:n="currentColor",size:t=24,strokeWidth:s=2,absoluteStrokeWidth:o,className:d="",children:p,iconNode:c,...g},h)=>T.createElement("svg",{ref:h,...Xb,width:t,height:t,stroke:n,strokeWidth:o?Number(s)*24/Number(t):s,className:Eg("lucide",d),...g},[...c.map(([b,f])=>T.createElement(b,f)),...Array.isArray(p)?p:[p]]));/**
57 * @license lucide-react v0.487.0 - ISC
58 *
59 * This source code is licensed under the ISC license.
60 * See the LICENSE file in the root directory of this source tree.
61 */const Pe=(n,t)=>{const s=T.forwardRef(({className:o,...d},p)=>T.createElement(Yb,{ref:p,iconNode:t,className:Eg(`lucide-${Jb(sp(n))}`,`lucide-${n}`,o),...d}));return s.displayName=sp(n),s};/**
62 * @license lucide-react v0.487.0 - ISC
63 *
64 * This source code is licensed under the ISC license.
65 * See the LICENSE file in the root directory of this source tree.
66 */const ey=[["path",{d:"M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l-2.35 8.36A2 2 0 0 1 4.49 12H2",key:"169zse"}]],ay=Pe("activity",ey);/**
67 * @license lucide-react v0.487.0 - ISC
68 *
69 * This source code is licensed under the ISC license.
70 * See the LICENSE file in the root directory of this source tree.
71 */const ny=[["path",{d:"M5 12h14",key:"1ays0h"}],["path",{d:"m12 5 7 7-7 7",key:"xquz4c"}]],wr=Pe("arrow-right",ny);/**
72 * @license lucide-react v0.487.0 - ISC
73 *
74 * This source code is licensed under the ISC license.
75 * See the LICENSE file in the root directory of this source tree.
76 */const ty=[["path",{d:"M12 8V4H8",key:"hb8ula"}],["rect",{width:"16",height:"12",x:"4",y:"8",rx:"2",key:"enze0r"}],["path",{d:"M2 14h2",key:"vft8re"}],["path",{d:"M20 14h2",key:"4cs60a"}],["path",{d:"M15 13v2",key:"1xurst"}],["path",{d:"M9 13v2",key:"rq6x2g"}]],Rg=Pe("bot",ty);/**
77 * @license lucide-react v0.487.0 - ISC
78 *
79 * This source code is licensed under the ISC license.
80 * See the LICENSE file in the root directory of this source tree.
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82 * @license lucide-react v0.487.0 - ISC
83 *
84 * This source code is licensed under the ISC license.
85 * See the LICENSE file in the root directory of this source tree.
86 */const ry=[["path",{d:"M16 20V4a2 2 0 0 0-2-2h-4a2 2 0 0 0-2 2v16",key:"jecpp"}],["rect",{width:"20",height:"14",x:"2",y:"6",rx:"2",key:"i6l2r4"}]],oy=Pe("briefcase",ry);/**
87 * @license lucide-react v0.487.0 - ISC
88 *
89 * This source code is licensed under the ISC license.
90 * See the LICENSE file in the root directory of this source tree.
91 */const ly=[["path",{d:"M6 22V4a2 2 0 0 1 2-2h8a2 2 0 0 1 2 2v18Z",key:"1b4qmf"}],["path",{d:"M6 12H4a2 2 0 0 0-2 2v6a2 2 0 0 0 2 2h2",key:"i71pzd"}],["path",{d:"M18 9h2a2 2 0 0 1 2 2v9a2 2 0 0 1-2 2h-2",key:"10jefs"}],["path",{d:"M10 6h4",key:"1itunk"}],["path",{d:"M10 10h4",key:"tcdvrf"}

91],["path",{d:"M10 14h4",key:"kelpxr"}],["path",{d:"M10 18h4",key:"1ulq68"}]],xd=Pe("building-2",ly);/**
92 * @license lucide-react v0.487.0 - ISC
93 *
94 * This source code is licensed under the ISC license.
95 * See the LICENSE file in the root directory of this source tree.
96 */const dy=[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M18 17V9",key:"2bz60n"}],["path",{d:"M13 17V5",key:"1frdt8"}],["path",{d:"M8 17v-3",key:"17ska0"}]],uy=Pe("chart-column",dy);/**
97 * @license lucide-react v0.487.0 - ISC
98 *
99 * This source code is licensed under the ISC license.
100 * See the LICENSE file in the root directory of this source tree.
101 */const cy=[["path",{d:"m6 9 6 6 6-6",key:"qrunsl"}]],my=Pe("chevron-down",cy);/**
102 * @license lucide-react v0.487.0 - ISC
103 *
104 * This source code is licensed under the ISC license.
105 * See the LICENSE file in the root directory of this source tree.
106 */const py=[["path",{d:"m18 15-6-6-6 6",key:"153udz"}]],gy=Pe("chevron-up",py);/**
107 * @license lucide-react v0.487.0 - ISC
108 *
109 * This source code is licensed under the ISC license.
110 * See the LICENSE file in the root directory of this source tree.
111 */const hy=[["path",{d:"M17.5 19H9a7 7 0 1 1 6.71-9h1.79a4.5 4.5 0 1 1 0 9Z",key:"p7xjir"}]],fy=Pe("cloud",hy);/**
112 * @license lucide-react v0.487.0 - ISC
113 *
114 * This source code is licensed under the ISC license.
115 * See the LICENSE file in the root directory of this source tree.
116 */const ky=[["path",{d:"m18 16 4-4-4-4",key:"1inbqp"}],["path",{d:"m6 8-4 4 4 4",key:"15zrgr"}],["path",{d:"m14.5 4-5 16",key:"e7oirm"}]],Nt=Pe("code-xml",ky);/**
117 * @license lucide-react v0.487.0 - ISC
118 *
119 * This source code is licensed under the ISC license.
120 * See the LICENSE file in the root directory of this source tree.
121 */const by=[["ellipse",{cx:"12",cy:"5",rx:"9",ry:"3",key:"msslwz"}],["path",{d:"M3 5V19A9 3 0 0 0 21 19V5",key:"1wlel7"}],["path",{d:"M3 12A9 3 0 0 0 21 12",key:"mv7ke4"}]],tt=Pe("database",by);/**
122 * @license lucide-react v0.487.0 - ISC
123 *
124 * This source code is licensed under the ISC license.
125 * See the LICENSE file in the root directory of this source tree.
126 */const yy=[["path",{d:"M15 3h6v6",key:"1q9fwt"}],["path",{d:"M10 14 21 3",key:"gplh6r"}],["path",{d:"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6",key:"a6xqqp"}]],rp=Pe("external-link",yy);/**
127 * @license lucide-react v0.487.0 - ISC
128 *
129 * This source code is licensed under the ISC license.
130 * See the LICENSE file in the root directory of this source tree.
131 */const vy=[["path",{d:"M15 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7Z",key:"1rqfz7"}],["path",{d:"M14 2v4a2 2 0 0 0 2 2h4",key:"tnqrlb"}],["path",{d:"M10 9H8",key:"b1mrlr"}],["path",{d:"M16 13H8",key:"t4e002"}],["path",{d:"M16 17H8",key:"z1uh3a"}]],Ay=Pe("file-text",vy);/**
132 * @license lucide-react v0.487.0 - ISC
133 *
134 * This source code is licensed under the ISC license.
135 * See the LICENSE file in the root directory of this source tree.
136 */const xy=[["line",{x1:"6",x2:"6",y1:"3",y2:"15",key:"17qcm7"}],["circle",{cx:"18",cy:"6",r:"3",key:"1h7g24"}],["circle",{cx:"6",cy:"18",r:"3",key:"fqmcym"}],["path",{d:"M18 9a9 9 0 0 1-9 9",key:"n2h4wq"}]],Ly=Pe("git-branch",xy);/**
137 * @license lucide-react v0.487.0 - ISC
138 *
139 * This source code is licensed under the ISC license.
140 * See the LICENSE file in the root directory of this source tree.
141 */const Py=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"M12 2a14.5 14.5 0 0 0 0 20 14.5 14.5 0 0 0 0-20",key:"13o1zl"}],["path",{d:"M2 12h20",key:"9i4pu4"}]],Ld=Pe("globe",Py);/**
142 * @license lucide-react v0.487.0 - ISC
143 *
144 * This source code is licensed under the ISC license.
145 * See the LICENSE file in the root directory of this source tree.
146 */const wy=[["path",{d:"M3 14h3a2 2 0 0 1 2 2v3a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-7a9 9 0 0 1 18 0v7a2 2 0 0 1-2 2h-1a2 2 0 0 1-2-2v-3a2 2 0 0 1 2-2h3",key:"1xhozi"}]],Sy=Pe("headphones",wy);/**
147 * @license lucide-react v0.487.0 - ISC
148 *
149 * This source code is licensed under the ISC license.
150 * See the LICENSE file in the root directory of this source tree.
151 */const Iy=[["rect",{width:"18",height:"11",x:"3",y:"11",rx:"2",ry:"2",key:"1w4ew1"}],["path",{d:"M7 11V7a5 5 0 0 1 10 0v4",key:"fwvmzm"}]],Ng=Pe("lock",Iy);/**
152 * @license lucide-react v0.487.0 - ISC
153 *
154 * This source code is licensed under the ISC license.
155 * See the LICENSE file in the root directory of this source tree.
156 */const My=[["rect",{width:"20",height:"16",x:"2",y:"4",rx:"2",key:"18n3k1"}],["path",{d:"m22 7-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 7",key:"1ocrg3"}]],jy=Pe("mail",My);/**
157 * @license lucide-react v0.487.0 - ISC
158 *
159 * This source code is licensed under the ISC license.
160 * See the LICENSE file in the root directory of this source tree.
161 */const Cy=[["line",{x1:"4",x2:"20",y1:"12",y2:"12",key:"1e0a9i"}],["line",{x1:"4",x2:"20",y1:"6",y2:"6",key:"1owob3"}],["line",{x1:"4",x2:"20",y1:"18",y2:"18",key:"yk5zj1"}]],Dy=Pe("menu",Cy);/**
162 * @license lucide-react v0.487.0 - ISC
163 *
164 * This source code is licensed under the ISC license.
165 * See the LICENSE file in the root directory of this source tree.
166 */const zy=[["path",{d:"M15 18h-5",key:"95g1m2"}],["path",{d:"M18 14h-8",key:"sponae"}],["path",{d:"M4 22h16a2 2 0 0 0 2-2V4a2 2 0 0 0-2-2H8a2 2 0 0 0-2 2v16a2 2 0 0 1-4 0v-9a2 2 0 0 1 2-2h2",key:"39pd36"}

166],["rect",{width:"8",height:"4",x:"10",y:"6",rx:"1",key:"aywv1n"}]],Ty=Pe("newspaper",zy);/**
167 * @license lucide-react v0.487.0 - ISC
168 *
169 * This source code is licensed under the ISC license.
170 * See the LICENSE file in the root directory of this source tree.
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177 * @license lucide-react v0.487.0 - ISC
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207 * @license lucide-react v0.487.0 - ISC
208 *
209 * This source code is licensed under the ISC license.
210 * See the LICENSE file in the root directory of this source tree.
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211,contact:{title:"組織情報",company:"telet.ai labo",ceo:"代表：奥泉 慶太",button:"お問い合わせ"},studios:{tokyo:{title:"東京拠点",address:"〒184-0014 東京都小金井市貫井南町3丁目3番21号 ファーストヒルズA武蔵小金井301号室"}}},lv={title:"Portfolio",subtitle:"AI技術を活用した実績をご覧ください",techStackLabel:"技術スタック",viewProject:"プロジェクトを見る",privateProject:"非公開プロジェクト",visualizationLabel:"データビジュアライゼーション",cta:{title:"お問い合わせ",description:"プロジェクトのご相談、技術的なお問い合わせはお気軽にご連絡ください。",button:"お問い合わせ"},projects:{"traffic-route-ai":{title:"交通経路AI選択システム",subtitle:"マレーシアの公共交通をつなぐ、経路選択サポート",description:"出発地と目的地からバス・鉄道を組み合わせた移動経路を検索し、到着時刻・所要時間・乗換回数で候補を比較できる交通経路選択システム。バスのみ・鉄道のみの絞り込みや近くの駅・停留所の検索、多言語表示に対応し、日常の移動から旅行まで目的に合ったルート選びを支援します。",techStack:["公共交通経路検索","バス・鉄道連携","経路比較","多言語UI"]},"multilingual-assistant":{title:"AI多言語アシスタント",description:"複数言語での問い合わせに対応するインテリジェントなチャットアシスタント。1,573件のベクトルインデックスを構築し、条件分岐、NGワードフィルタリング、キーワードトリガーによる精密な回答制御を実装しました。日本語・英語・中国語など多言語でのユーザー質問に対し、言語横断的なセマンティック検索により一貫した高精度な回答を生成します。",techStack:["RAG","Vector Search","多言語Embedding","LLM","条件分岐ロジック"]},"weather-forecast":{title:"気象APIによるAI気象予測",description:"過去の気象データを学習し、直近の気象状況をリアルタイム予測するAIシステムを開発。気象庁API、OpenWeatherMap等から取得した過去データをLLMで解析し、温度、降水確率、風速などの短期予測を生成します。時系列データのパターン認識とRAGによる過去類似パターン検索を組み合わせ、従来の統計モデルを補完する高精度な予測を実現しました。",techStack:["気象API連携","LLM","RAG","時系列データ解析","リアルタイム処理"]},"local-llm":{title:"社内サーバー／オンプレ ローカルLLM",description:"お客様の業務に合わせたAIを、社内ですぐ使える状態でお届けします。Open WebUIを共通の入り口に据え、Ollama／MLXによる高速推論レーンと、UIDごとに完全に分離されたデータ領域を構築。社内ドキュメントは英語埋め込み × 日本語クエリ自動翻訳のRAGに取り込み、ニュース要約・資料整理・ルーチン業務などの業務スキルを安全に並走させます。スキル実行系はOpenClawコンテナとして隔離し、Cloudflareエッジ＋ローカルNginxの三層で守る設計。モデル選定からスキル設計、運用ポリシーまで一括で支援し、御社専用のAIを「導入したその日から使える」形で立ち上げます。",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"惑星間軌道到達予測（AI）",description:"NASA APIから惑星位置データ、軌道要素、重力パラメータを取得し、惑星間移動の最適な出発ポイントを解析するシステムを開発。ホーマン遷移軌道、重力アシストを考慮した軌道計算をAPIとPythonで算定し、打ち上げウィンドウ、所要時間、燃料効率を表示します。教育機関・宇宙関連企業向けのシミュレーションツールとして活用されています。※AI機能を現在外しています、APIとPythonで計算中",techStack:["NASA API (Horizons, SPICE)","軌道力学計算","Python","データビジュアライゼーション"]},"real-estate":{title:"不動産データAI解析",description:"特定å

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211‚ファイアウォール、IDS/IPS、認証ログ、アプリケーションログなど多様なログソースをPythonモジュールで収集・解析し、RAGに蓄積された過去の攻撃パターン、脆弱性情報、インシデント事例と照合します。異常パターンを検知した際はLLMが脅威レベル、影響範囲、推奨対応を自然言語で解説し、担当者へ即時アラート通知。同一パターンの再発時は自動的に高優先度アラートを発報し、インシデント対応の迅速化を実現しました。",techStack:["Python監視モジュール","ログ収集 (Fluentd, Logstash)","RAG","脅威パターンDB","LLM解析","アラート通知 (Slack, PagerDuty)"]}}},dv={learnMore:"詳しく見る",getStarted:"始める",contactUs:"お問い合わせ",backToHome:"ホームへ戻る"},uv={title:"Privacy Policy",subtitle:"プライバシーポリシー",lastUpdated:"最終更新日：2026å¹´1月23日",intro:"telet.ai（以下「当サービス」）は、お客様の個人情報の保護を重要な責務と考え、以下のとおりプライバシーポリシーを定めます。",sections:{collection:{title:"1. 収集する情報",description:"当サービスでは、お問い合わせやサービスのご利用にあたり、以下の情報を収集することがあります。",items:["メールアドレス","お名前（任意）","お問い合わせ内容","その他、お客様が自発的に提供された情報"]},purpose:{title:"2. 情報の利用目的",description:"収集した個人情報は、以下の目的でのみ利用いたします。",items:["お問い合わせへの回答・対応","サービスに関するご連絡・ご案内","プロジェクト進行に必要なコミュニケーション","サービス改善のための分析（個人を特定しない形式で）"]},thirdParty:{title:"3. 第三者への提供",description:"当サービスは、以下の場合を除き、お客様の個人情報を第三者に提供することはありません。",items:["お客様の同意がある場合","法令に基づく開示要請があった場合","人の生命、身体または財産の保護のために必要な場合"]},security:{title:"4. 情報の管理",description:"当サービスは、収集した個人情報を適切に管理し、不正アクセス、紛失、漏洩、改ざんを防止するため、合理的な安全対策を講じます。"},email:{title:"5. メールの取り扱いについて",description:"当サービスでは、以下の方針に基づきメールの取り扱いを行います。",items:["お客様からいただいたメールの内容は、お問い合わせ対応およびサービス提供の目的のみに使用いたします","メールアドレスを第三者に販売、貸与、または共有することはありません","サービスに関するご案内メールをお送りする場合があります。配信停止をご希望の場合は、メール内の配信停止リンクまたは下記連絡先よりご連絡ください","メール通信にはSSL/TLS暗号化を使用し、通信の安全性を確保しています","お問い合わせいただいたメールは、適切な期間保管した後、安全に削除いたします"]},cookies:{title:"6. Cookieの使用",description:"当サービスのウェブサイトでは、利便性向上のためCookieを使用する場合があります。Cookieはブラウザの設定により無効化することが可能です。"},external:{title:"7. 外部サービスの利用",description:"当サービスでは、以下の外部サービスを利用する場合があります。各サービスのプライバシーポリシーについては、それぞれのリンク先をご確認ください。",items:["Google Analytics（アクセス解析）","その他、業務上必要なクラウドサービス"]},changes:{title:"8. プライバシーポリシーの変更",description:"本ポリシーは、法令の改正やサービス内容の変更に伴い、予告なく改定することがあります。変更後のポリシーは、当ページに掲載した時点で効力を生じます。"},contact:{title:"9. お問い合わせ",description:"本ポリシーに関するお問い合わせは、下記までã

211”連絡ください。",company:"telet.ai",contactLink:"お問い合わせフォームへ"}}},cv={title:"利用規約",subtitle:"本サイトのご利用、および telet.ai への相談予約・お問い合わせ・資料請求に関する規約です。",lastUpdated:"最終更新日：2026å¹´1月22日",intro:"本利用規約（以下「本規約」といいます。）は、telet.ai（以下「当方」といいます。）が運営するウェブサイト（以下「本サイト」といいます。）の利用条件を定めるものです。本サイトは主に、相談予約・お問い合わせ・資料請求のためのランディングページとして提供されており、本サイト上でのアカウント作成や有料課金機能は提供していません。",sections:{scope:{title:"1. 適用範囲",description:"本規約は、本サイトの閲覧者および利用者（以下「利用者」といいます。）に適用されます。当方と利用者との間で、別途NDA、業務委託契約、個別契約書等が締結される場合は、当該書面が優先して適用されます。"},prohibited:{title:"2. 禁止事項",description:"利用者は、本サイトの利用にあたり、以下の行為をしてはなりません。",items:["法令または公序良俗に違反する行為","当方または第三者の知的財産権、プライバシー、その他の権利・利益を侵害する行為","不正アクセス、過度な負荷、妨害行為等により本サイトまたは関連システムの運用を阻害する行為","問い合わせフォーム等で虚偽の情報を送信する行為、または第三者になりすます行為","当方の事前許可なく、スクレイピング、リバースエンジニアリング、過度な自動アクセスを行う行為"]},disclaimer:{title:"3. 免責事項",description:"当方は、本サイトを現状有姿で提供します。当方は、法令で許される範囲で、本サイトに関して一切の保証を行いません。",items:["本サイト上の情報は予告なく変更または削除されることがあります","当方は、本サイト上の情報の正確性・完全性・特定目的への適合性を保証しません","利用者が本サイトを利用したこと、または本サイト上の情報に依拠したことにより生じた損害について、当方は責任を負いません","相談・お問い合わせに対する回答は一般的な情報提供であり、法務・税務・医療等の専門的助言を構成するものではありません"]},ip:{title:"4. 知的財産権",description:"本サイト上の文章、画像、デザイン、商標、ロゴ等に関する権利は、当方または正当な権利者に帰属します。権利者の許諾なく利用することはできません。"},links:{title:"5. 外部サイトへのリンク",description:"本サイトには第三者のウェブサイトへのリンクが含まれる場合があります。リンク先の内容や運用について、当方は一切責任を負いません。"},changes:{title:"6. 本規約の変更",description:"当方は、必要に応じて本規約を変更することがあります。変更後の本規約は、本サイト上に掲示した時点で効力を生じます。"},governingLaw:{title:"7. 準拠法・管轄",description:"本規約は日本法を準拠法とします。本サイトに関連して紛争が生じた場合、東京地方裁判所を第一審の専属的合意管轄裁判所とします。"},contact:{title:"8. お問い合わせ",description:"本規約に関するお問い合わせは、以下までご連絡ください。",company:"telet.ai",contactLink:"お問い合わせフォームへ"}}},mv={title:"規則運動",subtitle:"をAIで予測する。",description1:"気象情報は各国がデータをリアルタイムに提供しています。地図の真ん中にあなたの国が表示されています。昨日の天気、と今の天気は実績です、雨雲はその日時にあなたの空にあったはずです。",description2:"しかし、このアニメーションは未来の気象運動もアニメーション化しています。",description3:"過去データをベクトル化、パターンを高速で検索。周辺環境から気象パターンを予想することも可能です。同じように、収穫、生産、販売を予測すること環境と高度な統計学的な数値を過去の実績から、要素（i)を算定して、AIに解析させることも可能です。",refresh:"リフレッシュ",loading:"気象マップを読み込み中..."},pv={hero:{badges:["Enterprise","Local LLM","Custom Agent"],title:"企業向け AI Agent 設計・開発",description:"業務要件に合わせた専用 AI Agent を設計・構築"},mission:{title:"カスタムAI Agent開発サービス",description:"貴社固有の業務課題に対応するカスタムAI Agentを、設計から実装、運用まで一貫して開発いたします。既存の基幹システムやSaaS環境との親和性を重視したアーキテクチャ設計により、API連携、データベース接続、社内ツール統合をスムーズに実現します。要件定義、概念実証、本番デプロイまで段

211階的に進行し、エンタープライズ水準のセキュリティとコンプライアンス対応を担保した堅牢なAgent基盤を構築いたします。LangChain、AutoGen、CrewAIをはじめとする主要フレームワークに精通したエンジニアが、貴社の技術スタックや運用体制に最適なソリューションをご提案。単発のチャットボット開発ではなく、複数Agentが協調して業務を遂行するマルチエージェントシステムや、外部ツールと連携するAgentic Workflowの構築にも対応いたします。導入後の継続的な改善、モデルアップデートへの追従、運用監視体制の整備まで含めた長期的なパートナーシップを通じて、AI Agentがもたらすビジネス価値の最大化を支援いたします。",imageAlt:"カスタムAI Agent開発サービス"},summary:{title:"Agent開発動向",paragraphs:["2025年から2026年にかけて、AI Agent開発は実証実験フェーズから本格的な企業導入フェーズへと移行しています。「Agentic AI」という概念が定着し、単なるチャットボットを超えた自律型業務遂行システムが各業界で稼働を始めました。","OpenAIのOperator、AnthropicのComputer Use、GoogleのProject Marinerなど、ブラウザやデスクトップを直接操作する「Computer-Using Agent」が実用化。定型業務の完全自動化が現実のものとなっています。MCP（Model Context Protocol）はエコシステムとして急成長し、数千のツール連携がプラグアンドプレイで実現可能になりました。","マルチエージェントアーキテクチャも成熟期を迎え、専門特化したAgentが協調してプロジェクト管理、カスタマーサポート、データ分析といった複合業務を遂行します。A2A（Agent-to-Agent）プロトコルの標準化により、異なるベンダー間でのAgent連携も進んでいます。","企業導入では「Human-in-the-Loop」から「Human-on-the-Loop」へとガバナンスモデルが進化。Agentの判断を事後監査する仕組みや、リスクレベルに応じた自動エスカレーション機能が標準装備となり、金融・医療・製造など規制業界での採用が加速しています。"]},features:{title:"設計・開発",subtitle:"Design & Development",items:[{title:"業務要件ベースのAgent設計",description:"業務フロー分析からユースケースを抽出し、タスク分解とAgent責務の明確化を実施。シングルAgentで完結する処理か、マルチAgent協調が必要かを判断し、ReAct、Plan-and-Execute、Reflexionなど最適な推論パターンを選定します。"},{title:"RAG / Tool / Workflow 統合",description:"Embedding生成、チャンク戦略、Hybrid Searchを組み合わせた高精度RAGパイプラインを構築。MCP準拠のTool定義により外部API・データベースとの接続を標準化し、LangGraphやCrewAIによるマルチステップWorkflowをオーケストレーションします。"},{title:"セキュリティ・権限設計",description:"RBAC/ABACによるAgent機能のアクセス制御、Prompt Injection対策、PII検出・マスキングを実装。全入出力の監査ログを保持し、SOC2・ISO27001準拠環境での運用に対å¿

211œã€‚Guardrailsによる出力フィルタリングも標準装備します。"},{title:"運用・監視まで含めた実装",description:"LangSmith、Langfuse等によるTrace可視化、レイテンシ・トークン消費・エラー率のリアルタイム監視を実装。異常検知アラート、A/Bテスト基盤、モデル更新時のRegression Testを含む継続的改善サイクルを構築します。"}]},techStack:{title:"技術スタック",subtitle:"Platform & Technology",categories:{agentFramework:{title:"Agent Framework",items:["AutoGen 0.4 / AG2","CrewAI","LangGraph","OpenAI Agents SDK","Anthropic MCP","Google ADK (Agent Development Kit)","Semantic Kernel","独自Agent Platform"]},apiLayer:{title:"API Layer",items:["FastAPI / Litestar","gRPC / Connect","GraphQL Federation","Server-Sent Events (SSE)","WebSocket / NATS","OAuth 2.1 / OIDC","API Gateway (Kong / Envoy)","Adaptive Rate Limiting"]},vectorDb:{title:"Vector DB / RAG",items:["Pinecone","Weaviate","Qdrant","Milvus","ChromaDB","pgvector","FAISS","Turbopuffer"]}

211,infrastructure:{title:"Infrastructure",items:["Kubernetes (EKS / GKE / AKS)","Docker / Podman","On-Premise GPU Cluster","Private Cloud","Serverless (Lambda / Cloud Run)","OpenTelemetry / Langfuse","Terraform / Pulumi","ArgoCD / Flux"]}},updateNote:{title:"2026年の主なアップデートポイント",description:"Agent Frameworkでは、OpenAI Agents SDK、Anthropic MCP、Google ADKなど主要ベンダーの公式SDK/プロトコルが台頭。API LayerではgRPCやSSEがAgent間通信の標準となり、OAuth 2.1への移行が進行中。Vector DBではQdrantとTurbopufferが高速検索で注目を集めています。"}},cta:{title:"お問い合わせ",description:`要件整理からご相談いただけます。
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212±ãƒ¼ã‚¹ãŒå¢—えています。ハイブリッド構成（機密処理はローカル、汎用処理はクラウド API）も一般的な選択肢となりました。"],icons:[{label:`Private
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216,infrastructure:{title:"Infrastructure",items:["Docker / Kubernetes","NVIDIA Triton Server","API Gateway（Kong / Envoy）","Prometheus / Grafana","Private Cloud（VMware / OpenStack）","Air-Gapped 環境対応"]}},updateNote:{title:"2026年の主なアップデートポイント",description:"オープンウェイトモデルでは Llama 3.3 が GPT-4 クラスの汎用性能を達成し、日本語性能も大幅向上。Qwen 2.5 シリーズは多様なサイズ展開とバランスの良い性能で選択肢が拡大。DeepSeek V3/R1 は推論コストパフォーマンスで注目を集めています。推論エンジンでは vLLM 2.0 の Chunked Prefill、SGLang の RadixAttention がマルチターン対話を高速化。llama.cpp の Apple Silicon 最適化が進み、M4 Max での 70B モデル推論が実用的に。量子化技術では 4bit 精度と推論速度の両立が進み、GGUF が事実上の標準フォーマットとして定着しました。"}},cta:{title:"お問い合わせ",description:`要件整理からご相談いただけます。
217PoC・ローカル LLM 構成の検討も可能です。`,primaryButton:"お問い合わせ",secondaryButton:"技術相談"}},yv={hero:{badges:["Enterprise","RAG","検索拡張生成"],title:"Vector / RAG 開発",description:"社内ナレッジを LLM と連携させる RAG システム構築"},mission:{title:"Vector / RAG 開発サービス概要",paragraphs:["社内に蓄積されたナレッジ資産を LLM と連携させ、従業員が自然言語で検索・質問できる RAG（検索拡張生成）システムを構築いたします。マニュアル、議事録、技術文書、FAQ、Slack 履歴、Confluence、SharePoint など多様なデータソースを統合し、組織知を即座に活用できる環境を実現します。","単純な文書検索に留まらず、Hybrid Search（ベクトル検索＋キーワード検索）、Reranking、階層的チャンキング、メタデータフィルタリング等の最新技術を適用し、検索精度と回答品質を最大化します。回答には出典を明示し、ハルシネーション抑制と信頼性確保を両立します。","AWS、GCP、Azure、オンプレミスなど多様な環境に対応し、既存の認証基盤・権限管理と統合。データの鮮度維持（差分更新・自動再インデックス）、利用ログ分析、継続的な精度改善まで一貫してサポートいたします。"]},summary:{title:"RAG 技術の動向",paragraphs:["2025年から2026年にかけて、RAG 技術は「基本実装から本番品質へ」のフェーズに移行しています。単純なベクトル検索だけでなく、複数の検索手法を組み合わせた Hybrid Search、検索結果の並び替えを行う Reranking、複雑な質問を分解して処理するクエリ分解が標準的な構成となりました。","チャンキング戦略も進化し、文書構造を考慮した階層的チャンキング、重複を持たせた Overlapping Chunks、テーブルや図表を個別処理する Multimodal Chunking が精度向上に寄与しています。Contextual Retrieval（チャンクにコンテキスト情報を付与）の有効性も広く認知されました。","ベクトルDB 選定においては、Pinecone、Weaviate、Qdrant、Milvus 等のマネージドサービスに加え、PostgreSQL pgvector やローカル FAISS など、要件に応じた選択肢が充実。Embedding モデルも text-embedding-3-large、Cohere Embed v3、多言語対応モデルなど選択肢が拡大しています。","エンタープライズ導入においては、権限管理（ACL）連携、PII マスキング、監査ログ、マルチテナント設計が必須要件となり、「動く PoC」から「運用可能な本番システム」への移行が求められています。"],icons:[{label:`Hybrid
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219RAG`},{label:`Agentic
220RAG`},{label:`Enterprise
221Security`}]},features:{title:"設計・開発の特徴",subtitle:"Design & Development",items:[{title:"データ収集・前処理パイプライン",description:"PDF、Word、Excel、HTML、Markdown、Slack、Confluence、SharePoint 等からのデータ抽出・クレンジング。OCR、テーブル構造解析、画像キャプション生成を含むマルチモーダル対応。差分更新・スケジュール実行による鮮度維持を実現します。"},{title:"高精度検索システム設計",description:"ベクトル検索＋BM25 の Hybrid Search、Cohere Rerank / Cross-Encoder による Reranking、クエリ拡張・分解を組み合わせた高精度検索を構築。メタデータフィルタリング、権限ベースのアクセス制御も実装します。"},{title:"回答生成・品質管理",description:"コンテキスト最適化、Few-shot プロンプト、Chain-of-Thought 推論により回答品質を向上。出典明示、信頼度スコア表示、ハルシネーション検知を実装し、業務利用に耐える信頼性を確保します。"},{title:"運用・改善サイクル",description:"利用ログ分析、検索ヒット率・回答満足度のモニタリング、フィードバックループによる継続的改善。A/B テスト基盤、Embedding モデル更新、チャンキング戦略見直しを含む運用支援を提供します。"}]},techStack:{title:"技術スタック",subtitle:"Platform & Technology",categories:{embeddingModels:{title:"Embedding Models",items:["OpenAI text-embedding-3-large","Cohere Embed v3","Voyage AI","Jina Embeddings v3","BGE-M3（多言語）","multilingual-e5-large","Japanese-specific models"]},vectorDatabases:{title:"Vector Databases",items:["Pinecone（マネージド）","Weaviate（Hybrid Search）","Qdrant（高性能）","Milvus（大規模）","Chroma（軽量）","PostgreSQL pgvector","FAISS（ローカル）"]},chunking:{title:"Chunking & Processing",items:["階層的チャンキング","Semantic Chunking","Overlapping Chunks","Sentence Window","Contextual Retrieval","テーブル・図表処理","マルチモーダル対応"]},search:{title:"Search & Retrieval",items:["Hybrid Search (Vector + BM25)","Cohere Rerank","Cross-Encoder Reranking","クエリ拡張・分解","HyDE (仮説文書生成)","Self-Query Retrieval","Multi-Query Retrieval"]}},updateNote:{title:"2026年の主なアップデートポイント",description:"Embedding モデルでは text-embedding-3-large と Cohere Embed v3 がエンタープライズ標準となり、日本語性能も大幅向上。Contextual Retrieval の有効性が実証され、チャンクへのコンテキスト付与が標準手法に。検索では Hybrid Search + Reranking が必須構成となり、単純なベクトル検索のみの構成は減少。Agentic RAG（エージェントが検索戦略を動的に選択）の

221実用化も進んでいます。運用面では、RAG 評価フレームワーク（RAGAS、LangSmith）による品質計測と改善サイクルの確立が重視されています。"}},cta:{title:"お問い合わせ",description:`要件整理からご相談いただけます。
222PoC・ローカル LLM 構成の検討も可能です。`,primaryButton:"お問い合わせ",secondaryButton:"技術相談"}},vv={hero:{badges:["Full Stack","LLM Integration","API Design"],title:"AIアプリケーション開発",description:"LLMを活用した実用的なアプリケーションを設計・構築"},mission:{title:"AIアプリケーション開発サービス概要",paragraphs:["外部APIから取得したデータをLLMで解析し、ビジネス価値を創出するAIアプリケーションの設計・開発を提供いたします。CRM、ERP、SaaS、IoTセンサー、ソーシャルメディアなど多様なデータソースとAPI連携し、取得したデータをリアルタイムでLLMに投入。自然言語による分析レポート生成、異常検知、トレンド予測、自動分類など、従来のBIツールでは実現できなかった高度なデータ解析を可能にします。","単純なAPI連携に留まらず、複数データソースの統合、データクレンジング、構造化処理を経てLLMが解釈しやすい形式に変換。プロンプトエンジニアリングにより解析精度を最大化し、業務要件に特化したアウトプットを生成します。営業データから商談成約予測、カスタマーサポートログからVoC分析、センサーデータから設備保全レコメンドなど、データとAIを組み合わせた実用的なアプリケーションを構築いたします。","Webアプリケーション、モバイルアプリ、Slack・Teams連携、定期バッチ処理など多様なインターフェースに対応。要件定義からAPI設計、LLM連携実装、本番運用まで一貫してサポートいたします。"]},summary:{title:"AIアプリケーション開発の動向",paragraphs:["2025年から2026年にかけて、AIアプリケーション開発は「API連携×LLM解析」を軸とした実用フェーズに突入しています。従来のBIツールやダッシュボードでは対応できなかった非構造化データの解析、自然言語によるインサイト生成が企業システムに組み込まれ始めました。","特に注目すべきは、LLMのStructured Output機能の成熟です。JSON Schema準拠の厳密な出力制御が可能となり、API経由で取得したデータをLLMで解析し、その結果を後続システムにそのまま連携するエンドツーエンドの自動化パイプラインが現実のものとなりました。Function Callingの多段実行により、複数のAPIを跨いだデータ収集・統合・解析を単一のワークフローで処理できるようになっています。","データ処理基盤ではPolarsが高速処理の標準として台頭し、Streaming処理との組み合わせでリアルタイム解析が容易に。MCPの普及により、LLMが外部APIを自律的に呼び出してデータ取得・解析を行うAgenticなアプリケーション構築も加速しています。","企業導入においては、単発のPoC開発から本番運用を見据えたスケーラブルな設計へとニーズがシフトし、コスト最適化、エラーハンドリング、監査ログ対応を含めた包括的なアーキテクチャ設計が求められています。"],icons:[{label:`Agentic
223AI`},{label:`MCP
224Protocol`},{label:`Multi
225Agent`},{label:`Human-on
226-the-Loop`}]},features:{title:"設計・開発の特徴",subtitle:"Design & Development",items:[{title:"API連携・データ収集設計",description:"REST API、GraphQL、WebSocket、Webhookなど多様なプロトコルに対応したデータ収集基盤を構築。認証処理（OAuth、APIキー、JWT）、レート制限対応、リトライ機構を実装し、安定したデータ取得パイプラインを設計します。複数APIからのデータ統合、スケジューリング、差分取得にも対応いたします。"},{title:"データ前処理・構造化",description:"取得した生データをLLMが効率的に解析できる形式に変換。JSONパース、データクレンジング、正規化、欠損値処理を実施し、コンテキストウィンドウ制限を考慮した最適なデータ分割・要約をè

226¡Œã„ます。メタデータ付与により、解析結果のトレーサビリティも確保します。"},{title:"LLM解析・プロンプト設計",description:"業務要件に特化したプロンプトテンプレートを設計し、データ解析精度を最大化。分類、要約、感情分析、異常検知、予測生成など目的に応じた解析パイプラインを構築します。Function Calling、Structured Outputを活用し、後続システムと連携しやすい形式でアウトプットを生成します。"},{title:"アプリケーション実装・運用",description:"FastAPI、Next.js等によるWebアプリケーション、Slack・Teams Bot、定期実行バッチなど多様なインターフェースで解析結果を提供。ダッシュボード、通知アラート、レポート自動生成を実装し、ビジネスユーザーが即座に活用できる形で納品いたします。運用監視、コスト最適化、継続改善まで対応します。"}]},techStack:{title:"技術スタック",subtitle:"Platform & Technology",categories:{apiIntegration:{title:"API Integration",items:["REST / GraphQL / WebSocket","OAuth 2.1 / APIキー / JWT","Webhook / Polling","Apache Airflow / Prefect","Celery / Temporal","requests / httpx / aiohttp","Rate Limiting / Retry","Data Validation (Pydantic)"]},dataProcessing:{title:"Data Processing",items:["Pandas / Polars","JSON / JSONL Parser","Data Cleansing Pipeline","Apache Spark (大規模処理)","dbt (データ変換)","Great Expectations","Delta Lake / Iceberg","Streaming (Kafka / Flink)"]},llmCloud:{title:"Cloud LLM",items:["OpenAI GPT-4o / GPT-4.1","Claude 3.5 / Claude 4","Gemini 2.0 / Gemini 2.5","Command R+ (Cohere)","Mistral Large"]},llmLocal:{title:"Local LLM",items:["Llama 3.3 / Llama 4","Mistral / Mixtral","Qwen 2.5 / Qwen 3","DeepSeek V3 / R1","Gemma 2 / Gemma 3","Phi-4","ELYZA / Swallow（日本語特化）","Command R（Cohere）"]},inference:{title:"Inference / Framework",items:["vLLM / SGLang","Ollama / llama.cpp","LiteLLM Proxy","Function Calling","Structured Output","Prompt Template Engine","LangChain / LlamaIndex","独自解析パイプライン"]}

226,application:{title:"Application Layer",items:["FastAPI / Litestar","Next.js / React","Slack SDK / Teams SDK","Streamlit / Gradio","Scheduled Jobs (Cron / Cloud Scheduler)","PostgreSQL / MongoDB","Redis (Cache / Queue)","Docker / Kubernetes"]}},updateNote:{title:"2026年の主なアップデートポイント",description:"LLM解析領域では、GPT-4.1やClaude 4のStructured Output機能が成熟し、JSON Schema準拠の厳密な出力制御が標準化。Function Callingの多段実行により、複数APIを跨いだ複合的なデータ収集・解析が1つのパイプラインで実現可能になりました。データ処理ではPolarsがPandasに代わる高速処理の選択肢として定着し、Streaming処理との組み合わせでリアルタイム解析基盤の構築が容易に。アプリケーション層ではMCPによる外部ツール連携が一般化し、LLMがAPIを自律的に呼び出してデータ取得・解析を行うAgenticなアプリケーション開発が加速しています。"}},cta:{title:"お問い合わせ",description:`要件整理からご相談いただけます。
227PoC・ローカル LLM 構成の検討も可能です。`,primaryButton:"お問い合わせ",secondaryButton:"技術相談"}},Av={seo:Jy,header:Zy,hero:Xy,mission:Yy,businessServices:ev,features:av,businessProcess:nv,story:tv,contact:iv,footer:sv,cookiePolicy:rv,about:ov,portfolio:lv,common:dv,privacyPolicy:uv,termsOfService:cv,weatherMap:mv,agentDevelopment:pv,faq:gv,pricing:hv,aiTranscription:fv,keywordTracking:kv,onPremiseLLM:bv,vectorRAG:yv,aiAppDevelopment:vv},xv={home:{title:"telet.ai - AI-Powered Solutions Platform",description:"Transform your customer conversations with AI-powered call analytics. Get real-time insights, sentiment analysis, and performance tracking to improve your team's results."},about:{title:"Organization Information - telet.ai",description:"Learn about telet.ai's mission to transform customer conversations with AI. Meet our team and explore our journey."},privacy:{title:"Privacy Policy - telet.ai",description:"Review telet.ai's privacy policy to understand how we handle your data and protect your privacy."},terms:{title:"Terms of Service - telet.ai",description:"Read telet.ai's terms of service for using this website and contacting us for consultations."},cookies:{title:"Cookie Policy - telet.ai",description:"Learn how telet.ai uses cookies and similar technologies (Cloudflare and Google Analytics)."},portfolio:{title:"Portfolio - telet.ai",description:"Explore our portfolio of AI solutions designed to enhance your business operations and customer experiences."},agentDevelopment:{title:"Enterprise AI Agent Design & Development - telet.ai",description:"Design and build production-grade AI agents tailored to your workflows, tools, and compliance needs."},onPremiseLLM:{title:"On-Premise LLM Operations - telet.ai",description:"Operate modern LLMs inside your environment for strong data control, predictable costs, and low-latency inference."},vectorRAG:{title:"Vector RAG & Index Design - telet.ai",description:"Design retrieval pipelines and indices tailored to each workflow to improve grounded answers."},aiAppDevelopment:{title:"AI Application Development - telet.ai",description:"Build practical applications powered by LLMs—integrated with your APIs, data, and business workflows."}},Lv={brand:"telet.ai",nav:{services:"Services",portfolio:"Portfolio",about:"Organization Information",contact:"Contact"}},Pv={brandName:"telet.ai",tagline:"Multi AI-communications",badge:"AI-Powered Call Intelligence",title:{line1:"Local and defensive,",line2:"Expand in the cloud,",line3:"Automated by agent."},description:"Edge inference in SLM ensures data sovereignty, and cloud integration in LLM scales processing power to infinity; the adoption of the Agentic AI architecture is beginning to show results from leading companies."},wv={badge:"Our Mission",title:"Making LLM more accessible",paragraphs:['Can you really go this far?" --That surprise is what we are aiming for.','The potential of large-scale language modeling (LLM) is expanding every day. However, we want to break down barriers such as "I am worried about sending confidential data to external APIs," "Advanced AI applications are only for large companies," and "The cost of implementation is not worth it.',"We specialize in building local LLMs that are completed within your environment. Highly sensitive customer information can be safely utilized without being exposed to the outside world. We can maximize the benefits of AI while ensuring data security.","It handles everything from routine document creation and data organization to specialized analysis and decision support. Our proprietary agent te

227chnology provides accuracy comparable to API models.",'LLM does not replace people. It is a powerful tool that allows people to exercise more creativity, judgment, and expertise. We take the technology from special to commonplace - a true "consumerization" of the technology.','Please feel free to consult with us. We will work with you to find out "What can we do?" together with you.'],animation:{label:"LLM",sublabel:"Technology"}},Sv={title:"Services",subtitle:"Business Services",services:[{title:"AI Agent Design and Development for Enterprises",titleEn:"Enterprise AI Agent",description:"Design and develop AI agents optimized for business requirements, internal rules, and data scope. Support from external API type LLM to local LLM.",badges:["Individual Design","LLM compatible","business specialization"],link:"Details."},{title:"on-premise LLM server",titleEn:"On-Premise LLM Server",description:"Runs 7B to 120B scale LLM within the company's dedicated server. Provides a secure and flexible LLM usage environment, avoiding the risk of external data transmission.",badges:["7B-120B supported","API KEY issued","Safety Design"],link:"Details."},{title:"Vector RAG and index design",titleEn:"Vector RAG Architecture",description:"Separate and optimize RAG data by business content and analysis logic. Index design for fast and accurate searches.",badges:["Index Separation","High speed search","multilingual support"],link:"Details."},{title:"AI Application Development",titleEn:"AI Application Dev",description:"Develop practical AI applications with analysis and generation by LLM at its core, utilizing existing APIs and external data.",badges:["API Linkage","LLM Analysis","Integrated Design"],link:"Details."}]},Iv={title:"Achievement projects using AI technology",subtitle:"AI development experience in a wide range of fields, including multilingual assistants, weather forecasting, local LLM, and security monitoring.",viewDetails:"View details →.",items:[{title:"AI Multilingual Assistant",description:"Multilingual chat assistant by RAG + LLM. Highly accurate responses to inquiries in multiple languages including Japanese, English, and Chinese."},{title:"Weather Forecasting System",description:"Learns weather data and generates real-time forecasts. Highly accurate forecasts are generated by using time-series pattern recognition and RAG to search for similar patterns in the past."},{title:"In-house Server / On-Prem Local LLM",description:"We deliver AI tailored to your business operations, ready to use within your company, with Open WebUI as a common entry point, fast inference lanes with Ollama/MLX, and completely separated data domains for each UID. In-house documents are captured in a RAG with English embedding and automatic Japanese query translation, and business skills such as news summarization, document organization, and routine tasks are safely executed side by side. The skill execution system is isolated as an OpenClaw container and protected by three layers of Cloudflare edge and local Nginx. We provide comprehensive support for model selection, skill design, and operational policies, and start up your company's dedicated AI in a form that can be used from the day it is deployed."},{title:"Interplanetary Orbital Arrival Prediction (AI)",description:"Obtain planetary data from NASA API and interpret orbit calculations in LLM. Generates reports on launch window, time required, and fuel efficiency."},{title:"Real Estate Data AI Analysis",description:"Collect real estate information by web scraping and analyze with LLM + Python. Automatic generation of price trends, market forecasts, and return on investment."},{title:"Fortune-telling AI (Four Pillars Fortune-telling)",description:"Reproduce the Four Pillars of Destiny logic in Python and interpret it with LLM. Acquire coordinates and time from the Astronomy API for personalized appraisal of fortune and compatibility."},{title:"Google Workspace AI integration",description:"LLM integration with Gmail, Drive, Calendar, etc. AI assists with email summary, file search, and scheduling."},{title:"World NEWS Multilingualization",description:"Real-time collection of world news, translated and summarized in multiple languages by LLM. Natural translation and summary generation based on contextual understanding."}

227,{title:"Security Log Monitoring AI Manager",description:"Real-time monitoring of security logs, threat detection and analysis by LLM, and immediate alerts by matching with past attack patterns by RAG."}]},Mv={title:"How to proceed with the project",headline:"All you need is a vision.",paragraphs:['Gone are the days when language differences were a barrier to business, and although we use AI translation, we can handle business negotiations and development in 14 languages through our unique workflow that makes full use of image sharing. Even complex specifications can be checked with sample images to avoid miscommunication. The only thing that matters is "what you want to create.',"The project begins with an initial hearing in the business application. After a general sharing of the client's vision and sense of the issues, an NDA (Non-Disclosure Agreement) is signed. After creating an environment in which confidential information can be discussed in confidence, we conduct detailed interviews and confirm requirements. Based on the content, we prepare a proposal and confirm the direction of the project using sample images. With the understanding in place, we prepare specifications and start development.","This flow is only a general introduction. We will flexibly adapt to your situation and schedule. Wherever you are in the world, even if you don't speak the same language, images and AI will support your communication. Why don't you join us to enter the frontier market that no one has yet tapped into? Let telet.ai be your partner to give shape to your ideas."],cards:[{title:"Hearing / NDA",description:"After the initial hearing by e-mail, a nondisclosure agreement is signed. You can provide us with details with peace of mind."},{title:"Planning & CA",description:"Proposal and sample images are provided. 14 languages are supported, so you can proceed without language barriers."},{title:"Sampling & Start",description:"We will prepare specifications and start business upon agreement."},{title:"Development, testing and operation",description:"Speedy implementation through agile development. We provide consistent support from testing to operational support."}]},jv={badge:"Case Studies",title:"AI Introduction Story",items:[{title:"Business Efficiency and Cost Reduction Solutions",subtitle:"Reduce the daily workload and create an environment in which you can focus on your core business.",description:`Many small and medium-sized enterprises (SMEs) face labor shortages and hiring difficulties. Even when new personnel are hired, training costs are high and there are problems with retention rates. Our AI solution supports daily tasks such as automating budget management, data entry and organization, internal document retrieval, and automatic generation of reports and documents. AI assistants handle routine tasks that used to take up a lot of time, 24 hours a day, 365 days a year, without errors. Companies that have introduced AI have realized significant reductions in work hours. From "increasing human resources" to "collaborating with AI. We will do our best to support your company's work style reform.`},{title:"Customer Support Automation",subtitle:"24-hour service improves customer satisfaction and reduces labor costs.",description:"AI chatbots provide automated responses to frequently asked questions, primary response and sorting of inquiries, and real-time response in multiple languages. Real-time response in multiple languages. Human operators can focus on complex cases and improve the quality of customer service. Take your service quality to the next level with AI customer support."},{title:"Knowledge and Document Utilization AI",subtitle:"Internal knowledge assets in a form that can be immediately utilized by anyone",description:'Where is that manual?" There is information that only the predecessor knows" - impersonalized knowledge hinders the growth of a company. Our RAG (search extension generation) technology allows you to retrieve necessary information from internal manuals, FAQs, and past documents by simply asking questions in natural language. It streamlines the training of new employees, digitizes the tacit 

227knowledge of veterans, and enables knowledge sharing across departments. Existing documents can be used as they are, so there is no need to create new documents.'},{title:"Image analysis AI for medical and nursing care",subtitle:"Watchful eyes, 24-hour support by AI",description:"In the medical and nursing care fields, it is necessary to watch over a large number of patients and users with limited staff. Our image/video analysis AI detects abnormalities in real time from camera images. It automatically detects infectious diseases by detecting fever, alerts immediately if a patient falls or wanders, and detects changes in physical condition by analyzing facial expressions. By reducing the burden of nighttime patrols, responding quickly to emergencies, and automating recording tasks, staff can focus on their primary care duties. On-premise installation is also available to ensure privacy."}]},Cv={title:"Contact Us",description:"We can consult with you from the requirements planning stage, and can also consider PoC and local LLM configurations.",openForm:"Open Form",closeForm:"Close the form",form:{name:"Your Name",namePlaceholder:"Taro Yamada",email:"Email Address",emailPlaceholder:"[email protected]",subject:"subject",subjectPlaceholder:"Subject of your inquiry",message:"Message",messagePlaceholder:"Please fill in your inquiry",required:"*",submit:"Send",submitting:"Sending...",turnstileMessage:'The "I am not a robot" authorization is required for submission.',turnstileError:"Failed to load security authorization. Please reload.",errorRetry:"Please try again."}},Dv={brand:"telet.ai",tagline:"Multi AI-communications",description:"Innovate your business with AI agents. Create value from dialogue.",sections:{product:{title:"Product",features:"Feature",benefits:"introduction",testimonials:"Customer Testimonials"},services:{title:"Service",agentDev:"AI Agent Design and Development for Enterprises",onPremise:"on-premise LLM server",vectorRag:"Vector RAG and index design",aiApp:"AI Application Development"},company:{title:"Organization Information",about:"About telet.ai",profile:"Representative Profile",contact:"Contact Us"},process:{title:"How we do business",hearing:"Hearing / NDA",planning:"Planning & CA",sampling:"Sampling & Start",development:"Development, testing and operation"}},copyright:"© 2026 telet.ai. All rights reserved.",legal:{privacy:"Privacy Policy",terms:"Terms of Use",cookies:"Cookie Policy"}},zv={title:"Cookie Policy",subtitle:"This section describes the cookies and other technologies (Cloudflare / Google Analytics) used on this site.",lastUpdated:"Last update: January 22, 2026",intro:'telet.ai ("we", "us", "our") ("We") may use cookies and similar technologies to provide and improve this Site and to ensure its security. We do not set our own special tracking cookies for advertising purposes (although Cloudflare and Google Analytics may set cookies or similar technologies in accordance with their respective service specifications).',sections:{what:{title:"1. what is a cookie?",description:"Cookies are small text files that are stored in your browser when you visit a website."},howWeUse:{title:"Purpose of our use of cookies, etc.",description:"We may use cookies, etc. primarily for the following purposes",items:["To stabilize the display and operation of the site","To detect and prevent unauthorized access and malicious traffic","To understand the status of access and to help improve the site"]},googleAnalytics:{title:"3. use of Google Analytics",description:"We use Google Analytics 4 (GA4) provided by Google LLC for access analysis.",items:["Information collected may include pages viewed, time spent, referral source, device and browser information, etc.","We do not use directly personally identifiable information (name, email address, etc.) for the purpose of sending it to Google Analytics.","The treatment of data by Google is subject to Google's terms and policies"]},cloudflare:{title:"4. use of Cloudflare",description:"We use the services of Cloudflare, Inc. for delivery and security measures of this Site. Cloudflare may set and refer to cookies, etc. for purposes such as DDoS protection and bot prevention.",items:["The primary purpose is for security and communication optimization, and is not intended to be tracked for ad delivery","Cloudflare's treatment of data is subject to Cloudflare's terms and policies"]},noSpecialCookies:{title:"5. our own special cookies",description:"We do not set our own special cookies for the purpose of behavioral targeting advertising, etc. The use of cookies, etc. is mainly limited to the scope of site operation, security, and access analysis."},choices:{title:"6. cookie management (opt-out)",description:"If cookies are disabled, some functions may not be available.",items:["Cookies can be deleted or disabled in browser settings","If you wish to disable measurement by Google Analytics, please use the opt-out method provided by Google","Use of a browser's private mode, etc., can limit the information stored."]},changes:{title:"7. changes to this policy",description:"We may revise this policy in response to amendments to laws and regulations or changes in the services used. The revised policy will become effective when it is posted on this page."},contact:{title:"8. contact us",description:"If you have any questions regarding this policy, please contact us at",company:"telet.ai",contactLink:"Go to Inquiry Form"}}},Tv={hero:{title:"About telet.ai",subtitle:"Careful AI development support that can only be provided by a small, personalized team."},story:{title:"About us",content:"telet.ai began with a question. Why are so many companies failing to leverage the insights that lie in the countless interactions between companies and their customers? The team, headed by a representative with experience in AI research and enterprise softw

227are development, was established to tackle this issue head-on. We are not a large organization. That is why we are able to face your challenges in depth and propose only the solutions that are truly necessary. Our strength is our ability to design hybrid architectures that integrate local LLM and cloud LLM. We can protect your company's sensitive data in a dedicated cloud environment while performing advanced AI inference in a scalable manner. We provide a CLI for developers and a chat UI for frontline staff, creating a foundation for AI use throughout your organization. And we focus on being the optimal solution rather than being the most advanced. Discarding functions according to budget and reviewing just one configuration can make a big difference in cost. We deliver what you need, in the amount you need. Our attentive AI development support is only possible because we are small and personal. To solve manpower shortages and support your company's growth. That is the promise of telet.ai."},representative:{title:"Representative of telet.ai labo",name:"Keita Okuizumi",tagline:"To the sky, to the sea, and to the code - a passion for AI development born of an unusual career",paragraphs:['As a former aircraft pilot and small-vessel captain, I have been dealing with weather both in the air and at sea for many years. In the cockpit and wheelhouse, I watched changes in the barometer, deciphered the isobaric pressure lines, and compared them with the movement of wind and clouds. The sense of "reading data, judging, and acting" that I developed through this repetitive process is the starting point for me today.',`I started learning Python programming at the age of 57. The impetus was simple curiosity, but as soon as I started writing code I was fascinated by the possibilities of AI agents. I created applications with my own hands, ran them, and improved them. In the process, I realized something. In past projects, I was involved as a non-engineer, and development was always done by someone else. However, once I started writing the code myself, I began to see the essence of the problem. This is the bottleneck here" or "This design cannot be expanded" - by having an engineer's point of view, the speed of realizing what I want to do has increased dramatically.`,"Developing weather services by combining field experience with data science, building AI agents to improve operational efficiency, and various experimental projects. As I continued to work with my hands, I became convinced not only that the technology was interesting, but also that there must be people who need this.","Our goal is not to chase the cutting edge. We face the customer's issues and budget, and deliver only what is truly necessary at a reasonable cost. We are not a large organization, so we are able to provide development support that is tailored to the needs of each individual customer. We want to use this technology for the benefit of society - that is why we launched telet.ai."]},contact:{title:"Organization Information",company:"telet.ai labo",ceo:"Representative: OKUIZUMI Keita",button:"Contact Us"},studios:{tokyo:{title:"Tokyo Studio",address:"#301 First Hills A Musashikoganei, 3-3-21 Nukuiminamicho, Koganei-shi, Tokyo 184-0014, Japan"}}},Ev={title:"Portfolio",subtitle:"See our track record of utilizing AI technology",techStackLabel:"Technology Stacks",viewProject:"View Project",privateProject:"Private Projects",visualizationLabel:"data visualization",cta:{title:"Contact Us",description:"Please feel free to contact us to discuss your project or for technical inquiries.",button:"Contact Us"},projects:{"traffic-route-ai":{title:"AI Transit Route Selection System",subtitle:"Route planning for public transport in Malaysia",description:"A transit route selection system that searches bus and train connections between an origin and destination and compares options by arrival time, journey duration, and transfers. Bus-only and train-only filters, nearby station and stop search, and a multilingual interface help users choose routes for everyday journeys and travel.",techStack:["Public Transit Routing","Bus & Rail Integration","Route Comparison","Multilingual UI"]},"multilingual-assistant":{title:"AI Multilingual Assistant",description:"Intelligent chat assistant for multi-language inquiries, building 1,573 vector indexes and implementing precise answer control through conditional branching, NG word filtering, and keyword triggering. Consistent and highly accurate answers to user questions in multiple languages, including Japanese, English, and Chinese, are generated through cross-language semantic search.",techStack:["RAG","Vector Search","Multilingual Embedding","LLM","conditional branch logic"]},"weather-forecast":{title:"AI Weather Forecasting with Weather API",description:"Developed an AI system that learns from past weather data and makes real-time forecasts of recent weather conditions. LLM analyzes past data obtained from JMA API, OpenWeatherMap, etc., and generates short-term forecasts for temperature, precipitation probability, wind speed, etc. Combining pattern recognition of time-series data with past similarity pattern search by RAG, the system achieves highly accurate forecasts that complement conventional statistical models.",techStack:["Weather API Linkage","LLM","RAG","Time Series Data Analysis","real time processing"]},"local-llm":{title:"In-house Server / On-Prem Local LLM",description:"We deliver AI tailored to your business operations, ready to use within your company, with Open WebUI as a common entry point, fast inference lanes with Ollama/MLX, and completely separated data domains for each UID. In-house documents are captured in a RAG with English embedding and automatic Japanese query translation, and business skills such as news summarization, document organization, and routine tasks are safely executed side by side. The skill execution system is isolated as an OpenClaw container and protected by three layers of Cloudflare edge and local Nginx. We provide comprehensive support for model selection, skill design, and operational policies, and start up your company's dedicated AI in a form that can be used from the day it is deployed.",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"Interplanetary Orbital Arrival Prediction (AI)",description:"Developed a system to analyze optimal departure points for interplanetary transfer by obtaining planetary position data, orbital elements, and gravity parameters from NASA API. Calculate orbit calculations considering Hohmann transfer orbit and gravity assist using API and Python, and display launch window, time required, and fuel efficiency. The system is used as a simulation tool for educational institutions and space-related companies. *AI function is currently removed, calculation is in progress with API and Python.",techStack:["NASA API (Horizons, SPICE)","Orbital Dynamics Calculation","Python","data visualization"]},"real-estate":{title:"Real Estate Data AI Analysis",description:"Developed a system that collects real estate information for a specific region via web scraping and performs advanced data analysis using LLM and Python. Variables such as property price, tsubo unit price, building age, and station distance are extracted, and price trend anal

227ysis, market forecasts, and investment return simulations are automatically generated. Features were also extracted from unstructured property descriptions using LLM to provide comprehensive market analysis combined with quantitative data. *Offering now available as a second version (FULL version provided separately)",techStack:["Web scraping (Scrapy, Playwright)","Python","Pandas","LLM Analysis","data visualization"]},"fortune-ai":{title:"Four Pillars AI Appraisal System",subtitle:"Reproducing traditional wisdom with technology.",paragraphs:["Four Pillars of Divination is the culmination of thousands of years of Eastern divination. This system reproduces its traditional divination logic completely in Python, combined with interpretation and advice generation using LLM (Large Language Models). The AI faithfully reproduces the appraisal process performed by an expert fortune teller.","After entering the date, time, and place of birth, the system retrieves the sunrise and sunset times and coordinate information at that time from the Astronomy API. Based on these data, the system accurately calculates the zodiac sign, the ten gods, and the balance of the five elements. Logic based on the divination science of the Four Pillars of Destiny is indexed in Python and combined with statistical data and RAG (Retrieval Augmentation Generation) information to improve the accuracy of the life formula analysis.","The calculated life-style data is explained in natural language by LLM in an easy-to-understand manner. Personalized appraisal results are generated, including the flow of fortune, compatibility of interpersonal relationships, and suitable occupation for you. You can obtain answers with the same depth as if you had consulted a professional fortune teller.","It is currently available as a second version. You can try out the basic kumishiki analysis and fortune analysis. The full version, which includes more detailed analysis and multifaceted advice, is available separately."],techStack:["Python","Astronomy API (Sunrise-Sunset, TimeZoneDB)","Four Pillar Logic","LLM","Coordinate and time calculation"]},"google-workspace":{title:"Google Workspace AI integration",description:"Developed a system that links Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) with LLM to read, edit, and automatically process business data. AI assists with daily tasks such as summarizing email content, generating drafts for automatic replies, searching and analyzing Drive files, extracting schedules from Calendar, and proposing adjustments. Workspace operation from the Agent is also realized through secure API connection with OAuth authentication and MCP compliant tool design. Second version is now available (FULL version will be provided separately).",techStack:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"]},"world-news":{title:"World NEWS Multilingualization",description:"Development of a system that collects articles in real time from news sources around the world and performs multilingual translation, summarization, and categorization using LLM. Translates original articles in English, Chinese, French, Arabic, and other languages into Japanese and organizes them by topic. Efficient international news monitoring is achieved not by simple machine translation, but by natural translation based on contextual understanding using LLM and summary generation that extracts the main points of the article. *Offering now available as a second version (FULL version provided separately).",techStack:["News API","RSS collection","LLM Translation and Summary","multilingual processing","Auto-categorization","scheduled batch processing"]},"security-monitoring":{title:"Security Log Monitoring AI Manager",description:"Developed a system for real-time monitoring of corporate security logs and advanced threat detection and analysis using LLM. Various log sources such as firewalls, IDS/IPS, authentication logs, and application logs are collected and analyzed by a Python module, and compared with past attack patterns, vulnerability information, and incident cases accumulated in RAG. When an abnormal pattern is detected, LLM explains the threat level, scope of impact, and recommended response in natural language, and immediately alerts the person in charge. If the same pattern recurs, a high priority alert is automatically issued to speed up the incident response.",techStack:["Python Monitoring Module","Log collection (Fluentd, Logstash)","RAG","Threat Pattern DB","LLM Analysis","Alert notifications (Slack, PagerDuty)"]}}},Rv={learnMore:"Read more",getStarted:"begin to ...",contactUs:"Contact Us",backToHome:"Return to Home"},Nv={title:"Privacy Policy",subtitle:"Privacy Policy",lastUpdated:"Last update: January 23, 2026",intro:'telet.ai (the "Service") considers the protection of your personal information to be an important responsibility and has established the following privacy policy.',sections:{collection:{title:"1. information to be collected",description:"The Service may collect the following information when you contact us or use the Service.",items:["Email Address","Your name (optional)","Inquiry Details","Other information voluntarily provided by you"]},purpose:{title:"2. purpose of use of information",description:"Personal information collected will be used only for the following purposes",items:["Answering and responding to inquiries","Contact and information about our services","Communication required for project progression","Analysis for service improvement (in a format that does not identify individuals)"]},thirdParty:{title:"3. provision to third parties",description:"The Service will not provide your personal information to third parties except in the following cases",

227items:["If the customer consents","When there is a request for disclosure based on laws and regulations","When necessary for the protection of the life, body, or property of a person"]},security:{title:"4. information management",description:"The Service will properly manage collected personal information and take reasonable security measures to prevent unauthorized access, loss, leakage, and falsification."},email:{title:"5. handling of e-mails",description:"The Service will handle e-mails in accordance with the following policy.",items:["We will use the contents of your e-mail only for the purpose of responding to your inquiry and providing our services.","We will not sell, rent, or share your email address with any third party","We may send you information emails about our services. If you wish to unsubscribe, please use the unsubscribe link in the email or contact us using the contact information below.","SSL/TLS encryption is used for e-mail communications to ensure secure communications","We will securely delete your email after storing it for an appropriate period of time."]},cookies:{title:"6. use of cookies",description:"Cookies can be disabled in your browser settings."},external:{title:"7. use of external services",description:"This service may use the following external services. Please refer to the respective links for the privacy policy of each service.",items:["Google Analytics (access analysis)","Other cloud services required for business operations"]},changes:{title:"8. changes to privacy policy",description:"This policy is subject to revision without notice in accordance with amendments to laws and regulations or changes in service content. The revised policy will become effective when posted on this page."},contact:{title:"9. contact us",description:"If you have any questions regarding this policy, please contact us at",company:"telet.ai",contactLink:"Go to Inquiry Form"}}},Gv={title:"Terms of Use",subtitle:"These are the terms and conditions for using this site and for booking a consultation, making inquiries, and requesting information from telet.ai.",lastUpdated:"Last update: January 22, 2026",intro:'These Terms of Use (hereinafter referred to as the "Terms") These Terms of Use (hereinafter referred to as the "Terms") apply to the website operated by telet.ai (hereinafter referred to as "we", "us", "our", or "ours"). (hereinafter referred to as "we", "us", or "our"). (hereinafter referred to as the "Site") operated by telet.ai (hereinafter referred to as the "We"). The Site is provided primarily as a landing page for consultation appointments, inquiries, and document requests, and does not provide for account creation or paid billing functions on the Site.',sections:{scope:{title:"1. scope of application",description:'These Terms and Conditions apply to all visitors and users of the Site ("Users"). The Terms of Use shall apply to all visitors and users of the Website (hereinafter referred to as "Users"). In the event that a separate NDA, outsourcing agreement, individual contract, etc. is concluded between us and a User, such document shall take precedence over the Terms and Conditions.'},prohibited:{title:"2. prohibited items",description:"In using the Site, Users shall not engage in any of the following acts",items:["Acts that violate laws and regulations or public order and morals","Any act that infringes on our or any third party's intellectual property rights, privacy, or other rights or interests","Acts that interfere with the operation of this site or related systems through unauthorized access, excessive load, sabotage, etc.","Submitting false information or impersonating a third party in inquiry forms, etc.","Scraping, reverse engineering, or excessive automated access without our prior permission"]},disclaimer:{title:"3. disclaimer",description:'we provide this site on an "as is" and "as available" basis. To the fullest extent permitted by law, we disclaim all warranties with respect to this site.',items:["Information on this site is subject to change or deletion without notice.","We do not guarantee the accuracy, completeness, or fitness for a particular purpose of the information on this site.","We will not be liable for any damages resulting from your use of this site or reliance on information contained herein.","Responses to consultations and inquiries are for general information only and do not constitute legal, tax, medical or other professional advice."]},ip:{title:"4. intellectual property rights",description:"All rights to the text, images, designs, trademarks, logos, etc. on this site belong to us or to the rightful right holders. You may not use them without the permission of the right holders."},links:{title:"5. links to external sites",description:"This website may contain links to third-party websites. We are not responsible for the content or operation of the linked sites."},changes:{title:"6. modification of these Terms and Conditions",description:"We reserve the right to change these Terms and Conditions from time to time as necessary. The amended Terms of Use will become effective when posted on the Site."},governingLaw:{title:"7. governing law and jurisdiction",description:"These Terms and Conditions shall be governed by and construed in accordance with the laws of Japan. The Tokyo District Court shall have exclusive jurisdiction as the court of first instance over any dispute arising out of or in connection with this Site."},contact:{title:"8. contact us",description:"If you have any questions regarding these Terms and Conditions, please contact",company:"telet.ai",contactLink:"Go to Inquiry Form"}}},Ov={title:"regular motion",subtitle:"AI predicts the",description1:"Weather information is provided in real time by each country. Your country is displayed in the center of the map. Yesterday's weather and today's weather are actual records - the rain clouds should have been in your sky at that date and time.",description2:"However, this animation also animates future weather movements.",description3:"

227Vectors past data and searches for patterns at high speed. Weather patterns can also be predicted from the surrounding environment. Similarly, it is possible to predict harvest, production, and sales by calculating elements (i) from the environment and advanced statistical values from past performance and having AI analyze them.",refresh:"refresh (e.g. memory)",loading:"Loading weather map..."},Bv={hero:{badges:["Enterprise","Local LLM","Custom Agent"],title:"AI Agent Design and Development for Enterprises",description:"Design and build dedicated AI Agents tailored to business requirements"},mission:{title:"Custom AI Agent Development Services",description:"We consistently develop custom AI Agents that address your company's unique business issues, from design to implementation and operation. Our architectural design emphasizes compatibility with existing mission-critical systems and SaaS environments, enabling smooth integration with APIs, databases, and internal tools. Our engineers are familiar with LangChain, AutoGen, CrewAI, and other major frameworks, Our engineers are familiar with LangChain, AutoGen, CrewAI, and other major frameworks, and will propose the best solution for your company's technology stack and operational structure. We do not only develop one-off chatbots, but also build multi-agent systems in which multiple Agents cooperate with each other to perform tasks, and Agentic Workflows that work in conjunction with external tools. We support maximizing the business value brought by AI Agents through a long-term partnership that includes continuous improvement after implementation, following model updates, and maintenance of operational monitoring systems.",imageAlt:"Custom AI Agent Development Services"},summary:{title:"Agent Development Trends",paragraphs:['From 2025 to 2026, AI Agent development is moving from the demonstration phase to the full-scale corporate implementation phase. The concept of "Agentic AI" has taken root, and autonomous business execution systems that go beyond mere chatbots have begun operating in various industries.',`Computer-Using Agents" that directly control browsers and desktops, such as OpenAI's Operator, Anthropic's Computer Use, and Google's Project Mariner, have become practical. The Model Context Protocol (MCP) is a burgeoning ecosystem, enabling plug-and-play integration with thousands of tools.`,"Multi-agent architectures have reached maturity, with specialized Agents working together to perform complex tasks such as project management, customer support, and data analysis. A2A (Agent-to-Agent) protocol standardization has also made progress in Agent collaboration between different vendors.",'The governance model has evolved from "Human-in-the-Loop" to "Human-on-the-Loop" for corporate implementations, with a mechanism for post-agent decision auditing and automatic escalation based on risk level as standard features, accelerating adoption in regulated industries such as finance, healthcare, and manufacturing. The adoption of this system is accelerating in regulated industries such as financial, medical, and manufacturing.']},features:{title:"Design and Development",subtitle:"Design & Development",items:[{title:"Agent design based on business requirements",description:"Extract use cases from business flow analysis, decompose tasks and clarify Agent responsibilities. We determine whether processing can be completed by a single Agent or requires multi-Agent coordination, and select the optimal inference patterns such as ReAct, Plan-and-Execute, and Reflexion."},{title:"RAG / Tool / Workflow integration",description:"Highly accurate RAG pipelines combining Embedding Generation, Chunk Strategy, and Hybrid Search, standardized connection to external APIs and databases with MCP compliant Tool definitions, and multi-step workflow orchestration using LangGraph and CrewAI. Orchestrate multi-step workflows with LangGraph and CrewAI."},{title:"Security and authorization design",description:"Access control of Agent functions by RBAC/ABAC, Prompt Injection countermeasures, and PII detection and masking are implemented. Audit logs of all inputs and outputs are retained, and SOC2 and ISO27001 compliant environment operation is supported."},{title:"Implementation including operation and monitoring",description:"Implement Trace visualization with LangSmith, Langfuse, etc., and real-time monitoring of latency, token consumption, and error rate. Build a continuous improvement cycle including anomaly detection alerts, A/B testing infrastru

227cture, and Regression Test when updating models."}]},techStack:{title:"Technology Stacks",subtitle:"Platform & Technology",categories:{agentFramework:{title:"Agent Framework",items:["AutoGen 0.4 / AG2","CrewAI","LangGraph","OpenAI Agents SDK","Anthropic MCP","Google ADK (Agent Development Kit)","Semantic Kernel","Proprietary Agent Platform"]},apiLayer:{title:"API Layer",items:["FastAPI / Litestar","gRPC / Connect","GraphQL Federation","Server-Sent Events (SSE)","WebSocket / NATS","OAuth 2.1 / OIDC","API Gateway (Kong / Envoy)","Adaptive Rate Limiting"]},vectorDb:{title:"Vector DB / RAG",items:["Pinecone","Weaviate","Qdrant","Milvus","ChromaDB","pgvector","FAISS","Turbopuffer"]},infrastructure:{title:"Infrastructure",items:["Kubernetes (EKS / GKE / AKS)","Docker / Podman","On-Premise GPU Cluster","Private Cloud","Serverless (Lambda / Cloud Run)","OpenTelemetry / Langfuse","Terraform / Pulumi","ArgoCD / Flux"]}},updateNote:{title:"Main update points for 2026",description:"In Agent Framework, official SDKs/protocols from major vendors such as OpenAI Agents SDK, Anthropic MCP, and Google ADK are emerging; in API Layer, gRPC and SSE have become the standard for Agent-to-Agent communication, and the transition to OAuth 2.1 is underway. In Vector DB, Qdrant and Turbopuffer are gaining attention for their high-speed search."}},cta:{title:"Contact Us",description:`We can consult with you from the requirements arrangement.

228PoC and local LLM configurations can also be considered.`,primaryButton:"Contact Us",secondaryButton:"Technical Consultation"}},Kv={title:"Frequently Asked Questions",subtitle:"FAQ",items:[{question:"Can I use my existing manuals and FAQs without modification?",answer:"Yes, you can use it; it supports most formats, including PDF, Word, Excel, HTML, and text files. Simply upload your existing internal documents as they are, and AI will learn the content and use it to answer your questions. No format conversion or special processing is required."},{question:"Is it safe to use with data containing sensitive information?",answer:"Yes, you can use our service securely. On-premise (in-house servers) or in a private cloud environment, data is never transmitted outside the company. We can propose configurations to meet your security requirements."},{question:"How long does it take to implement?",answer:"A standard FAQ bot can be implemented in as little as 2-4 weeks. If integration with existing systems or advanced customization is required, we estimate 1-3 months. We can also start with a small-scale PoC (proof of concept)."},{question:"Do you use external APIs such as ChatGPT or Claude?",answer:"Choice is available upon request. We support both configurations that use external APIs (OpenAI, Anthropic, Google, etc.) and configurations that use open source LLMs (Llama, Mistral, Qwen, etc.) that run completely locally. We can propose the best configuration for your cost, security, and performance requirements."},{question:"How accurate is the AI's response?",answer:"Depending on the application and data quality, a properly designed RAG system can return accurate answers to questions for which relevant documents exist with 90% or greater accuracy. Mechanisms to control halcination (generation of erroneous information) are also built in as standard."},{question:"Do you support languages other than Japanese?",answer:"Yes, multilingual support is available. We support all major languages, including Japanese, English, Chinese, and Korean. Instead of having separate models for each language, a single system can handle multilingual queries."},{question:"Can you integrate with existing systems (CRM, chat tools, etc.)?",answer:"Yes, it can be integrated; it supports API integration with major business tools such as Salesforce, HubSpot, Slack, Microsoft Teams, and LINE WORKS. You can incorporate an AI assistant without changing your existing workflow."},{question:"What is your fee structure?",answer:"It depends on the size and requirements of the project. While initial construction and monthly operation fees are generally combined, we also accept requests for spot development or consulting services only. Please let us know your requirements first. We will provide you with a rough estimate."}]},Vv={title:"Rate Plans",subtitle:"Pricing Plans",description:"Flexible plans to fit the size of your business",plans:[{name:"starter",price:"29,800 yen",period:"Monthly/user",description:"The best plan for small teams. Basic AI functions at an affordable price.",features:["Up to 100 calls per month","Basic Transcription","Keyword Detection","Email Support","7-day data retention"],cta:"Get started for free",popular:!1},{name:"professional",price:"Â¥79,800",period:"Monthly/user",description:"Ideal for growing teams. Advanced analytical features.",features:["Up to 500 calls per month","High-precision AI transcription","Emotional Analysis","CRM Linkage","Priority Support","30-day data retention","Custom Reports"],cta:"14-day free trial",popular:!0},{name:"enterprise",price:"Contact Us",period:"custom",description:"Full-featured plans for large organizations",features:["Unlimited calls","Dedicated AI Model","on-premise support","Dedicated Support","Custom collaboration","SLA Assurance","Security Audits","Unlimited data retention"],cta:"Consult with Sales",popular:!1}],faq:{title:"Frequently Asked Questions",items:[{question:"Is there a free trial?",answer:"Yes, a 14-day free trial is available for Professional plans. No credit card required to get started."},{question:"Can I change my plan at any time?",answer:"Yes, you can upgrade or downgrade your plan at any time. Changes will be applied starting with the next billing cycle."},{question:"Is there a contract term bound?",answer:"Monthly plans have no contract term restrictions. If you choose an annual contract, you will receive a 20% discount."}]}},Fv={hero:{badge:"AI-Powered Technology",title:"AI Transcription",description:"Advanced machine learning models provide 99.5% accuracy. AI trained on millions of customer interaction data converts voice to text.",features:["99.5% accuracy","real time processing","Support for more than 40 languages"]},features:{title:"Main Functions",subtitle:"Advanced transcription engine that goes beyond simple speech recognition",items:[{title:"real-time transcription",description:"Get real-time transcripts during conversations. Gain instant insight and never miss a real-time coaching opportunity."},{title:"multilingual support",description:"Supports more than 40 languages and covers global teams with automatic language detection."},{title:"contextual understanding",description:"AI models trained on industry-specific terminology accurately recognize technical and business terms."},{title:"speaker separation",description:"Automatically identifies and separates different speakers in a conversation and assigns clear speaker attributes."},{title:"custom lexicon",description:"Learning your company's unique product names, service names, and technical terms provides even higher accuracy."},{title:"Quality Assurance",description:"Automatic quality checks and reliability scoring ensure the highest quality transcription."}]}

228,useCases:{title:"Usage Scenes",subtitle:"Industries and teams requiring accurate call transcription",items:[{title:"Sales Team",description:"Reflect on sales calls and identify success patterns. Improve talk effectiveness and coach team members with specific examples."},{title:"Customer Support",description:"Accurately records customer issues. Maintain a comprehensive support history to ensure consistent service quality."},{title:"quality control",description:"Monitor compliance and ensure script adherence. Maintain detailed records for audits and training."}]},stats:{accuracy:{value:"99.5%",label:"transcription accuracy"},languages:{value:"40+",label:"Languages Supported"},latency:{value:"<2 sec.",label:"processing delay"},availability:{value:"24/7",label:"continuous processing"}}},Wv={hero:{badge:"Smart Detection",title:"Keyword Tracking",description:"Automatically monitor competitor mentions, rebuttals, and important topics. Never miss important signals from customer conversations.",badges:["Competitive Tracking","Response Detection","Topic Analysis","custom keyword"]},features:{title:"Track critical information",subtitle:"Automatic detection and analysis of keywords from all conversations",items:[{title:"Competitive Monitoring",description:"Track competitor mentions to understand how prospects compare your solution to alternatives on the market."},{title:"Response Detection",description:"Automatically identify common objections and concerns and develop effective responses and training."},{title:"custom keyword",description:"Define and track custom keywords and phrases specific to your business, product or industry."},{title:"Trend Analysis",description:"Discover emerging trends from customer conversations and adapt your strategy based on actual market signals."},{title:"context extraction",description:"Get context before and after the keyword, not just the mention of it. Understand n

228ot only what was said, but why and how it was said."},{title:"Smart Search",description:"Instantly search all conversations to find exactly where and when a particular topic was discussed."}]},applications:{title:"Usage Scenes",items:{competitive:{title:"Competitive Intelligence",description:"Deep understanding of the competitive landscape:",points:["Track your most frequently mentioned competitors","Understand how prospective customers compare features and prices","Identify your competitive advantages and weaknesses","Creating effective anti-competitive materials"]},objections:{title:"response to a rebuttal",description:"Turn a rebuttal into an opportunity:",points:["Identify most common customer concerns","Analyze top performers' rebuttal responses","Playbook with proven response.","Train your team with effective turnaround"]},product:{title:"Product Insights",description:"Promoting product development through customer feedback:",points:["Identify most requested features","Understand challenges to current functionality","Discover unexpected use cases","Prioritize roadmap based on customer needs"]},compliance:{title:"Compliance & Quality",description:"Ensure regulatory compliance and quality:",points:["Monitor required disclosures and legal language","Flag conversations that may violate policy","Track script compliance and best practices","Generate compliance reports for audit"]}}},advancedFeatures:{title:"Advanced Features",items:[{title:"ambiguous retrieval",description:"Automatically detects keyword variations, synonyms, and related terms without manual configuration."},{title:"historical analysis",description:"Analyze keyword trends over time to identify seasonal patterns and emerging topics."},{title:"Real-time alerts",description:"Receive immediate notifications when important keywords are mentioned in live conversations."}]}},qv={hero:{badges:["Enterprise","Local LLM","In-house server operation"],title:"On-Premise LLM Construction",description:"Operate LLM in your own environment, keeping data outside the company"},mission:{title:"On-Premise LLM Construction Service Overview",paragraphs:["For companies that cannot release confidential data outside the company, we build an LLM environment that can be completed on their own servers or in a private cloud. This solution is ideal for organizations that require strict information management, such as financial institutions, medical institutions, government agencies, and research institutes, because it does not use any public API and all processing is completed within the customer-managed infrastructure.","We provide comprehensive support from model selection, server construction, inference engine optimization, and operation monitoring, using commercially available open-weight models such as Llama 3.3, Qwen 2.5, Mistral, and DeepSeek with high-performance inference engines such as vLLM, Ollama, and SGLang. These models are run on high-performance inference engines such as vLLM, Ollama, and SGLang to provide a low-latency, high-throughput inference environment.","We support a wide range of use cases such as integration with existing systems, chatbots, internal RAGs, document summarization, and code generation. We can design to meet enterprise requirements, including business specialization through model fine tuning, NVIDIA GPU / Apple Silicon optimization, multi-tenant design, API gateway construction, and more."]},summary:{title:"On-Premise LLM Trends",paragraphs:["In 2025-2026, the utility of on-premise LLMs is rapidly improving. The performance of open-weighted models has flanked that of closed models, and the number of models offered with commercially available licenses has significantly lowered the hurdle for in-house operations.","The models with 8B to 70B parameters have reached a practical level for many business tasks, GGUF quantization enables high-speed inference even on consumer GPUs.","For inference engines, vLLM's Continuous Batching and PagedAttention dramatically increase throughput, and SGLang's RadixAttention accelerates complex multi-turn interactions. Ollama has become the de facto standard for local development environments, and llama.cpp is Apple Silicon optimized for fast inference on M-series Macs.","In enterprise adoption, in addition to data sovereignty and compliance, long-term cost optimization (moving away from pay-as-you-go API billing) is increasingly becoming a motivation for adoption. Hybrid configurations (local for sensitive processing and cloud API for general purpose processing) have become a common option."],icons:[{label:`Private
229Cloud`},{label:`Data
230Sovereignty`},{label:`Cost
231Optimization`},{label:`Enterprise
232Security`}]},features:{title:"Design and development features",subtitle:"Design & Development",items:[{title:"Infrastru

232cture design and GPU optimization",description:"Design the optimal hardware configuration for your workload and budget, including NVIDIA A100/H100, RTX 4090/5090, and Apple Silicon (M2 Ultra/M3 Max/M4 Max), and create an inference environment that takes full advantage of GPU performance through CUDA, MPS, and Metal optimization. CUDA, MPS, and Metal optimization to maximize GPU performance."},{title:"Model selection and optimization",description:"Select the optimal model from Llama, Qwen, Mistral, DeepSeek, etc. considering task characteristics, language requirements, and resource constraints. Quantization (GGUF/AWQ/GPTQ), KV cache optimization, and batch processing tuning are used to achieve both accuracy and throughput."},{title:"Inference engine construction",description:"Select and build inference engines such as vLLM, SGLang, Ollama, TGI, etc. according to your application, and apply the latest technologies such as Continuous Batching, Speculative Decoding, and Prefix Caching to provide low-latency and high-throughput inference APIs. We provide low-latency and high-throughput inference APIs."},{title:"Operation, Monitoring and Security",description:"Implement inferential metrics monitoring with Prometheus/Grafana, access control with API gateway, audit logs, auto scaling, and failure recovery. Build a production operation environment that meets enterprise security requirements."}]},techStack:{title:"Technology Stack",subtitle:"Platform & Technology",categories:{openWeightModels:{title:"Open-Weight Models",items:["Llama 3.3 70B / 8B","Qwen 2.5 72B / 32B / 7B","Mistral Large / Nemo","DeepSeek V3 / R1","Gemma 2 27B / 9B","Phi-4","Command R (Cohere)"]},japaneseModels:{title:"Japanese-specific model",items:["ELYZA-japanese-Llama-2","Swallow 70B / 13B","Japanese-Llama-3","Qwen-2.5-Japanese","Karakuri-LM"]},inferenceEngine:{title:"Inference Engine",items:["vLLM (Continuous Batching)","SGLang (RadixAttention)","Ollama (local development)","llama.cpp (Apple Silicon)","TGI (Hugging Face)","TensorRT-LLM (NVIDIA)","ExLlamaV2 (quantization specialization)"]},quantization:{title:"Quantization",items:["GGUF (llama.cpp standard)","AWQ (high precision 4bit)","GPTQ (GPU optimization)","bitsandbytes(8bit/4bit)","EETQ (Efficient Efficiency Quantization)"]},hardware:{title:"Hardware",items:["NVIDIA H100 / A100","NVIDIA L40S / A10G","RTX 4090 / 5090","Apple Silicon M2 Ultra","Apple Silicon M3/M4 Max","AMD MI300X"]}

232,infrastructure:{title:"Infrastructure",items:["Docker / Kubernetes","NVIDIA Triton Server","API Gateway (Kong / Envoy)","Prometheus / Grafana","Private Cloud (VMware / OpenStack)","Air-Gapped Environmental Support"]}},updateNote:{title:"Main update points for 2026",description:"In open-weight models, Llama 3.3 achieves GPT-4 class general-purpose performance and greatly improves Japanese language performance; the Qwen 2.5 series expands the choice with a variety of sizes and well-balanced performance; DeepSeek V3/R1 attracts attention for its inference cost performance; and the vLLM 2.0 Chunked Prefill and SGLang's RadixAttention inference engines speed up multi-turn dialogue. In the inference engine, vLLM 2.0's Chunked Prefill and SGLang's RadixAttention speed up multi-turn interactions; Apple Silicon optimization of llama.cpp makes 70B model inference practical on M4 Max; and the Quantization technology offers 4-bit precision and high performance. Quantization techniques achieved both 4-bit precision and inference speed, and GGUF became the de facto standard format."}},cta:{title:"Contact Us",description:`We can consult with you from the requirements arrangement.

233PoC and local LLM configurations can also be considered.`,primaryButton:"Contact Us",secondaryButton:"Technical Consultation"}},Uv={hero:{badges:["Enterprise","RAG","search extension generation"],title:"Vector / RAG Development",description:"Build a RAG system to link internal knowledge with LLM"},mission:{title:"Vector / RAG Development Services Overview",paragraphs:["We will build a RAG (search extension generation) system that allows employees to search and ask questions in natural language by linking the knowledge assets accumulated in the company with LLM. We integrate various data sources such as manuals, minutes, technical documents, FAQs, Slack history, Confluence, SharePoint, etc. to create an environment where organizational knowledge can be immediately utilized.","We maximize search accuracy and response quality by applying the latest technologies such as Hybrid Search (vector search + keyword search), Reranking, hierarchical chunking, metadata filtering, etc., in addition to simple document search. We clearly indicate the source of the answer to both suppress halcination and ensure reliability.","We support a variety of environments including AWS, GCP, Azure, and on-premise, and integrate with existing authentication infrastructure and authority management. We provide consistent support for maintaining data freshness (differential updates and automatic re-indexing), analyzing usage logs, and continuously improving accuracy."]},summary:{title:"RAG Technology Trends",paragraphs:['Between 2025 and 2026, RAG technology is moving into the "basic implementation to production quality" phase. In addition to simple vector search, Hybrid Search, which combines multiple search methods, Reranking, which reorders search results, and Query Decomposition, which decomposes and processes complex questions, have become standard configurations.',"Chunking strategies have also evolved, with hierarchical chunking that takes document structure into account, overlapping chunks, and multimodal chunking that processes tables and figures separately, all contributing to improved accuracy. Contextual Retrieval (adding contextual information to chunks) has also been widely recognized as effective.","For vector DB selection, in addition to managed services such as Pinecone, Weaviate, Qdrant, and Milvus, PostgreSQL pgvector and local FAISS offer a wide range of options to meet your requirements. Embedding models are also available, including text-Embedding-3-large, Cohere Embed v3, and multilingual models.",'In enterprise deployments, authority management (ACL) linkage, PII masking, audit logs, and multi-tenant design are essential requirements, and there is a need to move from a "working PoC" to a "production-ready production system.'],icons:[{label:`Hybrid
234Search`},{label:`Multi-modal
235RAG`},{label:`Agentic
236RAG`},{label:`Enterprise
237Security`}]},features:{title:"Design and development features",subtitle:"Design & Development",items:[{title:"Data collection and preprocessing pipeline",description:"Data extraction and cleansing from PDF, Word, Excel, HTML, Markdown, Slack, Confluence, SharePoint, etc. Multi-modal support including OCR, table structure analysis, and image caption generation. Maintain freshness with differential updates and scheduled execution."},{title:"High-precision search system design",description:"Construct high-precision search combining Vector Search + BM25 Hybrid Search, Reranking by Cohere Rerank / Cross-Encoder, and Query Expansion and Decomposition. Metadata filtering and permission-based access control are also implemented."},{title:"Response generation and quality control",description:"Improved answer quality with context optimization, Few-shot prompts, and Chain-of-Thought inference. Source clarification, confidence score display, and halcination detection are implemented to ensure reliability for business use."},{title:"Operation and improvement cycle",description:"We provide operational support including usage log analysis, search hit rate and response satisfaction monitoring, continuous improvement through feedback loops, A/B testing infrastru

237cture, Embedding model updates, and chunking strategy reviews."}]},techStack:{title:"Technology Stack",subtitle:"Platform & Technology",categories:{embeddingModels:{title:"Embedding Models",items:["OpenAI text-embedding-3-large","Cohere Embed v3","Voyage AI","Jina Embeddings v3","BGE-M3 (multilingual)","multilingual-e5-large","Japanese-specific models"]},vectorDatabases:{title:"Vector Databases",items:["Pinecone (Managed)","Weaviate (Hybrid Search)","Qdrant (high performance)","Milvus (large scale)","Chroma (lightweight)","PostgreSQL pgvector","FAISS (local)"]},chunking:{title:"Chunking & Processing",items:["hierarchical chunking","Semantic Chunking","Overlapping Chunks","Sentence Window","Contextual Retrieval","Table and chart processing","Multimodal support"]},search:{title:"Search & Retrieval",items:["Hybrid Search (Vector + BM25)","Cohere Rerank","Cross-Encoder Reranking","Query expansion and decomposition","HyDE (Hypothetical Document Generation)","Self-Query Retrieval","Multi-Query Retrieval"]}},updateNote:{title:"Main update points for 2026",description:"In the Embedding model, text-Embedding-3-large and Cohere Embed v3 became the enterprise standard, and Japanese language performance improved significantly; Contextual Retrieval proved to be effective, and adding context to chunks became the standard method. Hybrid Search + Reranking has become a mandatory configuration for search, reducing the number of simple vector search-only configurations, and Agentic RAG (agents dynamically select search strategies) is being put into practical use. On the operational side, emphasis is being placed on establishing quality measurement and improvement cycles using RAG evaluation frameworks (RAGAS, LangSmith)."}},cta:{title:"Contact Us",description:`We can consult with you from the requirements arrangement.

238PoC and local LLM configurations can also be considered.`,primaryButton:"Contact Us",secondaryButton:"Technical Consultation"}},_v={hero:{badges:["Full Stack","LLM Integration","API Design"],title:"AI Application Development",description:"Design and build practical applications that leverage LLM"},mission:{title:"AI Application Development Service Overview",paragraphs:["We provide design and development of AI applications that create business value by analyzing data acquired from external APIs with LLM, linking APIs with diverse data sources such as CRM, ERP, SaaS, IoT sensors, and social media, and feeding acquired data into LLM in real time. We enable advanced data analysis that cannot be achieved with conventional BI tools, such as analytical report generation using natural language, anomaly detection, trend prediction, and automatic classification.","Not just simple API integration, but also integration of multiple data sources, data cleansing, and conversion to a format that is easy for LLM to interpret through structured processing. Prompt engineering maximizes the accuracy of analysis and generates output specialized for business requirements. We build practical applications that combine data and AI, such as sales data to predict business negotiations, customer support logs to perform VoC analysis, and sensor data to provide equipment maintenance recommendations.","We support a variety of interfaces such as web applications, mobile applications, Slack/Teams integration, and periodic batch processing. We provide consistent support from requirement definition to API design, LLM integration implementation, and production operation."]},summary:{title:"Trends in AI Application Development",paragraphs:['From 2025 to 2026, AI application development is entering a practical phase based on "API integration x LLM analysis". Unstructured data analysis and natural language insight generation, which could not be handled by traditional BI tools and dashboards, are beginning to be incorporated into enterprise systems.',"Of particular note is the maturation of LLM's Structured Output functionality, which enables strict JSON Schema-compliant output control, making an end-to-e

238nd automated pipeline a reality, where data acquired via APIs is analyzed by LLM and the results are fed directly into subsequent systems. Multi-stage execution of Function Calling enables data collection, integration, and analysis across multiple APIs to be handled in a single workflow.","In the data processing infrastructure, Polars has emerged as a standard for high-speed processing, and in combination with streaming processing, it facilitates real-time analysis.","In corporate implementations, needs have shifted from one-off PoC development to scalable design for production operation, requiring comprehensive architecture design that includes cost optimization, error handling, and audit log support."],icons:[{label:`Agentic
239AI`},{label:`MCP
240Protocol`},{label:`Multi
241Agent`},{label:`Human-on
242-the-Loop`}]},features:{title:"Design and development features",subtitle:"Design & Development",items:[{title:"API integration and data collection design",description:"Build a data collection infrastructure that supports various protocols such as REST API, GraphQL, WebSocket, and Webhook. We design a stable data acquisition pipeline by implementing authentication processes (OAuth, API key, JWT), rate limiting, and retry mechanisms. We also support data integration, scheduling, and differential acquisition from multiple APIs."},{title:"Data preprocessing and structuring",description:"Converts raw data into a format that can be efficiently analyzed by LLM, including JSON parsing, data cleansing, normalization, and missing value processing, and performs optimal data segmentation and summarization considering context window restrictions. Metadata assignment ensures traceability of analysis results."},{title:"LLM analysis and prompt design",description:"Maximize the accuracy of data analysis by designing prompt templates specific to your business requirements. We build analysis pipelines for classification, summarization, sentiment analysis, anomaly detection, prediction generation, etc. We utilize Function Calling and Structured Output to generate output in a format that can be easily integrated with subsequent systems."},{title:"Application implementation and operation",description:"We provide analysis results through a variety of interfaces, including FastAPI, web applications using Next.js, etc., Slack/Teams Bot, and scheduled batch execution. Dashboards, notification alerts, and automatic report generation are implemented and delivered in a form that business users can immediately utilize. We also provide operational monitoring, cost optimization, and continuous improvement."}]},techStack:{title:"Technology Stack",subtitle:"Platform & Technology",categories:{apiIntegration:{title:"API Integration",items:["REST / GraphQL / WebSocket","OAuth 2.1 / API key / JWT","Webhook / Polling","Apache Airflow / Prefect","Celery / Temporal","requests / httpx / aiohttp","Rate Limiting / Retry","Data Validation (Pydantic)"]},dataProcessing:{title:"Data Processing",items:["Pandas / Polars","JSON / JSONL Parser","Data Cleansing Pipeline","Apache Spark (Large-scale processing)","dbt (data conversion)","Great Expectations","Delta Lake / Iceberg","Streaming (Kafka / Flink)"]},llmCloud:{title:"Cloud LLM",items:["OpenAI GPT-4o / GPT-4.1","Claude 3.5 / Claude 4","Gemini 2.0 / Gemini 2.5","Command R+ (Cohere)","Mistral Large"]},llmLocal:{title:"Local LLM",items:["Llama 3.3 / Llama 4","Mistral / Mixtral","Qwen 2.5 / Qwen 3","DeepSeek V3 / R1","Gemma 2 / Gemma 3","Phi-4","ELYZA / Swallow (specialized in Japanese)","Command R (Cohere)"]},inference:{title:"Inference / Framework",items:["vLLM / SGLang","Ollama / llama.cpp","LiteLLM Proxy","Function Calling","Structured Output","Prompt Template Engine","LangChain / LlamaIndex","Proprietary Analysis Pipeline"]}

242,application:{title:"Application Layer",items:["FastAPI / Litestar","Next.js / React","Slack SDK / Teams SDK","Streamlit / Gradio","Scheduled Jobs (Cron / Cloud Scheduler)","PostgreSQL / MongoDB","Redis (Cache / Queue)","Docker / Kubernetes"]}},updateNote:{title:"Main update points for 2026",description:"In the LLM analysis area, GPT-4.1 and Claude 4's Structured Output functionality has matured to standardize strict JSON Schema-compliant output control, and multi-stage execution of Function Calling enables complex data collection and analysis across multiple APIs in a single pipeline. The multi-stage execution of Function Calling enables complex data collection and analysis across multiple APIs in a single pipeline. In data processing, Polars has become an established alternative to Pandas for high-speed processing, and in combination with streaming processing, it facilitates the construction of a real-time analysis infrastructure. At the application layer, external tool integration using MCP has become commonplace, accelerating the development of agentic applications in which LLMs call APIs autonomously to acquire and analyze data."}},cta:{title:"Contact Us",description:`We can consult with you from the requirements arrangement.

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243ารฝังภาษาอังกฤษและการแปลคำถามภาษาญี่ปุ่นโดยอัตโนมัติ และทักษะทางธุรกิจ เช่น การสรุปข่าว การจัดระเบียบเอกสาร และการทำงานประจำ จะถูกดำเนินการอย่างปลอดภัยควบคู่กันไประบบการใช้งานทักษะถูกแยกออกเป็นคอนเทนเนอร์ OpenClaw และได้รับการปกป้องโดยชั้นป้องกันสามชั้นจาก Cloudflare edge และ Nginx ท้องถิ่น เราให้การสนับสนุนอย่างครอบคลุมสำหรับการเลือกแบบจำลอง, การออกแบบทักษะ, และนโยบายการดำเนินงาน และเริ่มต้นระบบ AI เฉพาะของบริษัทคุณในรูปแบบที่สามารถใช้งานได้ตั้งแต่วันที่ติดตั้ง"},{title:"การคาดการณ์การมาถึงวงโคจรระหว่างดาวเคราะห์ (AI)",description:"รับข้อมูลดาวเคราะห์จาก NASA API และแปลการคำนวณวงโคจรใน LLM สร้างรายงานเกี่ยวกับหน้าต่างการปล่อยจรวด เวลาที่ต้องการ และประสิทธิภาพเชื้อเพลิง"},{title:"การวิเคราะห์ข้อมูลอสังหาริมทรัพย์ด้วยปัญญาประดิษฐ์",description:"รวบรวมข้อมูลอสังหาริมทรัพย์ด้วยการดึงข้อมูลจากเว็บไซต์และวิเคราะห์ด้วย LLM + Python สร้างแนวโน้มราคา การคาดการณ์ตลาด และผลตอบแทนจากการลงทุนโดยอัตโนมัติ"},{title:"ปัญญาประดิษฐ์ทำนายโชคชะตา (โหราศาสตร์สี่เสา)",description:"สร้างตรรกะของเสาหลักสี่ประการแห่งโชคชะตาในภาษา Python และแปลความหมายด้วย LLM. รับพิกัดและเวลาจาก API ดาราศาสตร์เพื่อการประเมินโชคชะตาและความเข้ากันได้อย่างเฉพาะบุคคล"},{title:"Google Workspace AI integration",description:"การผสานรวม LLM กับ Gmail, Drive, ปฏิทิน, ฯลฯ AI ช่วยในการสรุปอีเมล, ค้นหาไฟล์, และจัดตารางเวลา"},{title:"ข่าวโลก การแปลเป็นหลายภาษา",description:"การรวบรวมข่าวจากทั่วโลกแบบเรียลไทม์ แปลและสรุปเป็นหลายภาษาโดย LLM การแปลและการสรุปที่เป็นธรรมชาติโดยอิงจากความเข้าใจในบริบท"},{title:"ผู้จัดการ AI สำหรับการตรวจสอบบันทึกความปลอดภัย",description:"การตรวจสอบบันทึกความปลอดภัยแบบเรียลไทม์ การตรวจจับภัยคุà¸

243à¸„ามและการวิเคราะห์โดย LLM และการแจ้งเตือนทันทีโดยการจับคู่กับรูปแบบการโจมตีในอดีตโดย RAG"}]},eA={title:"วิธีการดำเนินการโครงการ",headline:"สิ่งที่คุณต้องการคือวิสัยทัศน์",paragraphs:['หมดยุคที่ความแตกต่างทางภาษาเป็นอุปสรรคต่อการทำธุรกิจแล้ว แม้ว่าเราจะใช้การแปลด้วย AI แต่เราก็สามารถจัดการการเจรจาธุรกิจและการพัฒนาใน 14 ภาษาได้ผ่านกระบวนการทำงานที่เป็นเอกลักษณ์ของเรา ซึ่งใช้ประโยชน์จากการแชร์ภาพอย่างเต็มที่ แม้แต่ข้อกำหนดที่ซับซ้อนก็สามารถตรวจสอบได้ด้วยภาพตัวอย่างเพื่อหลีกเลี่ยงการสื่อสารที่ผิดพลาด สิ่งเดียวที่สำคัญคือ "สิ่งที่คุณต้องการสร้าง"',"โครงการเริ่มต้นด้วยการประชุมเบื้องต้นในขั้นตอนการยื่นคำขอทางธุรกิจ หลังจากมีการแบ่งปันวิสัยทัศน์และประเด็นสำคัญของลูกค้าร่วมกันโดยทั่วไปแล้ว จะมีการลงนามในข้อตกลงไม่เปิดเผยข้อมูล (NDA) เพื่อสร้างสภาพแวดล้อมที่ข้อมูลลับสามารถพูดคุยได้อย่างปลอดภัย จากนั้นเราจะดำเนินการสัมภาษณ์อย่างละเอียดและยืนยันความต้องการต่าง ๆ ตามเนื้อหาที่ได้ เราจะจัดทำข้อเสนอและยืนยันทิศทางของโครงการโดยใช้ภาพตัวอย่าง เมื่อมีความเข้าใจตรงกันแล้ว เราจะจัดทำข้อกำหนดและเริ่มกระบวนการพัฒนา","กระบวนการนี้เป็นเพียงการแนะนำทั่วไปเท่านั้น เราจะปรับให้เข้ากับสถานการณ์และตารางเวลาของคุณอย่างยืดหยุ่น ไม่ว่าคุณจะอยู่ที่ใดในโลก แม้ว่าคุณจะพูดภาษาเดียวกันหรือไม่ก็ตาม ภาพและ AI จะช่วยสนับสนุนการสื่อสารของคุณ ทำไมไม่เข้าร่วมกับเราเพื่อเข้าสู่ตลาดแนวหน้าที่ยังไม่มีใครเข้าถึงมาก่อนล่ะ? ให้ telet.ai เป็นพันธมิตรของคุณในการสร้างรูปร่างให้กับไอเดียของคุณ"],cards:[{title:"การรับฟัง / ข้อตกลงไม่เปิดเผยข้อมูล",description:"หลังจากการพิจารณาเบื้องต้นทางอีเมลแล้ว จะมีการลงนามในข้อตกลงไม่เปิดเผยข้อมูล คุณสามารถให้รายละเอียดแก่เราได้อย่างสบายใจ"},{title:"การวางแผนและการตรวจสอบ",description:"มีการจัดเตรียมข้อเสนอและตัวอย่างภาพไว้ให้แล้ว รองรับ 14 ภาษา คุณจึงสามารถดำเนินการต่อได้โดยไม่ต้องกังวลเรื่องภาษา"},{title:"การสุ่มตัวอย่าง & เริ่มต้น",description:"เราจะจัดเตรียมข้อกำหนดและเริ่มดำเนินธุรกิจเมื่อมีการตกลงกัน"},{title:"การพัฒนา, การทดสอบ และการดำเนินการ",description:"ดำเนินการอย่างรวดเร็วผ่านการพัฒนาแบบอไจล์ เราให้การสนับสนุนอย่างต่อเนื่องตั้งแต่การทดสอบไปจนถึงการสนับสนุนการดำเนินงาน"}]},aA={badge:"กรณีศึกษา",title:"เรื่องราวแนะนำ AI",items:[{title:"โซลูชันเพื่อเพิ่มประสิทธิภาพทางธุรกิจและลดต้นทุน",subtitle:"ลดปริมาณงานประจำวันและสร้างสภาพแวดล้อมที่คุณสามารถมุ่งเน้นไปที่ธุรกิจหลักของคุณได้",description:'ธุรกิจขนาดกลางและขนาดย่อม (SMEs) จำนวนมากเผชิญกับปัญหาการขาดแคลนแรงงานและการจ้างงานที่ยากลำบาก แม้ว่าจะมีการจ้างบุคลากรใหม่แล้วก็

243ตาม ค่าใช้จ่ายในการฝึกอบรมก็สูง และยังมีปัญหาเกี่ยวกับอัตราการรักษาบุคลากรไว้ได้ โซลูชัน AI ของเราช่วยสนับสนุนงานประจำวัน เช่น การจัดการงบประมาณอัตโนมัติ การป้อนข้อมูลและการจัดระเบียบ การค้นหาเอกสารภายใน และการสร้างรายงานและเอกสารโดยอัตโนมัติผู้ช่วย AI จัดการงานประจำที่เคยใช้เวลามาก 24 ชั่วโมงต่อวัน 365 วันต่อปี โดยไม่มีข้อผิดพลาด บริษัทที่ได้นำ AI มาใช้ได้ตระหนักถึงการลดชั่วโมงการทำงานอย่างมีนัยสำคัญ จาก "การเพิ่มทรัพยากรบุคคล" สู่ "การร่วมมือกับ AI" เราจะพยายามอย่างเต็มที่เพื่อสนับสนุนการปฏิรูปสไตล์การทำงานของบริษัทคุณ'},{title:"ระบบอัตโนมัติสำหรับการสนับสนุนลูกค้า",subtitle:"บริการตลอด 24 ชั่วโมงช่วยปรับปรุงความพึงพอใจของลูกค้าและลดต้นทุนแรงงาน",description:"แชทบอท AI ให้บริการตอบคำถามที่พบบ่อยโดยอัตโนมัติ, การตอบกลับเบื้องต้นและการจัดเรียงคำถาม, และการตอบกลับแบบเรียลไทม์ในหลายภาษา การตอบกลับแบบเรียลไทม์ในหลายภาษา ผู้ปฏิบัติงานมนุษย์สามารถมุ่งเน้นไปที่กรณีซับซ้อนและปรับปรุงคุณภาพการบริการลูกค้า ยกระดับคุณภาพการบริการของคุณไปอีกขั้นด้วยการสนับสนุนลูกค้าด้วย AI"},{title:"ปัญญาประดิษฐ์เพื่อการจัดการความรู้และการใช้เอกสาร",subtitle:"สินทรัพย์ความรู้ภายในในรูปแบบที่สามารถนำไปใช้ได้ทันทีโดยทุกคน",description:'คู่มืออยู่ที่ไหน?" มีข้อมูลที่เฉพาะผู้มาก่อนเท่านั้นที่รู้" - ความรู้ที่ไม่เป็นรูปธรรมเป็นอุปสรรคต่อการเติบโตของบริษัทเทคโนโลยี RAG (การสร้างส่วนขยายการค้นหา) ของเราช่วยให้คุณสามารถดึงข้อมูลที่จำเป็นจากคู่มือภายใน คำถามที่พบบ่อย และเอกสารในอดีตได้เพียงแค่ถามคำถามในภาษาธรรมชาติ มันช่วยปรับปรุงการฝึกอบรมพนักงานใหม่ ทำให้ความรู้ที่ซ่อนอยู่ของพนักงานที่มีประสบการณ์กลายเป็นดิà¸

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243à¸à¸µà¹‰"}},iA={title:"นโยบายคุกกี้",subtitle:"ส่วนนี้อธิบายเกี่ยวกับคุกกี้และเทคโนโลยีอื่น ๆ (Cloudflare / Google Analytics) ที่ใช้บนเว็บไซต์นี้",lastUpdated:"อัปเดตล่าสุด: 22 มกราคม 2569",intro:'telet.ai ("เรา", "พวกเรา", "ของเรา") ("เรา") อาจใช้คุกกี้และเทคโนโลยีที่คล้ายกันเพื่อให้บริการและปรับปรุงเว็บไซต์นี้ รวมถึงเพื่อรักษาความปลอดภัยของเว็บไซต์ เราไม่ได้ตั้งค่าคุกกี้ติดตามพิเศษของเราเองเพื่อวัตถุประสงค์ในการโฆษณา (แม้ว่า Cloudflare และ Google Analytics อาจตั้งค่าคุกกี้หรือเทคโนโลยีที่คล้ายกันตามข้อกำหนดการให้บริการของพวกเขา)',sections:{what:{title:"1. คุกกี้คืออะไร?",description:"คุกกี้คือไฟล์ข้อความขนาดเล็กที่ถูกเก็บไว้ในเบราว์เซอร์ของคุณเมื่อคุณเยี่ยมชมเว็บไซต์"},howWeUse:{title:"วัตถุประสงค์ในการใช้คุกกี้ของเรา เป็นต้น",description:"เราอาจใช้คุกกี้ ฯลฯ เป็นหลักเพื่อวัตถุประสงค์ดังต่อไปนี้",items:["เพื่อทำให้การแสดงผลและการทำงานของเว็บไซต์มีความเสถียร","เพื่อตรวจจับและป้องกันการเข้าถึงโดยไม่ได้รับอนุญาตและการจราจรที่เป็นอันตราย","เพื่อทำความเข้าใจสถานะการเข้าถึงและเพื่อช่วยปรับปรุงเว็บไซต์"]},googleAnalytics:{title:"3. การใช้ Google Analytics",description:"เราใช้ Google Analytics 4 (GA4) ที่ให้บริการโดย Google LLC สำหรับการวิเคราะห์การเข้าถึง",items:["ข้อมูลที่เก็บรวบรวมอาจรวมถึงหน้าเว็บที่เข้าชม เวลาที่ใช้ แหล่งที่มาของการอ้างอิง ข้อมูลอุปกรณ์และเบราว์เซอร์ ฯลฯ","เราไม่ใช้ข้อมูลที่สามารถระบุตัวตนได้โดยตรง (ชื่อ, ที่อยู่อีเมล, เป็นต้น) เพื่อวัตถุประสงค์ในการส่งไปยัง Google Analytics.","การจัดการข้อมูลโดย Google อยู่ภายใต้ข้อกำหนดและนโยบายของ Google"]},cloudflare:{title:"4. การใช้ Cloudflare",description:"เราใช้บริการของบริษัท Cloudflare, Inc. สำหรับการส่งมอบและมาตรการรักษาความปลอดภัยของเว็บไซต์นี้ Cloudflare อาจตั้งค่าและอ้างอิงคุกกี้ ฯลฯ เพื่อวัตถุประสงค์เช่น การป้องกันการโจมตีแบบ DDoS และการป้องกันบอท",items:["วัตถุประสงค์หลักคือเพื่อเพิ่มประสิทธิภาพด้านความปลอดภัยและการสื่อสาร และไม่ได้มีเจตนาให้ติดตามเพื่อการแสดงโฆษณา","การจัดการข้อมูลของ Cloudflare อยู่ภายใต้ข้อกำหนดและนโยบายของ Cloudflare"]}

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243à¸±à¸šà¸à¸²à¸£à¹€à¸„ลื่อนไหวของลมและเมฆ ความรู้สึกของการ "อ่านข้อมูล ประเมิน และลงมือทำ" ที่ผมพัฒนาขึ้นผ่านกระบวนการซ้ำๆ นี้คือจุดเริ่มต้นสำหรับผมในวันนี้','ฉันเริ่มเรียนรู้การเขียนโปรแกรม Python ตอนอายุ 57 ปี แรงจูงใจเริ่มต้นมาจากความอยากรู้อยากเห็นธรรมดา แต่ทันทีที่ฉันเริ่มเขียนโค้ด ฉันก็หลงใหลในความเป็นไปได้ของตัวแทนปัญญาประดิษฐ์ ฉันสร้างแอปพลิเคชันด้วยมือของตัวเอง ใช้งาน และปรับปรุงมันในระหว่างกระบวนการนี้ ผมได้ตระหนักถึงบางสิ่งบางอย่าง ในโครงการที่ผ่านมา ผมมีส่วนร่วมในฐานะผู้ที่ไม่ใช่วิศวกร และการพัฒนาถูกทำโดยผู้อื่นเสมอ อย่างไรก็ตาม เมื่อผมเริ่มเขียนโค้ดด้วยตัวเอง ผมเริ่มมองเห็นแก่นแท้ของปัญหา "นี่คือจุดที่เป็นคอขวด" หรือ "การออกแบบนี้ไม่สามารถขยายได้" - ด้วยการมีมุมมองของวิศวกร ความเร็วในการตระหนักถึงสิ่งที่ผมต้องการทำเพิ่มขึ้นอย่างมาก',"การพัฒนาบริการด้านสภาพอากาศโดยการผสานประสบการณ์ภาคสนามกับวิทยาศาสตร์ข้อมูล การสร้างตัวแทนปัญญาประดิษฐ์เพื่อเพิ่มประสิทธิภาพการดำเนินงาน และโครงการทดลองต่าง ๆ เมื่อฉันยังคงทำงานด้วยมือของตัวเองต่อไป ฉันก็เริ่มเชื่อมั่นไม่เพียงแต่เทคโนโลยีนี้มีความน่าสนใจเท่านั้น แต่ยังต้องมีผู้คนที่ต้องการสิ่งนี้อย่างแน่นอน","เป้าหมายของเราไม่ใช่การไล่ตามเทคโนโลยีล้ำสมัย เราเผชิญกับปัญหาและงบประมาณของลูกค้า และส่งมอบเฉพาะสิ่งที่จำเป็นจริงในราคาที่สมเหตุสมผล เราไม่ใช่องค์กรขนาดใหญ่ ดังนั้นเราจึงสามารถให้การสนับสนุนการพัฒนาที่ปรับให้เหมาะกับความต้องการของลูกค้าแต่ละราย เราต้องการใช้เทคโนโลยีนี้เพื่อประโยชน์ของสังคม - นั่นคือเหตุผลที่เราเปิดตัว telet.ai"]},contact:{title:"เกี่ยวกับเรา",company:"telet.ai labo",ceo:"ผู้แทน: โอกุอิซูมิ เคตะ",button:"ติดต่อเรา"},studios:{tokyo:{title:"โตเกียว สตูดิโอ",address:"#301 เฟิร์ส ฮิลส์ เอ มูซาชิโคก

243าเนอิ, 3-3-21 นุกุอิมินามิโจ, โคกาเนอิ-ชิ, โตเกียว 184-0014, ญี่ปุ่น"}}},rA={title:"พอร์ตโฟลิโอ",subtitle:"ดูประวัติการดำเนินงานของเราในการใช้เทคโนโลยีปัญญาประดิษฐ์",techStackLabel:"เทคโนโลยีสแตกส์",viewProject:"ดูโครงการ",privateProject:"โครงการส่วนตัว",visualizationLabel:"การแสดงข้อมูล",cta:{title:"ติดต่อเรา",description:"กรุณาติดต่อเราเพื่อหารือเกี่ยวกับโครงการของคุณหรือสอบถามข้อมูลทางเทคนิคได้ตลอดเวลา",button:"ติดต่อเรา"},projects:{"traffic-route-ai":{title:"ระบบ AI เลือกเส้นทางการเดินทาง",subtitle:"วางแผนเส้นทางขนส่งสาธารณะในมาเลเซีย",description:"ระบบค้นหาเส้นทางที่ผสมผสานรถโดยสารและรถไฟจากจุดเริ่มต้นถึงปลายทาง พร้อมเปรียบเทียบเวลาเดินทาง เวลาถึง และจำนวนครั้งที่เปลี่ยนสาย รองรับตัวกรองเฉพาะรถโดยสารหรือรถไฟ การค้นหาสถานีและป้ายใกล้เคียง และหน้าจอหลายภาษา เพื่อช่วยเลือกเส้นทางสำหรับการเดินทางประจำวันและการท่องเที่ยว",techStack:["ค้นหาเส้นทางขนส่งสาธารณะ","เชื่อมต่อรถโดยสารและรถไฟ","เปรียบเทียบเส้นทาง","หน้าจอหลายภาษา"]},"multilingual-assistant":{title:"ผู้ช่วยหลายภาษาด้วยปัญญาประดิษฐ์",description:"ผู้ช่วยแชทอัจฉริยะสำหรับการสอบถามหลายภาษา สร้างดัชนีเวกเตอร์ 1,573 รายการ และควบคุมการตอบกลับอย่างแม่นยำผ่านการแยกสาขาตามเงื่อนไข การกรองคำ NG และการกระตุ้นด้วยคำสำคัญ คำถามของผู้ใช้ในหลายภาษา รวมถึงภาษาญี่ปุ่น ภาษาอังกฤษ และภาษาจีน จะได้รับการตอบกลับอย่างสม่ำเสมอและแม่นยำสูงผ่านการค้นหาความหมายข้ามภาษา",techStack:["RAG","การค้นหาแบบเวกเตอร์","การฝังตัวแบบหลายภาษา","LLM","ตรรกะการตัดสินใจแบบมีเงื่อนไข"]},"weather-forecast":{title:"การพยากรณ์อากาศด้วยระบบปัญญาประดิษฐ์ผ่าน Weather API",description:"พัฒนาระบบ AI ที่เรียนรู้จากข้อมูลสภาพอากาศในอดีตและทำการพยากรณ์สภาพอากาศล่าสุดแบบเรียลไทม์ LLM วิเคราะห์ข้อมูลในอดีตที่ได้จาก JMA API, OpenWeatherMap และแหล่งอื่น ๆ และสร้างการพยากรณ์ระยะสั้นสำหรับอุณหภูมิ ความน่าจะเป็นของฝน ความเร็วลม เป็นต้น การผสมผสานการจดจำรูปแบบของข้อมูลอนุกรมเวลาเข้ากับการค้นหาความคล้ายคลึงในอดีตโดยใช้ RAG ทำให้ระบบสามารถพยากรณ์ได้อย่างแม่นยำสูงซึ่งเสริมแบบจำลองทางสถิติแบบดั้งเดิม",techStack:["การเชื่อมโยง API สภาพอากาศ","LLM","RAG","การวิเคราะห์ข้อมูลอนุกรมเวลา","การประมวลผลแบบเรียลไทม์"]},"local-llm":{title:"เซิร์ฟเวอร์ภายใน / On-Prem Local LLM",description:"เราส่งมอบ AI ที่ปรับแต่งให้เหมาะกับกระบวนการทำงานของธุรกิจคุณ พร้อมใช้งานภายในบริษัทของคุณ ด้วย Open WebUI เป็นจุดเข้าใช้งานทั่วไป ช่องทางประมวลผลที่รวดเร็วด้วย Ollama/MLX และโดเมนข้อมูลที่แยกออกจากกันอย่างสมบูรณ์สำหรับแต่ละ UID เอกสารภายในองค์กรจะถูกบันทึกไว้ใน RAG พร้อมก

243ารฝังภาษาอังกฤษและการแปลคำถามภาษาญี่ปุ่นโดยอัตโนมัติ และทักษะทางธุรกิจ เช่น การสรุปข่าว การจัดระเบียบเอกสาร และการทำงานประจำ จะถูกดำเนินการอย่างปลอดภัยควบคู่กันไประบบการใช้งานทักษะถูกแยกออกเป็นคอนเทนเนอร์ OpenClaw และได้รับการปกป้องโดยชั้นป้องกันสามชั้นจาก Cloudflare edge และ Nginx ท้องถิ่น เราให้การสนับสนุนอย่างครอบคลุมสำหรับการเลือกแบบจำลอง, การออกแบบทักษะ, และนโยบายการดำเนินงาน และเริ่มต้นระบบ AI เฉพาะของบริษัทคุณในรูปแบบที่สามารถใช้งานได้ตั้งแต่วันที่ติดตั้ง",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"การคาดการณ์การมาถึงวงโคจรระหว่างดาวเคราะห์ (AI)",description:"พัฒนาระบบเพื่อวิเคราะห์จุดออกเดินทางที่เหมาะสมที่สุดสำหรับการเดินทางระหว่างดาวเคราะห์ โดยรับข้อมูลตำแหน่งดาวเคราะห์ องค์ประกอบวงโคจร และพารามิเตอร์แรงโน้มถ่วงจาก NASA API คำนวณวงโคจรโดยพิจารณาวงโคจรถ่ายโอนแบบ Hohmann และการใช้แรงโน้มถ่วงเพื่อช่วยในการเดินทาง โดยใช้ API และ Python และแสดงหน้าต่างการปล่อยจรวด เวลาที่ใช้ และประสิทธิภาพเชื้อเพลิง ระบบนี้ใช้เป็นเครื่องมือจำลองสำหรับสถาบันการศึกษาและบริษัทที่เกี่ยวข้องกับอวกาศ *ฟังก์ชัน AI ถูกนำออกในขณะนี้ การคำนวณกำลังดำเนินการด้วย API และ Python",techStack:["NASA API (Horizons, SPICE)","การคำนวณพลศาสตร์วงโคจร","ไพธอน","การแสดงข้อมูล"]},"real-estate":{title:"การวิเคราะห์ข้อมูลอสังหาริมทรัพย์ด้วยปัญญาประดิษฐ์",description:"พัฒนาระบบที่รวบรวมข้อมูลอสังหาริมทรัพย์สำหรับภูมิภาคเฉพาะผ่านการดึงข้อมูลจากเว็บไซต์และทำการวิเคราะห์ข้อมูลขั้นสูงโดยใช้ LLM และ Python ตัวแปรต่างๆ เช่น ราคาทรัพย์สิน ราคาต่อตารางฟุต อายุอาคาร และระยะทางจากสถานีรถไฟถูกดึงออกมา และทำการวิเคราะห์แนวโน้มราคา การคาดการณ์ตลาด และการจำลองผลตอบแทนจากการลงทุนโดยอัตโนมัติ นอกจากนี้ยังมีการดึงคุณลักษณะจากคำอธิบายทรัพย์สินที่ไม่มีโครงสร้างโดยใช้ LLM เพื่อให้การวิเคราะห์ตลาดที่ครอบคลุมร่วมกับข้อมูลเชิงปริมาณ*ข้อเสนอพร้อมให้บริการในเวอร์ชันที่สอง (เวอร์ชันเต็มให้บริการแยกต่างหาก)",techStack:["การดึงข้อมูลจากเว็บไซต์ (Scrapy, Playwright)","ไพธอน","แพนด้า","การวิเคราะห์ LLM","การแสดงข้อมูล"]},"fortune-ai":{title:"ระบบประเมินด้วยปัญญาประดิษฐ์สี่เสาหลัก",subtitle:"ถ่ายทอดภูมิปัญญาดั้งเดิมด้วยเทคโนโลยี",paragraphs:["หลักสี่เสาแห่งการพยากรณ์เป็นจุดสูงสุดของการพยากรณ์ตะวันออกที่สั่งสมมาหลายพันปี ระบบนี้ได้จำลองตรรกะการพยากรณ์แบบดั้งเดิมอย่างสมบูรณ์ในภาษาไพธอน ผสมผสานกับการตีความและการให้คำแนะนำโดยใช้ LLM (Large Language Models) ปัญญาประดิษฐ์นี้สามารถจำลองกระบวนการประเมินผลที่ดำเนินการโดยหมอดูผู้เชี่ยวชาญได้อย่างแม่นยำ","หลังจากป้อนวันที่ เวลา และสถานที่เกิด ระบบจะดึงข้อมูลเวลาพระอาทิตย์ขึ้นและพระอาทิตย์ตก รวมถึงข้อมูลพิกัด ณ เวลานั้นจาก API ด้านดาราศาสตร์ จากนั้นระบบจะคำนวณราศี เทพเจ้าทั้งสิบ และความสมดุลของธาตุทั้งห้าได้อย่างแม่นยำโดยอิงจากข้อมูลเหล่านี้ตรรกะที่อิงตามศาสตร์การพยากรณ์ของหลักโหราศาสตร์สี่เสาหลักของชะตากรรมถูกจัดทำดัชนีในภาษาไพธอนและรวมกับข้อมูลทางสถิติและข้อมูล RAG (Retrieval Augmentation Generation) เพื่อปรับปรุงความแม่นยำของการวิเคราะห์สูตรชีวิต","ข้อมูลไลฟ์สไตล์ที่คำนวณได้ถูกอธ

243ิบายด้วยภาษาธรรมชาติโดย LLM ในรูปแบบที่เข้าใจง่าย ผลการประเมินส่วนบุคคลจะถูกสร้างขึ้น รวมถึงกระแสโชคลาภ ความเข้ากันได้ของความสัมพันธ์ระหว่างบุคคล และอาชีพที่เหมาะสมสำหรับคุณ คุณสามารถรับคำตอบที่มีความลึกซึ้งเทียบเท่ากับการปรึกษาหมอดูมืออาชีพ","ขณะนี้สามารถใช้งานได้ในเวอร์ชันที่สอง คุณสามารถทดลองใช้การวิเคราะห์พื้นฐานของคูมิชิกิและการวิเคราะห์โชคชะตาได้ เวอร์ชันเต็มซึ่งรวมถึงการวิเคราะห์ที่ละเอียดมากขึ้นและคำแนะนำที่หลากหลายสามารถซื้อแยกต่างหากได้"],techStack:["ไพธอน","API ดาราศาสตร์ (พระอาทิตย์ขึ้น-พระอาทิตย์ตก, TimeZoneDB)","หลักตรรกะสี่เสา","LLM","การประสานงานและการคำนวณเวลา"]},"google-workspace":{title:"Google Workspace AI integration",description:"พัฒนาระบบที่เชื่อมโยง Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) กับ LLM เพื่ออ่าน แก้ไข และประมวลผลข้อมูลทางธุรกิจโดยอัตโนมัติ AI ช่วยงานประจำวัน เช่น สรุปเนื้อหาอีเมล สร้างร่างข้อความตอบกลับอัตโนมัติ ค้นหาและวิเคราะห์ไฟล์ใน Drive สกัดตารางเวลาจากปฏิทิน และเสนอการปรับเปลี่ยนที่เหมาะสม การทำงานของ Workspace ผ่าน Agent ยังสามารถทำได้ผ่านการเชื่อมต่อ API ที่ปลอดภัยด้วยการยืนยันตัวตนแบบ OAuth และการออกแบบเครื่องมือที่สอดคล้องกับมาตรฐาน MCPเวอร์ชันที่สองพร้อมใช้งานแล้ว (เวอร์ชันเต็มจะจัดเตรียมให้แยกต่างหาก)",techStack:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"]},"world-news":{title:"ข่าวโลก การแปลเป็นหลายภาษา",description:"พัฒนาระบบที่รวบรวมบทความจากแหล่งข่าวทั่วโลกแบบเรียลไทม์ และดำเนินการแปลภาษาหลายภาษา สรุปเนื้อหา และจัดหมวดหมู่โดยใช้ LLM แปลบทความต้นฉบับภาษาอังกฤษ à¸

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243scription:"ตรวจจับคำค้นหาที่เปลี่ยนแปลง, คำที่มีความหมายเหมือนกัน, และคำที่เกี่ยวข้องได้โดยอัตโนมัติโดยไม่ต้องตั้งค่าเอง"},{title:"การวิเคราะห์เชิงประวัติศาสตร์",description:"วิเคราะห์แนวโน้มของคำค้นหาตามเวลาเพื่อระบุรูปแบบตามฤดูกาลและหัวข้อที่กำลังเกิดขึ้น"},{title:"การแจ้งเตือนแบบเรียลไทม์",description:"รับการแจ้งเตือนทันทีเมื่อมีการกล่าวถึงคำสำคัญในบทสนทนาสด"}]}},fA={hero:{badges:["องค์กร","LLM ในท้องถิ่น","การดำเนินงานเซิร์ฟเวอร์ภายในองค์กร"],title:"การก่อสร้าง LLM ภายในองค์กร",description:"ดำเนินการ LLM ในสภาพแวดล้อมของคุณเอง โดยเก็บข้อมูลไว้ภายนอกบริษัท"},mission:{title:"ภาพรวมบริการก่อสร้าง LLM ภายในองค์กร",paragraphs:["สำหรับบริษัทที่ไม่สามารถเปิดเผยข้อมูลลับนอกองค์กรได้ เราสร้างสภาพแวดล้อม LLM ที่สามารถทำงานได้บนเซิร์ฟเวอร์ของตนเองหรือในคลาวด์ส่วนตัว โซลูชันนี้เหมาะอย่างยิ่งสำหรับองค์กรที่ต้องการการจัดการข้อมูลอย่างเข้มงวด เช่น สถาบันการเงิน หน่วยงานทางการแพทย์ หน่วยงานรัฐบาล และสถาบันวิจัย เนื่องจากไม่ใช้ API สาธารณะใดๆ และการประมวลผลทั้งหมดจะเสร็จสิ้นภายในโครงสร้างพื้นฐานที่ลูกค้าเป็นผู้จัดการเอง","เราให้การสนับสนุนอย่างครอบคลุมตั้งแต่การเลือกแบบจำลอง, การสร้างเซิร์ฟเวอร์, การปรับแต่งเครื่องมือการอนุมาน, และการตรวจสอบการดำเนินงาน โดยใช้แบบจำลองน้ำหนักเปิดที่มีจำหน่ายในเชิงพาณิชย์ เช่น Llama 3.3, Qwen 2.5, Mistral, และ DeepSeek พร้อมเครื่องมือการอนุมานประสิทธิภาพสูง เช่น vLLM, Ollama, และ SGLangโมเดลเหล่านี้ทำงานบนเอนจินการอนุมานประสิทธิภาพสูง เช่น vLLM, Ollama และ SGLang เพื่อให้สภาพแวดล้อมการอนุมานที่มีความหน่วงต่ำและประสิทธิภาพสูง","เราสนับสนุนการใช้งานที่หลากหลาย เช่น การผสานรวมกับระบบที่มีอยู่เดิม, แชทบอท, RAG ภายในองค์กร, การสรุปเอกสาร, และการสร้างโค้ด เราสามารถออกแบบให้ตรงตามความต้องการขององค์กร รวมถึงการปรับแต่งโมเดลให้เหมาะสมกับธุรกิจเฉพาะทาง, การเพิ่มประสิทธิภาพด้วย NVIDIA GPU / Apple Silicon, การออกแบบสำหรับหลายผู้ใช้งาน, การสร้าง API gateway และอื่นๆ อีกมากมาย"]},summary:{title:"แนวโน้มของ LLM ภายในองค์กร",paragraphs:["ในปี 2025-2026 ความเป็นประโยชน์ของ LLM ที่ติดตั้งภายในองค์กรกำลังพัฒนาอย่างรวดเร็ว ประสิทธิภาพของโมเดลแบบเปิดน้ำหนักได้เทียบเคียงกับโมเดลแบบปิด และจำนวนโมเดลที่มีใบอนุญาตเชิงพาณิชย์ได้ลดลงอย่างมาก ทำให้การดำเนินงานภายในองค์กรเป็นเรื่องง่ายขึ้น","โมเดลที่มีพารามิเตอร์ 8B ถึง 70B ได้ถึงระดับที่สามารถนำไปใช้ได้จริงสำหรับงานธุรกิจมากมาย การควอนไทซ์แบบ GGUF ช่วยให้การอนุมานได้รวดเร็วแม้บนการ์ดจอของผู้บริโภค","สำหรับเครื่องมือการอนุมาน, การจัดกลุ่มต่อเนื่อง (Continuous Batching) และการให้ความสนใจแบบแบ่งหน้า (PagedAttention) ของ vLLM ช่วยเพิ่มปริมาณงานได้อย่างมาก และการให้ความสนใจแบบฐานเลข (RadixAttention) ของ SGLang ช่วยเร่งการโต้ตอบหลายขั้นตอนที่ซับซ้อน Ollama ได้กลายเป็นมาตรฐานที่ใช้กันทั่วไปสำหรับสภาพแวดล้อมก

243ารพัฒนาในเครื่อง และ llama.cpp ได้รับการปรับให้เหมาะสมกับ Apple Silicon สำหรับการอนุมานที่รวดเร็วบน Mac ซีรีส์ M","ในการนำไปใช้ในองค์กร นอกเหนือจากอธิปไตยของข้อมูลและการปฏิบัติตามข้อกำหนดแล้ว การเพิ่มประสิทธิภาพต้นทุนในระยะยาว (การย้ายออกจากรูปแบบการเรียกเก็บเงิน API แบบจ่ายตามการใช้งาน) กำลังกลายเป็นแรงจูงใจในการนำไปใช้มากขึ้น การกำหนดค่าแบบไฮบริด (การประมวลผลข้อมูลที่ละเอียดอ่อนในเครื่องและ API บนคลาวด์สำหรับการประมวลผลทั่วไป) ได้กลายเป็นตัวเลือกที่พบได้บ่อย"],icons:[{label:"คลาวด์ส่วนตัว"},{label:"อธิปไตยของข้อมูล"},{label:"การเพิ่มประสิทธิภาพต้นทุน"},{label:"ความปลอดภัยขององค์กร"}]},features:{title:"คุณสมบัติการออกแบบและพัฒนา",subtitle:"การออกแบบและพัฒนา",items:[{title:"การออกแบบโครงสร้างพื้นฐานและการปรับแต่งประสิทธิภาพด้วย GPU",description:"ออกแบบการกำหนดค่าฮาร์ดแวร์ที่เหมาะสมที่สุดสำหรับปริมาณงานและงบประมาณของคุณ รวมถึง NVIDIA A100/H100, RTX 4090/5090 และ Apple Silicon (M2 Ultra/M3 Max/M4 Max) และสร้างสภาพแวดล้อมการอนุมานที่ใช้ประโยชน์จากประสิทธิภาพของ GPU ได้อย่างเต็มที่ผ่านการปรับแต่ง CUDA, MPS และ Metal การปรับแต่ง CUDA, MPS และ Metal เพื่อเพิ่มประสิทธิภาพ GPU ให้สูงสุด"},{title:"การเลือกแบบจำลองและการปรับให้เหมาะสม",description:"เลือกโมเดลที่เหมาะสมที่สุดจาก Llama, Qwen, Mistral, DeepSeek และอื่น ๆ โดยพิจารณาจากลักษณะของงาน ความต้องการด้านภาษา และข้อจำกัดของทรัพยากร ใช้การควอนไทซ์ (GGUF/AWQ/GPTQ) การปรับแต่งแคช KV และการปรับแต่งการประมวลผลแบบแบตช์เพื่อให้ได้ทั้งความแม่นยำและปริมาณงาน"},{title:"การสร้างเครื่องมือการอนุมาน",description:"เลือกและสร้างเครื่องมือการอนุมาน เช่น vLLM, SGLang, Ollama, TGI เป็นต้น ตามการใช้งานของคุณ และนำเทคโนโลยีล่าสุด เช่น Continuous Batching, Speculative Decoding, และ Prefix Caching มาใช้เพื่อให้ได้ API การอนุมานที่มีความหน่วงต่ำและประสิทธิภาพสูง เราให้บริการ API การอนุมานที่มีความหน่วงต่ำและประสิทธิภาพสูง"},{title:"การปฏิบัติการ การตรวจสอบ และการรักษาความปลอดภัย",description:"ดำเนินการตรวจสอบเมตริกเชิงอนุมานด้วย Prometheus/Grafana, การควบคุมการเข้าถึงด้วย API gateway, บันทึกการตรวจสอบ, การปรับขนาดอัตโนมัติ และการกู้คืนความล้มเหลว สร้างสภาพแวดล้อมก

243ารดำเนินงานสำหรับใช้งานจริงที่ตรงตามข้อกำหนดด้านความปลอดภัยขององค์กร"}]},techStack:{title:"เทคโนโลยีที่ใช้: LLM แบบติดตั้งภายในองค์กร",subtitle:"แพลตฟอร์มและเทคโนโลยี",categories:{openWeightModels:{title:"โมเดลน้ำหนักเปิด",items:["Llama 3.3 70B / 8B","Qwen 2.5 72B / 32B / 7B","Mistral Large / Nemo","DeepSeek V3 / R1","เจมม่า 2 27B / 9B","ฟี-4","คำสั่ง R (Cohere)"]},japaneseModels:{title:"รุ่นเฉพาะสำหรับประเทศญี่ปุ่น",items:["ELYZA-ญี่ปุ่น-ลามะ-2","Swallow 70B / 13B","ญี่ปุ่น-ลามะ-3","Qwen-2.5-Japanese","คาราคูริ-LM"]},inferenceEngine:{title:"เครื่องมือการอนุมาน",items:["vLLM (การจัดกลุ่มข้อมูลแบบต่อเนื่อง)","SGLang (RadixAttention)","โอลามา (การพัฒนาท้องถิ่น)","llama.cpp (Apple Silicon)","ทีจีไอ (ฮักกิ้ง เฟイス)","TensorRT-LLM (NVIDIA)","ExLlamaV2 (การปรับค่าเป็นระดับ)"]},quantization:{title:"การควอนไทซ์",items:["GGUF (มาตรฐาน llama.cpp)","AWQ (ความแม่นยำสูง 4 บิต)","GPTQ (การเพิ่มประสิทธิภาพ GPU)","บิตแอนด์ไบต์(8บิต/4บิต)","EETQ (การควอนไทซ์ประสิทธิภาพที่มีประสิทธิภาพ)"]},hardware:{title:"ฮาร์ดแวร์",items:["NVIDIA H100 / A100","NVIDIA L40S / A10G","RTX 4090 / 5090","Apple Silicon M2 Ultra","Apple Silicon M3/M4 Max","AMD MI300X"]}

243,infrastructure:{title:"โครงสร้างพื้นฐาน",items:["Docker / Kubernetes","NVIDIA Triton Server","เกตเวย์ API (Kong / Envoy)","Prometheus / Grafana","คลาวด์ส่วนตัว (VMware / OpenStack)","การสนับสนุนด้านสิ่งแวดล้อมแบบแยกขาดจากเครือข่าย"]}},updateNote:{title:"จุดอัปเดตหลักสำหรับปี 2026",description:"ในโมเดลน้ำหนักเปิด Llama 3.3 สามารถบรรลุประสิทธิภาพทั่วไปในระดับเดียวกับ GPT-4 และปรับปรุงประสิทธิภาพภาษาญี่ปุ่นอย่างมาก; ซีรีส์ Qwen 2.5 ขยายตัวเลือกด้วยขนาดที่หลากหลายและประสิทธิภาพที่สมดุล;DeepSeek V3/R1 ดึงดูดความสนใจด้วยประสิทธิภาพด้านต้นทุนการอนุมาน; และเอนจินการอนุมาน vLLM 2.0 Chunked Prefill และ RadixAttention ของ SGLang ช่วยเร่งความเร็วในการสนทนาหลายรอบในเอนจินการอนุมาน Chunked Prefill ของ vLLM 2.0 และ RadixAttention ของ SGLang ช่วยเพิ่มความเร็วในการโต้ตอบหลายรอบ การปรับแต่ง Apple Silicon ของ llama.cpp ทำให้การอนุมานโมเดลขนาด 70B เป็นไปได้จริงบน M4 Max และเทคโนโลยีการควอนไทซ์ให้ความแม่นยำระดับ 4 บิตและประสิทธิภาพสูง เทคนิคการควอนไทซ์สามารถบรรลุทั้งความแม่นยำระดับ 4 บิตและความเร็วในการอนุมาน และ GGUF กลายเป็นรูปแบบมาตรฐานโดยพฤตินัย"}},cta:{title:"ติดต่อเรา",description:"เราสามารถให้คำปรึกษาแก่คุณตั้งแต่การจัดเตรียมข้อกำหนด PoC และการกำหนดค่า LLM ท้องถิ่นก็สามารถพิจารณาได้เช่นกัน",primaryButton:"ติดต่อเรา",secondaryButton:"การให้คำปรึกษาทางเทคนิค"}},kA={hero:{badges:["องค์กร","RAG","การสร้างส่วนขยายการค้นหา"],title:"การพัฒนาเวกเตอร์ / RAG",description:"สร้างระบบ RAG เพื่อเชื่อมโยงความรู้ภายในกับ LLM"},mission:{title:"บริการพัฒนาเวกเตอร์ / RAG ภาพรวม",paragraphs:["เราจะสร้างระบบ RAG (ระบบสร้างการขยายการค้นหา) ที่ช่วยให้พนักงานสามารถค้นหาและถามคำถามในภาษาธรรมชาติได้โดยการเชื่อมโยงสินทรัพย์ความรู้ที่สะสมไว้ในบริษัทกับ LLM เราผสานรวมแหล่งข้อมูลต่าง ๆ เช่น คู่มือ, บันทึกการประชุม, เอกสารทางเทคนิค, คำถามที่พบบ่อย, ประวัติการสื่อสารใน Slack, Confluence, SharePoint, เป็นต้น เพื่อสร้างสภาพแวดล้อมที่ความรู้ขององค์กรสามารถนำมาใช้ได้ทันที","เราเพิ่มประสิทธิภาพความแม่นยำในการค้นหาและคุณภาพการตอบสนองโดยใช้เทคโนโลยีล่าสุด เช่น การค้นหาแบบไฮบริด (การค้นหาด้วยเวกเตอร์ + การค้นหาด้วยคำสำคัญ), การจัดอันดับใหม่, การแบ่งข้อมูลเป็นลำดับชั้น, การกรองข้อมูลเมตา, เป็นต้น นอกเหนือจากการค้นหาเอกสารแบบธรรมดา เราแสดงแหล่งที่มาของคำตอบอย่างชัดเจนเพื่อลดการเà¸

243à¸´à¸”ภาพหลอนและรับประกันความน่าเชื่อถือ","เราสนับสนุนสภาพแวดล้อมที่หลากหลาย รวมถึง AWS, GCP, Azure และระบบภายในองค์กร พร้อมทั้งผสานการทำงานกับโครงสร้างพื้นฐานการยืนยันตัวตนและการจัดการสิทธิ์ที่มีอยู่เดิม เราให้การสนับสนุนอย่างสม่ำเสมอในการรักษาความทันสมัยของข้อมูล (การอัปเดตแบบต่างส่วนและการจัดทำดัชนีใหม่โดยอัตโนมัติ) การวิเคราะห์บันทึกการใช้งาน และการปรับปรุงความแม่นยำอย่างต่อเนื่อง"]},summary:{title:"แนวโน้มเทคโนโลยี RAG",paragraphs:['ระหว่างปี 2025 ถึง 2026 เทคโนโลยี RAG กำลังก้าวเข้าสู่ระยะ "การนำไปใช้พื้นฐานสู่คุณภาพการผลิต" นอกเหนือจากการค้นหาแบบเวกเตอร์อย่างง่ายแล้ว การค้นหาแบบไฮบริด ซึ่งรวมวิธีการค้นหาหลายวิธีเข้าด้วยกัน การจัดอันดับใหม่ ซึ่งจัดลำดับผลการค้นหาใหม่ และการแยกคำถาม ซึ่งแยกและประมวลผลคำถามที่ซับซ้อน ได้กลายเป็นส่วนประกอบมาตรฐาน',"กลยุทธ์การแบ่งข้อมูลออกเป็นส่วน ๆ ก็ได้พัฒนาขึ้นเช่นกัน โดยมีการแบ่งข้อมูลแบบลำดับชั้นที่คำนึงถึงโครงสร้างของเอกสาร การแบ่งข้อมูลแบบซ้อนทับ และการแบ่งข้อมูลแบบหลายรูปแบบที่ประมวลผลตารางและรูปภาพแยกกัน ซึ่งทั้งหมดนี้ช่วยเพิ่มความแม่นยำ การค้นหาตามบริบท (การเพิ่มข้อมูลบริบทให้กับส่วนข้อมูล) ก็ได้รับการยอมรับอย่างกว้างขวางว่ามีประสิทธิภาพเช่นกัน","สำหรับการเลือกฐานข้อมูลเวกเตอร์ นอกเหนือจากบริการที่มีการจัดการ เช่น Pinecone, Weaviate, Qdrant และ Milvus แล้ว PostgreSQL pgvector และ FAISS ในเครื่องยังเสนอตัวเลือกที่หลากหลายเพื่อตอบสนองความต้องการของคุณ นอกจากนี้ยังมีโมเดลฝังตัวให้เลือกใช้ รวมถึง text-Embedding-3-large, Cohere Embed v3 และโมเดลหลายภาษา",'ในการนำไปใช้ในองค์กร การเชื่อมโยงการจัดการสิทธิ์ (ACL) การปิดบังข้อมูลส่วนบุคคล (PII) บันทึกการตรวจสอบ และการออกแบบแบบหลายผู้เช่า เป็นข้อกำหนดที่จำเป็น และมีความจำเป็นต้องย้ายจาก "PoC ที่ใช้งานได้" ไปสู่ "ระบบที่พร้อมใช้งานจริง"'],icons:[{label:"การค้นหาแบบผสมผสาน"},{label:"ระบบตรวจสอบข้อมูลด้วยหลายรูปแบบ"},{label:"เอเจนติก RAG"},{label:"ความปลอดภัยขององค์กร"}]},features:{title:"คุณสมบัติการออกแบบและพัฒนา",subtitle:"การออกแบบและพัฒนา",items:[{title:"การรวบรวมข้อมูลและกระบวนการเตรียมข้อมูลก่อนการประมวลผล",description:"การดึงข้อมูลและการทำความสะอาดข้อมูลจาก PDF, Word, Excel, HTML, Markdown, Slack, Confluence, SharePoint, เป็นต้น รองรับหลายรูปแบบ รวมถึง OCR, การวิเคราะห์โครงสร้างตาราง, และการสร้างคำบรรยายภาพ รักษาความสดใหม่ด้วยการอัปเดตแบบต่างส่วนและการดำเนินการตามกำหนดเวลา"},{title:"การออกแบบระบบค้นหาความแม่นยำสูง",description:"สร้างการค้นหาความแม่นยำสูงที่ผสมผสานการค้นหาแบบเวกเตอร์ + การค้นหาแบบไฮบริด BM25, การจัดอันดับใหม่โดย Cohere Rerank / Cross-Encoder และการขยายและแยกคำค้นหา การกรองเมตาดาต้าและการควบคุมการเข้าถึงตามสิทธิ์ก็ได้รับการดำเนินการเช่นกัน"},{title:"การสร้างคำตอบและการควบคุมคุณภาพ",description:"ปรับปรุงคุณภาพของคำตอบด้วยการปรับบริบทให้เหมาะสม, คำสั่งแบบ Few-shot, และการอนุมานแบบ Chain-of-Thought. การชี้แจงแหล่งที่มา, การแสดงคะแนนความมั่นใจ, และการตรวจจับการเà¸

243à¸´à¸”ภาพหลอนถูกนำมาใช้เพื่อให้มั่นใจในความน่าเชื่อถือสำหรับการใช้งานทางธุรกิจ."},{title:"วงจรการดำเนินงานและการปรับปรุง",description:"เราให้บริการสนับสนุนการดำเนินงาน รวมถึงการวิเคราะห์บันทึกการใช้งาน อัตราการค้นหาที่ตรงเป้าหมาย และการติดตามความพึงพอใจในการตอบสนอง การปรับปรุงอย่างต่อเนื่องผ่านวงจรข้อเสนอแนะ การทดสอบ A/B โครงสร้างพื้นฐาน การฝังการอัปเดตโมเดล และการทบทวนกลยุทธ์การแบ่งข้อมูล"}]},techStack:{title:"เทคโนโลยีที่ใช้: เวกเตอร์ / RAG",subtitle:"แพลตฟอร์มและเทคโนโลยี",categories:{embeddingModels:{title:"การฝังแบบจำลอง",items:["OpenAI text-embedding-3-large","Cohere Embed v3","การเดินทางด้วยปัญญาประดิษฐ์","จินา เอมเบดดิ้งส์ v3","BGE-M3 (หลายภาษา)","มัลติไลอnguage-e5-ขนาดใหญ่","รุ่นเฉพาะสำหรับประเทศญี่ปุ่น"]},vectorDatabases:{title:"ฐานข้อมูลเวกเตอร์",items:["โคนสน (จัดการ)","Weaviate (การค้นหาแบบไฮบริด)","Qdrant (ประสิทธิภาพสูง)","มิลวุส (ขนาดใหญ่)","โครมา (น้ำหนักเบา)","PostgreSQL pgvector","FAISS (ท้องถิ่น)"]},chunking:{title:"การแบ่งข้อมูลเป็นชิ้นและการประมวลผล",items:["การจัดกลุ่มแบบลำดับชั้น","การแบ่งกลุ่มเชิงความหมาย","ชิ้นส่วนที่ซ้อนทับกัน","หน้าต่างประโยค","การเรียกคืนตามบริบท","การประมวลผลตารางและแผนภูมิ","การสนับสนุนแบบหลายช่องทาง"]},search:{title:"การค้นหาและการเรียกคืน",items:["การค้นหาแบบไฮบริด (เวกเตอร์ + BM25)","การจัดอันดับใหม่ของ Cohere","การจัดอันดับใหม่แบบข้ามตัวเข้ารหัส","การขยายและการแยกย่อยคำถาม","HyDE (การสร้างเอกสารสมมติ)","การสืบค้นด้วยตนเอง","การค้นหาหลายคำสั่ง"]}},updateNote:{title:"จุดอัปเดตหลักสำหรับปี 2026",description:"ในโมเดล Embedding, text-Embedding-3-large และ Cohere Embed v3 ได้กลายเป็นมาตรฐานสำหรับองค์กร และประสิทธิภาพในการประมวลผลภาษาญี่ปุ่นได้ปรับปรุงอย่างมีนัยสำคัญ; การค้นหาตามบริบทได้พิสูจน์แล้วว่ามีประสิทธิภาพ และการเพิ่มบริบทให้กับชิ้นส่วนข้อมูลได้กลายเป็นวิธีการมาตรฐานการค้นหาแบบไฮบริด + การจัดอันดับใหม่ได้กลายเป็นค่ากำหนดที่จำเป็นสำหรับการค้นหา ลดจำนวนการกำหนดค่าที่ใช้การค้นหาเวกเตอร์แบบง่ายเพียงอย่างเดียว และ Agentic RAG (ตัวแทนที่เลือกกลยุทธ์การค้นหาแบบไดนามิก) กำลังถูกนำไปใช้ในทางปฏิบัติ ในด้านการดำเนินงาน มีการเน้นย้ำในการสร้างวงจรการวัดคุณภาพและการปรับปรุงโดยใช้กรอบการประเมิน RAG (RAGAS, LangSmith)"}},cta:{title:"ติดต่อเรา",description:"เราสามารถให้คำปรึกษาแก่คุณตั้งแต่การจัดเตรียมข้อกำหนด PoC และการกำหนดค่า LLM ท้องถิ่นก็

243สามารถพิจารณาได้เช่นกัน",primaryButton:"ติดต่อเรา",secondaryButton:"การให้คำปรึกษาทางเทคนิค"}},bA={hero:{badges:["ฟูลสแต็ก","การบูรณาการ LLM","การออกแบบ API"],title:"การพัฒนาแอปพลิเคชันปัญญาประดิษฐ์",description:"ออกแบบและสร้างแอปพลิเคชันที่ใช้งานได้จริงโดยใช้ประโยชน์จาก LLM"},mission:{title:"บริการพัฒนาแอปพลิเคชันปัญญาประดิษฐ์",paragraphs:["เราให้บริการออกแบบและพัฒนาแอปพลิเคชัน AI ที่สร้างคุณค่าทางธุรกิจโดยการวิเคราะห์ข้อมูลที่ได้จาก API ภายนอกด้วย LLM เชื่อมต่อ API กับแหล่งข้อมูลที่หลากหลาย เช่น CRM, ERP, SaaS, เซ็นเซอร์ IoT และโซเชียลมีเดีย และป้อนข้อมูลที่ได้เข้าสู่ LLM แบบเรียลไทม์ เราช่วยให้สามารถวิเคราะห์ข้อมูลขั้นสูงที่ไม่สามารถทำได้ด้วยเครื่องมือ BI แบบดั้งเดิม เช่น การสร้างรายงานวิเคราะห์โดยใช้ภาษาธรรมชาติ การตรวจจับความผิดปกติ การทำนายแนวโน้ม และการจำแนกประเภทอัตโนมัติ","ไม่เพียงแต่การผสานระบบ API อย่างง่าย แต่ยังรวมถึงการผสานระบบจากแหล่งข้อมูลหลายแหล่ง การทำความสะอาดข้อมูล และการแปลงข้อมูลให้เป็นรูปแบบที่ง่ายต่อการตีความของ LLM ผ่านการประมวลผลที่มีโครงสร้าง การออกแบบคำสั่ง (Prompt Engineering) ช่วยเพิ่มประสิทธิภาพการวิเคราะห์ให้สูงสุด และสร้างผลลัพธ์ที่เหมาะกับความต้องการทางธุรกิจ เราสร้างแอปพลิเคชันที่สามารถนำไปใช้ได้จริงซึ่งผสานข้อมูลกับปัญญาประดิษฐ์ เช่น ข้อมูลการขายเพื่อทำนายการเจรจาทางธุรกิจ บันทึกการสนับสนุนลูกค้าเพื่อวิเคราะห์เสียงของลูกค้า (VoC) และข้อมูลจากเซ็นเซอร์เพื่อให้คำแนะนำการบำรุงรักษาอุปกรณ์","เราสนับสนุนหลากหลายรูปแบบการใช้งาน เช่น เว็บแอปพลิเคชัน, แอปพลิเคชันบนมือถือ, การเชื่อมต่อกับ Slack/Teams และการประมวลผลแบบแบทช์เป็นระยะ เราให้การสนับสนุนอย่างสม่ำเสมอตั้งแต่การกำหนดความต้องการ การออกแบบ API การผสานรวม LLM ไปจนถึงการดำเนินงานในระบบจริง"]},summary:{title:"แนวโน้มการพัฒนาแอปพลิเคชันปัญญาประดิษฐ์",paragraphs:['ตั้งแต่ปี 2025 ถึง 2026 การพัฒนาแอปพลิเคชัน AI กำลังเข้าสู่ระยะปฏิบัติจริงโดยอาศัย "การผสาน API x การวิเคราะห์ LLM" การวิเคราะห์ข้อมูลที่ไม่มีโครงสร้างและการสร้างข้อมูลเชิงลึกจากภาษาธรรมชาติ ซึ่งไม่สามารถจัดการได้ด้วยเครื่องมือ BI แบบดั้งเดิมและแดชบอร์ด กำลังเริ่มถูกนำมาใช้ในระบบขององค์กร',"สิ่งที่ควรสังเกตเป็นพิเศษคือการพัฒนาของฟังก์ชันการทำงานแบบผลลัพธ์ที่มีโครงสร้าง (Structured Output) ของ LLM ซึ่งช่วยให้สามารถควบคุมผลลัพธ์ให้สอดคล้องกับ JSON Schema อย่างเคร่งครัด ทำให้ระบบการทำงานแบบอัตโนมัติตั้งแต่ต้นจนจบเป็นจริงได้ โดยที่ข้อมูลที่ได้มาจาก API จะถูกวิเคราะห์โดย LLM และผลลัพธ์จะถูกส่งต่อไปยังระบบต่อไปโดยตรง การดำเนินการแบบหลายขั้นตอนของการเรียกฟังก์ชัน (Function Calling) ช่วยให้การรวบรวมข้อมูล การผสานข้อมูล และการวิเคราะห์ข้อมูลผ่าน API หลายตัวสามารถทำได้ในกระบวนการทำงานเดียว","ในโครงสร้างพื้นฐานการประมวลผลข้อมูล Polars ได้กลายเป็นมาตรฐานสำหรับการประมวลผลความเร็วสูง และเมื่อรวมกับการประมวลผลแบบสตรีมมิ่ง จะช่วยอำนวยความสะดวกในการวิเคราะห์แบบเรียลไทม์","ในการนำไปใช้ในองค์กร ความต้องการได้เปลี่ยนจากการพัฒนา PoC แบบครั้งเดียวเป็นการออกแบบที่สามารถปรับขนาดได้สำหรับการดำเนินงานในเชิงการผลิต ซึ่งต้องการการออกแบบสถาปัตยกรรมที่ครอบคลุมซึ่งรวมถึงการเพิ่มประสิทธิภาพด้านต้นทุน การจัดการข้อผิดพลาด และการสนับสนุนบันทึกการตรวจสอบ"],icons:[{label:"เอเจนติก เอไอ"},{label:"โปรโตคอล MCP"},{label:"หลายตัวแทน"},{label:"มนุษย์ในห่วงโซ่"}]},features:{title:"คุณสมบัติการออกแบบและพัฒนา",subtitle:"การออกแบบและพัฒนา",items:[{title:"การออกแบบการรวม API และการเà¸

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243k keselamatan dan pengoptimuman komunikasi, dan tidak dimaksudkan untuk dijejaki bagi penghantaran iklan.","Penyeliaan data oleh Cloudflare tertakluk kepada terma dan dasar Cloudflare."]},noSpecialCookies:{title:"5. Kuki istimewa kami sendiri",description:"Kami tidak menetapkan kuki khusus kami sendiri untuk tujuan pengiklanan penargetan tingkah laku dan sebagainya. Penggunaan kuki dan sebagainya terhad terutamanya kepada skop operasi laman, keselamatan, dan analisis capaian."},choices:{title:"6. Pengurusan kuki (pencabutan)",description:"Jika kuki dilumpuhkan, beberapa fungsi mungkin tidak tersedia.",items:["Kuki boleh dipadam atau dilumpuhkan dalam tetapan pelayar.","Jika anda ingin menyahdayakan pengukuran oleh Google Analytics, sila gunakan kaedah opt-out yang disediakan oleh Google.","Penggunaan mod peribadi pelayar dan sebagainya boleh mengehadkan maklumat yang disimpan."]},changes:{title:"7. Perubahan kepada dasar ini",description:"Kami mungkin menyemak semula dasar ini sebagai tindak balas kepada pindaan undang-undang dan peraturan atau perubahan dalam perkhidmatan yang digunakan. Dasar yang disemak semula akan berkuat kuasa apabila ia disiarkan di halaman ini."},contact:{title:"8. hubungi kami",description:"Jika anda mempunyai sebarang pertanyaan mengenai dasar ini, sila hubungi kami di",company:"telet.ai",contactLink:"Pergi ke Borang Pertanyaan"}}},DA={hero:{title:"Tentang telet.ai",subtitle:"Sokongan pembangunan AI yang berhati-hati hanya boleh disediakan oleh pasukan kecil yang diperibadikan."},story:{title:"Tentang kami",content:"telet.ai bermula dengan satu persoalan. Mengapa begitu banyak syarikat gagal memanfaatkan wawasan yang tersimpan dalam interaksi tanpa had antara syarikat dan pelanggan mereka? Pasukan ini, yang diketuai oleh seorang wakil dengan pengalaman dalam penyelidikan AI dan pembangunan perisian perusahaan, ditubuhkan untuk menangani isu ini secara langsung. Kami bukan sebuah organisasi besar. Itulah sebabnya kami dapat menangani cabaran anda secara mendalam dan mencadangkan hanya penyelesaian yang benar-benar diperlukan. Kekuatan kami ialah keupayaan kami untuk mereka bentuk seni bina hibrid yang menggabungkan LLM tempatan dan LLM awan. Kami dapat melindungi data sensitif syarikat anda dalam persekitaran awan khusus sambil menjalankan inferens AI lanjutan secara berskala. Kami menyediakan CLI untuk pembangun dan UI sembang untuk kakitangan barisan hadapan, mewujudkan asas untuk penggunaan AI di seluruh organisasi anda. Dan kami menumpukan pada menjadi penyelesaian yang optimum dan bukannya yang paling canggih. Menggugurkan fungsi mengikut bajet dan menyemak hanya satu konfigurasi boleh membuat perbezaan besar dari segi kos. Kami menyampaikan apa yang anda perlukan, dalam kuantiti yang anda perlukan. Sokongan pembangunan AI kami yang teliti hanya mungkin kerana kami kecil dan peribadi. Untuk menyelesaikan kekurangan tenaga kerja dan menyokong pertumbuhan syarikat anda. Itulah janji telet.ai."},representative:{title:"Wakil telet.ai labo",name:"Keita Okuizumi",tagline:"Kepada langit, kepada laut, dan kepada kod - semangat pembangunan AI yang lahir daripada kerjaya yang luar biasa",paragraphs:['Sebagai bekas juruterbang persendirian (PPL) dan pemegang lesen kapal, saya telah lama berurusan dengan cuaca di udara dan di laut. Di kokpit dan bilik kemudi, saya memerhati perubahan pada barometer, mentafsir garis tekanan isobar, dan membandingkannya dengan pergerakan angin dan awan. Rasa "membaca data, menilai, dan bertindak" yang saya kembangkan melalui proses berulang ini menjadi titik permulaan bagi saya hari ini.','Saya mula belajar pengaturcaraan Python pada usia 57 tahun. Dorongannya hanyalah rasa ingin tahu semata-mata, tetapi sebaik sahaja saya mula menulis kod, saya terpesona dengan kemungkinan ejen AI. Saya mencipta aplikasi dengan tangan sendiri, menjalankannya, dan memperbaikinya. Dalam proses itu, saya sedar sesuatu. Dalam projek-projek sebelum ini, saya terlibat sebagai bukan jurutera, dan pembangunan sentiasa dilakukan oleh orang lain. Namun, sebaik sahaja saya mula menulis kod sendiri, saya mula melihat intipati masalah. "Ini adalah titik sempit di sini" atau "Reka bentuk ini tidak boleh diperluas" - dengan mempunyai sudut pandang seorang jurutera, kelajuan untuk merealisasikan apa yang saya ingin lakukan meningkat dengan ketara.',"Membangunkan perkhidmatan cuaca dengan menggabungkan pengalaman lapangan dan sains data, membina ejen AI untuk meningkatkan kecekapan operasi, dan pelbagai projek eksperimen. Semakin saya terus bekerja dengan tangan, saya semakin yakin bukan sahaja bahawa teknologi ini menarik, tetapi juga bahawa pasti ada orang yang memerlukan ini.","Matlamat kami bukan mengejar teknologi tercanggih. Kami menangani isu dan bajet pelanggan, dan hanya menyampaikan apa yang benar-benar diperlukan pada kos yang munasabah. Kami bukan organisasi besar, jadi kami dapat menyediakan sokongan pembangunan yang disesuaikan dengan keperluan setiap pelanggan. Kami ingin menggunakan teknologi ini untuk manfaat masyarakat – itulah sebabnya kami melancarkan telet.ai."]},contact:{title:"Maklumat Organisasi",company:"telet.ai labo",ceo:"Wakil: OKUIZUMI Keita",button:"Hubungi Kami"},studios:{tokyo:{title:"Studio Tokyo",address:"#301 First Hills A Musashikoganei, 3-3-21 Nukuiminamicho, Koganei-shi, Tokyo 184-0014, Jepun"}}},zA={title:"Portfolio",subtitle:"Lihat rekod prestasi kami dalam memanfaatkan teknologi AI",techStackLabel:"Susunan Teknologi",viewProject:"Lihat Projek",privateProject:"Projek Swasta",visualizationLabel:"visualisasi data",cta:{title:"Hubungi Kami",description:"Sila hubungi kami untuk membincangkan projek anda atau untuk pertanyaan teknikal.",button:"Hubungi Kami"},projects:{"traffic-route-ai":{title:"Sistem Pemilihan Laluan Pengangkutan AI",subtitle:"Perancangan laluan pengangkutan awam di Malaysia",description:"Sistem yang mencari gabungan laluan bas dan kereta api antara tempat bertolak dan destinasi serta membandingkan pilihan mengikut waktu ketibaan, tempoh perjalanan dan bilangan pertukaran. Penapis bas sahaja atau kereta api sahaja, carian stesen dan hentian berdekatan serta antara muka berbilang bahasa membantu pemilihan laluan untuk perjalanan harian dan percutian.",techStack:["Laluan Pengangkutan Awam","Integrasi Bas dan Kereta Api","Perbandingan Laluan","Antara Muka Berbilang Bahasa"]},"multilingual-assistant":{title:"Pembantu Pelbagai Bahasa AI",description:"Penolong sembang pintar untuk pertanyaan berbilang bahasa, membina 1,573 indeks vektor dan melaksanakan kawalan jawapan tepat melalui percabangan bersyarat, penapisan kata NG, dan pencetus kata kunci. Jawapan yang konsisten dan sangat tepat kepada soalan pengguna dalam pelbagai bahasa, termasuk Jepun, Inggeris, dan Cina, dijana melalui carian semantik merentas bahasa.",techStack:["RAG","Carian Vektor","Penyisipan Pelbagai Bahasa","LLM","logik cawangan bersyarat"]},"weather-forecast":{title:"Peramalan Cuaca AI dengan API Cuaca",description:"Membangunkan sistem AI yang belajar daripada data cuaca lalu dan membuat ramalan masa nyata mengenai keadaan cuaca terkini. LLM menganalisis data lalu yang diperoleh daripada JMA API, OpenWeatherMap dan lain-lain, dan menjana ramalan 

243jangka pendek untuk suhu, kebarangkalian hujan, kelajuan angin dan lain-lain. Dengan menggabungkan pengecaman corak data siri masa dan carian corak keserupaan lalu melalui RAG, sistem ini mencapai ramalan yang sangat tepat yang melengkapi model statistik konvensional.",techStack:["Pautan API Cuaca","LLM","RAG","Analisis Data Siri Masa","pemprosesan masa nyata"]},"local-llm":{title:"Pelayan Dalaman / On-Prem Local LLM",description:"Kami menyediakan AI yang disesuaikan dengan operasi perniagaan anda, sedia untuk digunakan dalam syarikat anda, dengan Open WebUI sebagai titik kemasukan biasa, laluan inferens pantas dengan Ollama/MLX, dan domain data yang dipisahkan sepenuhnya untuk setiap UID. Dokumen dalaman dirakam dalam RAG dengan pemadatan Bahasa Inggeris dan terjemahan pertanyaan Jepun secara automatik, dan kemahiran perniagaan seperti ringkasan berita, pengaturan dokumen, dan tugas rutin dijalankan dengan selamat secara serentak. Sistem pelaksanaan kemahiran diasingkan sebagai kontena OpenClaw dan dilindungi oleh tiga lapisan edge Cloudflare serta Nginx tempatan. Kami menyediakan sokongan menyeluruh untuk pemilihan model, reka bentuk kemahiran, dan dasar operasi, dan memulakan AI khusus syarikat anda dalam bentuk yang boleh digunakan sejak hari ia dikerahkan.",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"Ramalan Ketibaan Orbit Antarpanah (AI)",description:"Membangunkan sistem untuk menganalisis titik pelepasan optimum bagi pemindahan antara planet dengan mendapatkan data kedudukan planet, elemen orbit, dan parameter graviti daripada NASA API. Mengira pengiraan orbit dengan mengambil kira orbit pemindahan Hohmann dan bantuan graviti menggunakan API dan Python, serta memaparkan tetingkap pelancaran, masa yang diperlukan, dan kecekapan bahan api. Sistem ini digunakan sebagai alat simulasi untuk institusi pendidikan dan syarikat berkaitan angkasa lepas. *Fungsi AI pada masa ini telah dialih keluar, pengiraan sedang dijalankan dengan API dan Python.",techStack:["NASA API (Horizons, SPICE)","Pengiraan Dinamik Orbit","Python","visualisasi data"]},"real-estate":{title:"Analisis AI Data Hartanah",description:"Membangunkan sistem yang mengumpul maklumat hartanah untuk wilayah tertentu melalui pengikisan web dan menjalankan analisis data lanjutan menggunakan LLM dan Python. Pembolehubah seperti harga hartanah, harga unit tsubo, umur bangunan, dan jarak stesen diekstrak, dan analisis trend harga, ramalan pasaran, serta simulasi pulangan pelaburan dijana secara automatik. Ciri-ciri juga diekstrak daripada keterangan hartanah tidak berstruktur menggunakan LLM untuk menyediakan analisis pasaran menyeluruh yang digabungkan dengan data kuantitatif. *Tawaran kini tersedia sebagai versi kedua (versi PENUH disediakan secara berasingan)",techStack:["Pengikisan web (Scrapy, Playwright)","Python","Beruang","Analisis LLM","visualisasi data"]},"fortune-ai":{title:"Sistem Penilaian AI Empat Pilar",subtitle:"Menghasilkan semula kebijaksanaan tradisional dengan teknologi.",paragraphs:["Empat Pilar Peramalan adalah kemuncak beribu-ribu tahun peramalan Timur. Sistem ini menghasilkan semula logik peramalan tradisionalnya sepenuhnya dalam Python, digabungkan dengan tafsiran dan penjanaan nasihat menggunakan LLM (Model Bahasa Besar). AI ini menghasilkan semula dengan setia proses penilaian yang dijalankan oleh peramal pakar.","Selepas memasukkan tarikh, masa, dan tempat kelahiran, sistem mendapatkan masa matahari terbit dan terbenam serta maklumat koordinat pada waktu tersebut daripada Astronomy API. Berdasarkan data ini, sistem mengira dengan tepat tanda zodiak, sepuluh dewa, dan keseimbangan lima unsur. Logik berdasarkan sains peramalan Empat Tiang Takdir diindeks dalam Python dan digabungkan dengan data statistik serta maklumat RAG (Retrieval Augmentation Generation) untuk meningkatkan ketepatan analisis formula kehidupan.","Data gaya hidup yang dikira dijelaskan dalam bahasa semula jadi oleh LLM dengan cara yang mudah difahami. Keputusan penilaian peribadi dihasilkan, termasuk aliran nasib, keserasian hubungan interpersonal, dan pekerjaan yang sesuai untuk anda. Anda boleh mendapatkan jawapan dengan kedalaman yang sama seperti apabila berunding dengan peramal profesional.","Ia kini tersedia sebagai versi kedua. Anda boleh mencuba analisis kumishiki asas dan analisis nasib. Versi penuh, yang merangkumi analisis yang lebih terperinci dan nasihat pelbagai aspek, tersedia secara berasingan."],techStack:["Python","API Astronomi (Terbit-Terbenam, Pangkalan Data Zon Waktu)","Logik Empat Tiang","LLM","Pengkoordinasian dan pengiraan masa"]}

243,"google-workspace":{title:"Google Workspace AI integration",description:"Membangunkan sistem yang menghubungkan Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) dengan LLM untuk membaca, mengedit, dan memproses data perniagaan secara automatik. AI membantu dengan tugasan harian seperti meringkaskan kandungan e-mel, menjana draf untuk balasan automatik, mencari dan menganalisis fail Drive, mengekstrak jadual daripada Calendar, dan mencadangkan penyesuaian. Operasi Workspace oleh Agent juga direalisasikan melalui sambungan API selamat dengan pengesahan OAuth dan reka bentuk alat yang mematuhi MCP. Versi kedua kini tersedia (Versi PENUH akan disediakan secara berasingan).",techStack:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"]},"world-news":{title:"Berita Dunia Pelbagai Bahasa",description:"Pembangunan sistem yang mengumpul artikel secara masa nyata daripada sumber berita di seluruh dunia dan menjalankan terjemahan pelbagai bahasa, penyusunan ringkas, dan pengkategorian menggunakan LLM. Menyelaras artikel asal dalam Bahasa Inggeris, Cina, Perancis, Arab, dan bahasa lain ke dalam Bahasa Jepun serta menyusunnya mengikut topik. Pemantauan berita antarabangsa yang cekap dicapai bukan melalui terjemahan mesin ringkas, tetapi melalui terjemahan semula jadi berdasarkan pemahaman konteks menggunakan LLM dan penjanaan ringkasan yang mengekstrak isi utama artikel. *Tawaran kini tersedia sebagai versi kedua (versi PENUH disediakan secara berasingan).",techStack:["API Berita","Koleksi RSS","Terjemahan dan Ringkasan LLM","pemprosesan pelbagai bahasa","Pengkategorian automatik","pemprosesan kelompok terjadual"]},"security-monitoring":{title:"Pengurus AI Pemantauan Log Keselamatan",description:"Membangunkan sistem untuk pemantauan log keselamatan korporat secara masa nyata dan pengesanan serta analisis ancaman lanjutan menggunakan LLM. Pelbagai sumber log seperti firewall, IDS/IPS, log pengesahan, dan log aplikasi dikumpul dan dianalisis oleh modul Python, dan dibandingkan dengan corak serangan lampau, maklumat kerentanan, dan kes insiden yang terkumpul dalam RAG. Apabila corak tidak normal dikesan, LLM menerangkan tahap ancaman, skop impak, dan tindak balas yang disyorkan dalam bahasa semula jadi, dan segera memberi amaran kepada pihak yang bertanggungjawab. Jika corak yang sama berulang, amaran keutamaan tinggi akan dikeluarkan secara automatik untuk mempercepat tindak balas insiden.",techStack:["Modul Pemantauan Python","Pengumpulan log (Fluentd, Logstash)","RAG","Corak Ancaman DB","Analisis LLM","Notifikasi amaran (Slack, PagerDuty)"]}}},TA={learnMore:"Baca lagi",getStarted:"mula untuk ...",contactUs:"Hubungi Kami",backToHome:"Kembali ke Laman Utama"},EA={title:"Dasar Privasi",subtitle:"Dasar Privasi",lastUpdated:"Kemas kini terakhir: 23 Januari 2026",intro:'telet.ai (the "Service") menganggap perlindungan maklumat peribadi anda sebagai tanggungjawab penting dan telah menetapkan dasar privasi berikut.',sections:{collection:{title:"1. maklumat yang akan dikumpul",description:"Perkhidmatan mungkin mengumpul maklumat berikut apabila anda menghubungi kami atau menggunakan Perkhidmatan.",items:["Alamat E-mel","Nama anda (pilihan)","Butiran Siasatan","Maklumat lain yang disediakan secara sukarela oleh anda"]},purpose:{title:"2. Tujuan penggunaan maklumat",description:"Maklumat peribadi yang dikumpul akan digunakan hanya untuk tujuan berikut",items:["Menjawab dan memberi maklum balas kepada pertanyaan","Hubungi dan maklumat mengenai perkhidmatan kami","Komunikasi diperlukan untuk kemajuan projek.","Analisis untuk penambahbaikan perkhidmatan (dalam format yang tidak mengenal pasti individu)"]},thirdParty:{title:"3. penyediaan kepada pihak ketiga",description:"Perkhidmatan ini tidak akan memberikan maklumat peribadi anda kepada pihak ketiga kecuali dalam kes-kes berikut",items:["Jika pelanggan bersetuju","Apabila terdapat permintaan pendedahan berdasarkan undang-undang dan peraturan","Apabila perlu untuk melindungi nyawa, badan atau harta seseorang"]},security:{title:"4. pengurusan maklumat",description:"Perkhidmatan akan menguruskan maklumat peribadi yang dikumpul dengan sewajarnya dan mengambil langkah keselamatan yang munasabah untuk mencegah akses tanpa kebenaran, kehilangan, kebocoran, dan pemalsuan."},email:{title:"5. Pengendalian e-mel",description:"Perkhidmatan akan mengendalikan e-mel selaras dengan dasar berikut.",items:["Kami akan menggunakan kandungan e-mel anda hanya untuk tujuan menjawab pertanyaan anda dan menyediakan perkhidmatan kami.","Kami tidak akan menjual, menyewa, atau berkongsi alamat e-mel anda dengan mana-mana pihak ketiga.","Kami mungkin menghantar e-mel maklumat mengenai perkhidmatan kami. Jika anda ingin berhenti melanggan, sila gunakan pautan berhenti melanggan dalam e-mel tersebut atau hubungi kami menggunakan maklumat hubungan di bawah.","Sulitan SSL/TLS digunakan untuk komunikasi e-mel bagi memastikan komunikasi yang selamat.","Kami akan memadam e-mel anda dengan selamat selepas menyimpannya untuk tempoh masa yang sesuai."]},cookies:{title:"6. Penggunaan kuki",description:"Kuki boleh dilumpuhkan dalam tetapan pelayar anda."},external:{title:"7. penggunaan perkhidmatan luaran",description:"Perkhidmatan ini mungkin menggunakan perkhidmatan luaran berikut. Sila rujuk pautan masing-masing untuk dasar privasi 

243setiap perkhidmatan.",items:["Google Analytics (access analysis)","Perkhidmatan awan lain yang diperlukan untuk operasi perniagaan"]},changes:{title:"8. perubahan kepada dasar privasi",description:"Dasar ini tertakluk kepada semakan tanpa notis selaras dengan pindaan undang-undang dan peraturan atau perubahan dalam kandungan perkhidmatan. Dasar yang disemak semula akan berkuat kuasa apabila dipaparkan di halaman ini."},contact:{title:"9. hubungi kami",description:"Jika anda mempunyai sebarang pertanyaan mengenai dasar ini, sila hubungi kami di",company:"telet.ai",contactLink:"Pergi ke Borang Pertanyaan"}}},RA={title:"Syarat Penggunaan",subtitle:"Ini adalah terma dan syarat untuk menggunakan laman ini dan untuk menempah konsultasi, membuat pertanyaan, dan meminta maklumat daripada telet.ai.",lastUpdated:"Kemas kini terakhir: 22 Januari 2026",intro:'Syarat Penggunaan ini (selepas ini dirujuk sebagai "Syarat") Syarat Penggunaan ini (selepas ini dirujuk sebagai "Syarat") terpakai kepada laman web yang dioperasikan oleh telet.ai (selepas ini dirujuk sebagai "kami", "kamilah", "milik kami", atau "milik kami"). (selepas ini dirujuk sebagai "kami", "kamil", atau "milik kami"). (selepas ini dirujuk sebagai "Laman") yang dioperasikan oleh telet.ai (selepas ini dirujuk sebagai "Kami"). Laman ini disediakan terutamanya sebagai halaman pendaratan untuk temu janji perundingan, pertanyaan, dan permintaan dokumen, dan tidak menyediakan fungsi penciptaan akaun atau pengebilan berbayar di Laman ini.',sections:{scope:{title:"1. Skop aplikasi",description:'Terma dan Syarat ini terpakai kepada semua pelawat dan pengguna Laman ("Pengguna"). Terma Penggunaan hendaklah terpakai kepada semua pelawat dan pengguna Laman Web (selepas ini dirujuk sebagai "Pengguna"). Sekiranya perjanjian NDA berasingan, perjanjian outsourcing, kontrak individu dan sebagainya dimeterai antara kami dan seorang Pengguna, dokumen tersebut akan mengatasi Terma dan Syarat ini.'},prohibited:{title:"2. Barangan terlarang",description:"Dalam menggunakan Laman ini, Pengguna tidak boleh melakukan mana-mana daripada perkara berikut",items:["Tindakan yang melanggar undang-undang dan peraturan atau ketenteraman awam dan moral","Mana-mana perbuatan yang menceroboh hak harta intelek kami atau mana-mana pihak ketiga, privasi, atau hak dan kepentingan lain.","Tindakan yang mengganggu operasi laman ini atau sistem berkaitan melalui akses tanpa kebenaran, beban berlebihan, sabotaj, dan sebagainya.","Mengemukakan maklumat palsu atau menyamar sebagai pihak ketiga dalam borang pertanyaan, dan sebagainya.","Mengikis, kejuruteraan terbalik, atau akses automatik berlebihan tanpa kebenaran kami terlebih dahulu"]},disclaimer:{title:"3. Penafian",description:'Kami menyediakan laman ini atas dasar "seadanya" dan "sebagaimana tersedia". Setakat yang dibenarkan oleh undang-undang, kami menafikan semua jaminan berkaitan laman ini.',items:["Maklumat di laman ini tertakluk kepada perubahan atau pemadaman tanpa notis.","Kami tidak menjamin ketepatan, kelengkapan, atau kesesuaian maklumat di laman ini untuk tujuan tertentu.","Kami tidak akan bertanggungjawab atas sebarang kerosakan yang timbul daripada penggunaan laman ini oleh anda atau kebergantungan anda terhadap maklumat yang terkandung di dalamnya.","Maklum balas kepada perundingan dan pertanyaan adalah untuk maklumat umum sahaja dan tidak merupakan nasihat undang-undang, cukai, perubatan atau nasihat profesional lain."]},ip:{title:"4. hak harta intelek",description:"Semua hak terhadap teks, imej, reka bentuk, tanda dagangan, logo dan lain-lain di laman ini milik kami atau pemegang hak yang sah. Anda tidak boleh menggunakannya tanpa kebenaran pemegang hak."},links:{title:"5. pautan ke laman luar",description:"Laman web ini mungkin mengandungi pautan ke laman web pihak ketiga. Kami tidak bertanggungjawab terhadap kandungan atau operasi laman yang dipautkan."},changes:{title:"6. Pengubahsuaian Terma dan Syarat ini",description:"Kami berhak untuk mengubah Terma dan Syarat ini dari semasa ke semasa mengikut keperluan. Terma Penggunaan yang telah diubah akan berkuat kuasa apabila dipaparkan di 

243Laman Web."},governingLaw:{title:"7. undang-undang yang mengatur dan bidang kuasa",description:"Terma dan Syarat ini ditadbir dan ditafsirkan mengikut undang-undang Jepun. Jika timbul sebarang pertikaian berkaitan dengan Laman ini, Mahkamah Daerah Tokyo hendaklah mempunyai bidang kuasa eksklusif sebagai mahkamah peringkat pertama."},contact:{title:"8. hubungi kami",description:"Jika anda mempunyai sebarang pertanyaan mengenai Terma dan Syarat ini, sila hubungi",company:"telet.ai",contactLink:"Pergi ke Borang Pertanyaan"}}},NA={title:"gerakan biasa",subtitle:"AI meramalkan",description1:"Maklumat cuaca disediakan secara masa nyata oleh setiap negara. Negara anda dipaparkan di tengah peta. Cuaca semalam dan cuaca hari ini adalah rekod sebenar - awan hujan sepatutnya berada di langit anda pada tarikh dan masa tersebut.",description2:"Walau bagaimanapun, animasi ini juga menganimasikan pergerakan cuaca masa depan.",description3:"Vektor memproses data lampau dan mencari corak pada kelajuan tinggi. Corak cuaca juga boleh diramalkan daripada persekitaran sekeliling. Begitu juga, adalah mungkin untuk meramalkan hasil tuaian, pengeluaran, dan jualan dengan mengira elemen (i) daripada persekitaran serta nilai statistik lanjutan daripada prestasi lampau dan membiarkan AI menganalisisnya.",refresh:"menyegarkan (contohnya memori)",loading:"Memuat peta cuaca..."},GA={hero:{badges:["Perusahaan","LLM Tempatan","Ejen Tersuai"],title:"Reka Bentuk dan Pembangunan Ejen AI untuk Perusahaan",description:"Reka dan bina ejen AI khusus yang disesuaikan dengan keperluan perniagaan"},mission:{title:"Perkhidmatan Pembangunan Ejen AI Tersuai",description:"Kami sentiasa membangunkan ejen AI tersuai yang menangani isu perniagaan unik syarikat anda, daripada reka bentuk hingga pelaksanaan dan operasi. Reka bentuk seni bina kami menekankan keserasian dengan sistem kritikal sedia ada dan persekitaran SaaS, membolehkan integrasi lancar dengan API, pangkalan data, dan alat dalaman. Jurutera kami biasa dengan LangChain, AutoGen, CrewAI, dan rangka kerja utama lain, dan akan mencadangkan penyelesaian terbaik untuk susunan teknologi dan struktur operasi syarikat anda. Kami bukan sahaja membangunkan chatbot sekali sahaja, tetapi juga membina sistem ejen pelbagai di mana beberapa Ejen bekerjasama antara satu sama lain untuk melaksanakan tugas, dan Aliran Kerja Agensik yang berfungsi bersama alat luaran. Kami menyokong memaksimumkan nilai perniagaan yang dibawa oleh Ejen AI melalui perkongsian jangka panjang yang merangkumi penambahbaikan berterusan selepas pelaksanaan, pengemaskinian model, dan penyelenggaraan sistem pemantauan operasi.",imageAlt:"Perkhidmatan Pembangunan Ejen AI Tersuai"},summary:{title:"Tren Pembangunan Ejen",paragraphs:['Dari 2025 hingga 2026, pembangunan Ejen AI bergerak daripada fasa demonstrasi ke fasa pelaksanaan korporat berskala penuh. Konsep "AI Agensial" telah berakar, dan sistem pelaksanaan perniagaan autonomi yang melangkaui sekadar chatbot telah mula beroperasi dalam pelbagai industri.','"Ejen yang menggunakan komputer" yang mengawal pelayar dan desktop secara langsung, seperti Operator OpenAI, Computer Use Anthropic, dan Project Mariner Google, telah menjadi praktikal. Model Context Protocol (MCP) adalah ekosistem yang sedang berkembang, membolehkan integrasi plug-and-play dengan beribu-ribu alat.',"Arkitektur berbilang ejen telah mencapai kematangan, dengan ejen khusus bekerjasama untuk melaksanakan tugas kompleks seperti pengurusan projek, sokongan pelanggan, dan analisis data. Penstandardan protokol A2A (Ejen-ke-Ejen) juga telah membuat kemajuan dalam kerjasama ejen antara pembekal yang berbeza.",'Model tadbir urus telah berkembang daripada "Human-in-the-Loop" kepada "Human-on-the-Loop" untuk pelaksanaan korporat, dengan mekanisme untuk pengauditan keputusan pasca ejen dan eskalasi automatik berdasarkan tahap risiko sebagai ciri standard, mempercepatkan penerimaan dalam industri terkawal seperti kewangan, penjagaan kesihatan, dan pembuatan. Penerimaan sistem ini semakin pesat dalam industri terkawal seperti kewangan, perubatan, dan pembuatan.']},features:{title:"Reka Bentuk dan Pembangunan",subtitle:"Reka Bentuk & Pembangunan",items:[{title:"Reka bentuk ejen berdasarkan keperluan perniagaan",description:"Ekstrak kes penggunaan daripada analisis aliran perniagaan, pecahkan tugas dan jelaskan tanggungjawab Agens. Kami menentukan sama ada pemprosesan boleh diselesaikan oleh seorang Agens sahaja atau memerlukan koordinasi berbilang Agens, dan memilih corak inferens optimum seperti ReAct, Rancang-dan-Laksana, dan Refleksi."},{title:"Integrasi RAG / Alat / Aliran Kerja",description:"Pipeline RAG yang sangat tepat menggabungkan Generasi Embedding, Strategi Pekali, dan Carian Hibrid, penyambungan piawai kepada API dan pangkalan data luaran dengan definisi Alat yang mematuhi MCP, serta orkestrasi aliran kerja berbilang langkah menggunakan LangGraph dan CrewAI. Orkestrasikan aliran kerja berbilang langkah dengan LangGraph dan CrewAI."}

243,{title:"Reka bentuk keselamatan dan kebenaran",description:"Kawalan akses fungsi ejen melalui RBAC/ABAC, langkah balas terhadap suntikan Prompt, serta pengesanan dan penyamaran PII telah dilaksanakan. Log audit bagi semua input dan output disimpan, dan operasi persekitaran yang mematuhi SOC2 dan ISO27001 disokong."},{title:"Pelaksanaan termasuk operasi dan pemantauan",description:"Melaksanakan visualisasi jejak dengan LangSmith, Langfuse dan lain-lain, serta pemantauan masa nyata terhadap kelewatan, penggunaan token, dan kadar ralat. Membina kitaran penambahbaikan berterusan yang merangkumi amaran pengesanan anomali, infrastruktur ujian A/B, dan Ujian Regresi semasa mengemas kini model."}]},techStack:{title:"Susunan Teknologi",subtitle:"Platform & Teknologi",categories:{agentFramework:{title:"Kerangka Agens",items:["AutoGen 0.4 / AG2","CrewAI","Graf Bahasa","SDK Ejen OpenAI","MCP Anthropic","Google ADK (Agent Development Kit)","Nukleus Semantik","Platform Ejen Milik Perusahaan"]},apiLayer:{title:"Lapisan API",items:["FastAPI / Litestar","gRPC / Sambung","Persekutuan GraphQL","Acara yang dihantar oleh pelayan (SSE)","WebSocket / NATS","OAuth 2.1 / OIDC","Gerbang API (Kong / Envoy)","Pembatasan Kadar Adaptif"]},vectorDb:{title:"Pangkalan data vektor / RAG",items:["Kuncup pain","Menenun","Kuadrant","Milvus","ChromaDB","pgvektor","FAISS","Turbopuffer"]}

243,infrastructure:{title:"Infrastruktur",items:["Kubernetes (EKS / GKE / AKS)","Docker / Podman","Kumpulan GPU di Tempat","Awan Persendirian","Tanpa pelayan (Lambda / Cloud Run)","OpenTelemetry / Langfuse","Terraform / Pulumi","ArgoCD / Flux"]}},updateNote:{title:"Titik-titik utama kemas kini untuk 2026",description:"Dalam Agent Framework, SDK/protokol rasmi daripada pembekal utama seperti OpenAI Agents SDK, Anthropic MCP, dan Google ADK sedang muncul; dalam Lapisan API, gRPC dan SSE telah menjadi piawaian untuk komunikasi antara ejen, dan peralihan kepada OAuth 2.1 sedang dijalankan. Dalam Vector DB, Qdrant dan Turbopuffer mendapat perhatian kerana carian berkelajuan tinggi mereka."}},cta:{title:"Hubungi Kami",description:"Kami boleh berunding dengan anda bermula daripada penyusunan keperluan. PoC dan konfigurasi LLM tempatan juga boleh dipertimbangkan.",primaryButton:"Hubungi Kami",secondaryButton:"Perundingan Teknikal"}},OA={title:"Soalan Lazim",subtitle:"Soalan Lazim",items:[{question:"Bolehkah saya menggunakan manual dan Soalan Lazim sedia ada tanpa pengubahsuaian?",answer:"Ya, anda boleh menggunakannya; ia menyokong kebanyakan format, termasuk PDF, Word, Excel, HTML, dan fail teks. Cukup muat naik dokumen dalaman sedia ada seperti sedia kala, dan AI akan mempelajari kandungannya serta menggunakannya untuk menjawab soalan anda. Tiada penukaran format atau pemprosesan khas diperlukan."},{question:"Adakah selamat untuk digunakan dengan data yang mengandungi maklumat sensitif?",answer:"Ya, anda boleh menggunakan perkhidmatan kami dengan selamat. Sama ada di premis (pelayan dalaman) atau dalam persekitaran awan peribadi, data tidak pernah dihantar keluar dari syarikat. Kami boleh mencadangkan konfigurasi untuk memenuhi keperluan keselamatan anda."},{question:"Berapa lama masa yang diperlukan untuk melaksanakannya?",answer:"Bot FAQ standard boleh dilaksanakan dalam tempoh serendah 2–4 minggu. Jika integrasi dengan sistem sedia ada atau penyesuaian lanjutan diperlukan, kami menganggarkan 1–3 bulan. Kami juga boleh memulakan dengan PoC (bukti konsep) skala kecil."},{question:"Adakah anda menggunakan API luaran seperti ChatGPT atau Claude?",answer:"Pilihan tersedia atas permintaan. Kami menyokong kedua-dua konfigurasi yang menggunakan API luaran (OpenAI, Anthropic, Google, dan lain-lain) dan konfigurasi yang menggunakan LLM sumber terbuka (Llama, Mistral, Qwen, dan lain-lain) yang dijalankan sepenuhnya secara tempatan. Kami boleh mencadangkan konfigurasi terbaik untuk keperluan kos, keselamatan, dan prestasi anda."},{question:"Sejauh manakah ketepatan respons AI?",answer:"Bergantung pada aplikasi dan kualiti data, sistem RAG yang direka dengan betul boleh memberikan jawapan tepat kepada soalan yang mempunyai dokumen berkaitan dengan ketepatan 90% atau lebih. Mekanisme untuk mengawal halusinasi (penghasilan maklumat yang salah) juga disertakan sebagai standard."},{question:"Adakah anda menyokong bahasa selain bahasa Jepun?",answer:"Ya, sokongan pelbagai bahasa tersedia. Kami menyokong semua bahasa utama, termasuk Jepun, Inggeris, Cina, dan Korea. Daripada mempunyai model berasingan untuk setiap bahasa, satu sistem boleh mengendalikan pertanyaan pelbagai bahasa."},{question:"Bolehkah anda berintegrasi dengan sistem sedia ada (CRM, alat sembang, dan lain-lain)?",answer:"Ya, ia boleh diintegrasikan; ia menyokong integrasi API dengan alat perniagaan utama seperti Salesforce, HubSpot, Slack, Microsoft Teams, dan LINE WORKS. Anda boleh memasukkan pembantu AI tanpa mengubah aliran kerja sedia ada."},{question:"Apakah struktur yuran anda?",answer:"Ia bergantung pada saiz dan keperluan projek. Walaupun yuran pembinaan awal dan yuran operasi bulanan biasanya digabungkan, kami juga menerima permintaan untuk pembangunan terpilih atau perkhidmatan perundingan sahaja. Sila beritahu kami keperluan anda terlebih dahulu. Kami akan memberikan anda anggaran kasar."}]},BA={title:"Pelan Tarif",subtitle:"Pelan Harga",description:"Pelan fleksibel untuk memenuhi saiz perniagaan anda",plans:[{name:"pemula",price:"29,800 yen",period:"Bulan/pengguna",description:"Rancangan terbaik untuk pasukan kecil. Fungsi AI asas pada harga yang berpatutan.",features:["Sehingga 100 panggilan sebulan","Transkripsi Asas","Pengesanan Kata Kunci","Sokongan E-mel","Penahanan data 7 hari"],cta:"Mulakan secara percuma",popular:!1},{name:"profesional",price:"¥79,800",period:"Bulan/pengguna",description:"Sesuai untuk pasukan yang sedang berkembang. Ciri analitik lanjutan.",features:["Sehingga 500 panggilan sebulan","Transkripsi AI berketepatan tinggi","Analisis Emosi","Pautan CRM","Sokongan Keutamaan","Penahanan data 30 hari","Laporan Tersuai"],cta:"Uji cuba percuma 14 hari",popular:!0},{name:"perusahaan",price:"Hubungi Kami",period:"sesuai",description:"Pelan berperincian penuh untuk organisasi besar",features:["Panggilan tanpa had","Model AI Dedicated","Sokongan di lokasi","Sokongan Khusus","Kerjasama tersuai","Jaminan SLA","Audit Keselamatan","Penahanan data tanpa had"],cta:"Berkonsultasi dengan Jualan",popular:!1}],faq:{title:"Soalan Lazim",items:[{question:"Adakah terdapat percubaan percuma?",answer:"Ya, percubaan percuma selama 14 hari tersedia untuk pelan Profesional. Tiada kad kredit diperlukan untuk memulakan."},{question:"Bolehkah saya menukar pelan saya pada bila-bila masa?",answer:"Ya, anda boleh menaik taraf atau menurunkan taraf pelan anda pada bila-bila masa. Perubahan akan digunakan bermula kitaran pengebilan seterusnya."},{question:"Adakah terdapat terma kontrak yang terikat?",answer:"Pelan bulanan tidak mempunyai sekatan tempoh kontrak. Jika anda memilih kontrak tahunan, anda akan menerima diskaun 20%."}]}},KA={hero:{badge:"Teknologi Berkuasa AI",title:"Transkripsi AI",description:"Model pembelajaran mesin lanjutan menyediakan ketepatan 99.5%. AI yang dilatih pada jutaan data interaksi pelanggan menukarkan suara kepada teks.",features:["Ketepatan 99.5%","pemprosesan masa nyata","Sokongan untuk lebih daripada 40 bahasa"]},features:{title:"Fungsi Utama",subtitle:"Enjin transkripsi lanjutan yang melangkaui pengecaman ucapan ringkas",items:[{title:"Transkripsi masa nyata",description:"Dapatkan transkrip masa nyata semasa perbualan. Dapatkan pandangan segera dan jangan lepaskan peluang bimbingan masa nyata."},{title:"Sokongan pelbagai bahasa",description:"Mendukung lebih daripada 40 bahasa dan merangkumi pasukan global dengan pengesanan bahasa automatik."},{title:"pemahaman kontekstual",description:"Model AI yang dilatih dengan terminologi khusus industri dapat mengenal pasti istilah teknikal dan perniagaan dengan tepat."},{title:"pemisahan pembicara",description:"Secara automatik mengenal pasti dan memisahkan penceramah yang berbeza dalam perbualan serta menetapkan atribut penceramah yang jelas."},{title:"leksikon tersuai",description:"Mempelajari nama produk unik syarikat anda, nama perkhidmatan, dan istilah teknikal memberikan ketepatan yang lebih tinggi."},{title:"Penyelamatan Kualiti",description:"Semakan kualiti automatik dan pemarkahan kebolehpercayaan memastikan transkripsi berkualiti tertinggi."}]},useCases:{title:"Adegan Penggunaan",subtitle:"Industri dan pasukan yang memerlukan transkripsi panggilan yang tepat",items:[{title:"Pasukan Jualan",description:"Renungkan panggilan jualan dan kenal pasti corak kejayaan. Tingkatkan keberkesanan ucapan dan bimbing ahli pasukan de

243ngan contoh khusus."},{title:"Sokongan Pelanggan",description:"Merekod dengan tepat isu pelanggan. Menjaga rekod sokongan yang menyeluruh untuk memastikan kualiti perkhidmatan yang konsisten."},{title:"kawalan kualiti",description:"Memantau pematuhan dan memastikan pematuhan kepada skrip. Menyimpan rekod terperinci untuk audit dan latihan."}]},stats:{accuracy:{value:"99.5%",label:"ketepatan transkripsi"},languages:{value:"40+",label:"Bahasa yang disokong"},latency:{value:"kurang daripada 2 saat",label:"kelewatan pemprosesan"},availability:{value:"24/7",label:"pemprosesan berterusan"}}},VA={hero:{badge:"Pengesanan Pintar",title:"Penjejakan Kata Kunci",description:"Memantau secara automatik sebutan pesaing, sanggahan, dan topik penting. Jangan terlepas isyarat penting daripada perbualan pelanggan.",badges:["Penjejakan Kompetitif","Pengesanan Tindak Balas","Analisis Topik","kata kunci tersuai"]},features:{title:"Lacak maklumat kritikal",subtitle:"Pengesanan dan analisis automatik kata kunci daripada semua perbualan",items:[{title:"Pemantauan Persaingan",description:"Lacak sebutan pesaing untuk memahami bagaimana prospek membandingkan penyelesaian anda dengan alternatif di pasaran."},{title:"Pengesanan Tindak Balas",description:"Mengesan secara automatik bantahan dan kebimbangan biasa serta membangunkan tindak balas dan latihan yang berkesan."},{title:"kata kunci tersuai",description:"Takrifkan dan jejaki kata kunci dan frasa tersuai yang khusus untuk perniagaan, produk atau industri anda."},{title:"Analisis Tren",description:"Temui trend baharu daripada perbualan pelanggan dan sesuaikan strategi anda berdasarkan isyarat pasaran sebenar."},{title:"pengeluaran konteks",description:"Dapatkan konteks sebelum dan selepas kata kunci, bukan hanya sebutan semata-mata. Fahamilah bukan sahaja apa yang dikatakan, tetapi juga mengapa dan bagaimana ia dikatakan."},{title:"Carian Pintar",description:"Cari semua perbualan dengan segera untuk mengetahui dengan tepat di mana dan bila topik tertentu dibincangkan."}]},applications:{title:"Adegan Penggunaan",items:{competitive:{title:"Perisikan Persaingan",description:"Pemahaman mendalam tentang landskap persaingan:",points:["Lacak pesaing yang paling kerap disebut","Memahami bagaimana pelanggan prospektif membandingkan ciri-ciri dan harga","Kenal pasti kelebihan dan kelemahan anda dalam persaingan.","Mencipta bahan anti-persaingan yang berkesan"]},objections:{title:"tindak balas terhadap sanggahan",description:"Ubah sanggahan menjadi peluang:",points:["Kenal pasti kebimbangan pelanggan yang paling biasa","Analisis respons sanggahan para peforma teratas","Buku panduan dengan tindak balas yang terbukti.","Latih pasukan anda dengan penukaran yang berkesan"]},product:{title:"Pandangan Produk",description:"Mempromosikan pembangunan produk melalui maklum balas pelanggan:",points:["Kenal pasti ciri yang paling banyak diminta","Memahami cabaran terhadap fungsi semasa","Temui kes penggunaan yang tidak dijangka","Keutamakan peta jalan berdasarkan keperluan pelanggan"]},compliance:{title:"Pematuhan & Kualiti",description:"Pastikan pematuhan peraturan dan kualiti:",points:["Pantau pendedahan yang diperlukan dan bahasa undang-undang","Tandakan perbualan yang mungkin melanggar dasar","Lacak pematuhan skrip dan amalan terbaik","Jana laporan pematuhan untuk audit"]}}},advancedFeatures:{title:"Ciri Lanjutan",items:[{title:"pemulihan yang samar",description:"Mengesan secara automatik variasi kata kunci, sinonim, dan istilah berkaitan tanpa konfigurasi manual."},{title:"analisis sejarah",description:"Analisis trend kata kunci dari masa ke masa untuk mengenal pasti corak bermusim dan topik yang sedang muncul."},{title:"Amaran masa nyata",description:"Terima pemberitahuan segera apabila kata kunci penting disebut dalam perbualan langsung."}]}},FA={hero:{badges:["Perusahaan","LLM Tempatan","Pengoperasian pelayan dalaman"],title:"Pembinaan LLM di Tempat",description:"Mengendalikan LLM dalam persekitaran anda sendiri, dengan menyimpan data di luar syarikat."},mission:{title:"Rangkuman Perkhidmatan Pembinaan LLM di Lokasi",paragraphs:["Bagi syarikat yang tidak dapat mengeluarkan data sulit di luar syarikat, kami membina persekitaran LLM yang boleh dilaksanakan pada pelayan mereka sendiri atau di awan persendirian. Penyelesaian ini sesuai untuk organisasi yang memerlukan pengurusan maklumat yang ketat, seperti institusi kewangan, institusi perubatan, agensi kerajaan, dan institut penyelidikan, kerana ia tidak menggunakan sebarang API awam dan semua pemprosesan diselesaikan dalam infrastruktur yang diuruskan oleh pelanggan.","Kami menyediakan sokongan menyeluruh daripada pemilihan model, pembinaan pelayan, pe

243ngoptimuman enjin inferens, dan pemantauan operasi, menggunakan model open-weight yang boleh didapati secara komersial seperti Llama 3.3, Qwen 2.5, Mistral, dan DeepSeek dengan enjin inferens berprestasi tinggi seperti vLLM, Ollama, dan SGLang. Model-model ini dijalankan pada enjin inferens prestasi tinggi seperti vLLM, Ollama, dan SGLang untuk menyediakan persekitaran inferens berlatensi rendah dan berprestasi tinggi.","Kami menyokong pelbagai kes penggunaan seperti integrasi dengan sistem sedia ada, chatbot, RAG dalaman, penyusunan dokumen, dan penjanaan kod. Kami boleh mereka bentuk untuk memenuhi keperluan perusahaan, termasuk pengkhususan perniagaan melalui penalaan halus model, pengoptimuman NVIDIA GPU / Apple Silicon, reka bentuk berbilang penyewa, pembinaan gerbang API, dan banyak lagi."]},summary:{title:"Trend LLM di Lokasi",paragraphs:["Pada tahun 2025–2026, kegunaan LLM di premis semakin meningkat dengan pesat. Prestasi model berat terbuka telah menyamai model tertutup, dan bilangan model yang ditawarkan dengan lesen komersial telah mengurangkan dengan ketara halangan untuk operasi dalaman.","Model dengan parameter 8B hingga 70B telah mencapai tahap praktikal untuk banyak tugas perniagaan, kuantisasi GGUF membolehkan inferens berkelajuan tinggi walaupun pada GPU pengguna.","Untuk enjin inferens, Continuous Batching dan PagedAttention vLLM meningkatkan throughput dengan ketara, dan RadixAttention SGLang mempercepat interaksi kompleks berbilang pusingan. Ollama telah menjadi piawaian de facto untuk persekitaran pembangunan tempatan, dan llama.cpp dioptimumkan untuk Apple Silicon bagi inferens pantas pada Mac siri M.","Dalam penerimaan perusahaan, selain kedaulatan data dan pematuhan, pe

243ngoptimuman kos jangka panjang (beralih daripada pengebilan API bayar-sebagaimana-digunakan) semakin menjadi motivasi untuk penerimaan. Konfigurasi hibrid (lokal untuk pemprosesan sensitif dan API awan untuk pemprosesan tujuan umum) telah menjadi pilihan biasa."],icons:[{label:"Awan Persendirian"},{label:"Kedaulatan Data"},{label:"Pengoptimuman Kos"},{label:"Keselamatan Perusahaan"}]},features:{title:"Ciri reka bentuk dan pembangunan",subtitle:"Reka Bentuk & Pembangunan",items:[{title:"Reka bentuk infrastruktur dan pengoptimuman GPU",description:"Reka konfigurasi perkakasan optimum untuk beban kerja dan bajet anda, termasuk NVIDIA A100/H100, RTX 4090/5090, dan Apple Silicon (M2 Ultra/M3 Max/M4 Max), dan bina persekitaran inferens yang memanfaatkan sepenuhnya prestasi GPU melalui pengoptimuman CUDA, MPS, dan Metal. Pengoptimuman CUDA, MPS, dan Metal untuk memaksimumkan prestasi GPU."},{title:"Pemilihan dan pengoptimuman model",description:"Pilih model optimum daripada Llama, Qwen, Mistral, DeepSeek dan lain-lain dengan mengambil kira ciri tugas, keperluan bahasa, dan sekatan sumber. Kuantisasi (GGUF/AWQ/GPTQ), pengoptimuman cache KV, dan penalaan pemprosesan ber kelompok digunakan untuk mencapai ketepatan dan throughput."},{title:"Pembinaan enjin inferens",description:"Pilih dan bina enjin inferens seperti vLLM, SGLang, Ollama, TGI dan lain-lain mengikut aplikasi anda, dan terapkan teknologi terkini seperti Continuous Batching, Speculative Decoding dan Prefix Caching untuk menyediakan API inferens berlatensi rendah dan berprestasi tinggi. Kami menyediakan API inferens berlatensi rendah dan berprestasi tinggi."},{title:"Operasi, Pemantauan dan Keselamatan",description:"Melaksanakan pemantauan metrik inferensial dengan Prometheus/Grafana, kawalan akses dengan gerbang API, log audit, penskalaan automatik, dan pemulihan kegagalan. Membina persekitaran operasi pengeluaran yang memenuhi keperluan keselamatan perusahaan."}]},techStack:{title:"Susunan Teknologi: LLM di premis",subtitle:"Platform & Teknologi",categories:{openWeightModels:{title:"Model Berat Terbuka",items:["Llama 3.3 70B / 8B","Qwen 2.5 72B / 32B / 7B","Mistral Large / Nemo","DeepSeek V3 / R1","Gemma 2 27B / 9B","Phi-4","Perintah R (Cohere)"]},japaneseModels:{title:"Model khusus Jepun",items:["ELYZA-jepun-Llama-2","Telan 70B / 13B","Jepun-Llama-3","Qwen-2.5-Japanese","Karakuri-LM"]},inferenceEngine:{title:"Enjin inferens",items:["vLLM (Pengumpulan Berterusan)","SGLang (RadixAttention)","Ollama (pembangunan tempatan)","llama.cpp (Apple Silicon)","TGI (Hugging Face)","TensorRT-LLM (NVIDIA)","ExLlamaV2 (khusus kuantisasi)"]},quantization:{title:"Kuantizasi",items:["GGUF (piawaian llama.cpp)","AWQ (4-bit ketepatan tinggi)","GPTQ (Pengoptimuman GPU)","bitdanbait(8bit/4bit)","EETQ (Kuantisasi Kecekapan Berkesan)"]},hardware:{title:"Perangkat keras",items:["NVIDIA H100 / A100","NVIDIA L40S / A10G","RTX 4090 / 5090","Apple Silicon M2 Ultra","Apple Silicon M3/M4 Max","AMD MI300X"]}

243,infrastructure:{title:"Infrastruktur",items:["Docker / Kubernetes","Pelayan NVIDIA Triton","Gerbang API (Kong / Envoy)","Prometheus / Grafana","Awan Persendirian (VMware / OpenStack)","Sokongan Persekitaran Berjarak Udara"]}},updateNote:{title:"Titik-titik utama kemas kini untuk 2026",description:"Dalam model berat terbuka, Llama 3.3 mencapai prestasi gunaan umum kelas GPT-4 dan sangat meningkatkan prestasi bahasa Jepun; siri Qwen 2.5 memperluas pilihan dengan pelbagai saiz dan prestasi seimbang; DeepSeek V3/R1 menarik perhatian kerana prestasi kos inferensnya; dan enjin inferens vLLM 2.0 Chunked Prefill serta RadixAttention SGLang mempercepatkan dialog berbilang pusingan. Dalam enjin inferens, Chunked Prefill vLLM 2.0 dan RadixAttention SGLang mempercepatkan interaksi berbilang pusingan; pengoptimuman Apple Silicon untuk llama.cpp menjadikan inferens model 70B praktikal pada M4 Max; dan teknologi Kuantizasi menawarkan ketepatan 4-bit dan prestasi tinggi. Teknik kuantizasi mencapai kedua-dua ketepatan 4-bit dan kelajuan inferens, dan GGUF menjadi format piawaian de facto."}},cta:{title:"Hubungi Kami",description:"Kami boleh berunding dengan anda bermula daripada penyusunan keperluan. PoC dan konfigurasi LLM tempatan juga boleh dipertimbangkan.",primaryButton:"Hubungi Kami",secondaryButton:"Perundingan Teknikal"}},WA={hero:{badges:["Perusahaan","RAG","generasi sambungan carian"],title:"Pembangunan Vektor / RAG",description:"Bina sistem RAG untuk menghubungkan pengetahuan dalaman dengan LLM."},mission:{title:"Rangkuman Perkhidmatan Pembangunan Vector / RAG",paragraphs:["Kami akan membina sistem RAG (generasi sambungan carian) yang membolehkan pekerja mencari dan mengemukakan soalan dalam bahasa semula jadi dengan menghubungkan aset pengetahuan yang terkumpul di syarikat dengan LLM. Kami mengintegrasikan pelbagai sumber data seperti manual, minit mesyuarat, dokumen teknikal, soalan lazim, sejarah Slack, Confluence, SharePoint dan lain-lain untuk mewujudkan persekitaran di mana pengetahuan organisasi boleh digunakan serta-merta.","Kami memaksimumkan ketepatan carian dan kualiti respons dengan menerapkan teknologi terkini seperti Carian Hibrid (carian vektor + carian kata kunci), Reranking, pengagihan hierarki, penapisan metadata dan lain-lain, di samping carian dokumen ringkas. Kami dengan jelas menunjukkan sumber jawapan untuk mengurangkan halusinasi dan memastikan kebolehpercayaan.","Kami menyokong pelbagai persekitaran termasuk AWS, GCP, Azure, dan on-premise, serta mengintegrasikannya dengan infrastruktur pengesahan sedia ada dan pengurusan autoriti. Kami menyediakan sokongan konsisten untuk mengekalkan kesegaran data (kemas kini perbezaan dan penalaan semula automatik), menganalisis log penggunaan, dan sentiasa meningkatkan ketepatan."]},summary:{title:"Trend Teknologi RAG",paragraphs:['Antara tahun 2025 dan 2026, teknologi RAG sedang memasuki fasa "pelaksanaan asas ke kualiti pengeluaran". Selain carian vektor ringkas, Carian Hibrid yang menggabungkan pelbagai kaedah carian, Penyusunan Semula yang menyusun semula keputusan carian, dan Pemecahan Permintaan yang memecah dan memproses soalan kompleks telah menjadi konfigurasi standard.',"Strategi pengelompokan juga telah berkembang, dengan pengelompokan hierarki yang mengambil kira struktur dokumen, kelompok yang bertindih, dan pengelompokan multimodal yang memproses jadual dan figura secara berasingan, kesemuanya menyumbang kepada peningkatan ketepatan. Pemulihan Kontekstual (menambah maklumat kontekstual kepada kelompok) juga telah diiktiraf secara meluas sebagai berkesan.","Untuk pemilihan pangkalan data vektor, selain perkhidmatan terurus seperti Pinecone, Weaviate, Qdrant, dan Milvus, PostgreSQL pgvector dan FAISS tempatan menawarkan pelbagai pilihan untuk memenuhi keperluan anda. Model pemapan juga tersedia, termasuk text-Embedding-3-large, Cohere Embed v3, dan model pelbagai bahasa.",'Dalam penyebaran perusahaan, pautan pengurusan kebenaran (ACL), penyamaran PII, log audit, dan reka bentuk berbilang penyewa adalah keperluan penting, dan perlu beralih daripada "PoC berfungsi" kepada "sistem pengeluaran yang sedia untuk di

243hasilkan".'],icons:[{label:"Pencarian Hibrid"},{label:"RAG multimodal"},{label:"RAG Agensial"},{label:"Keselamatan Perusahaan"}]},features:{title:"Ciri reka bentuk dan pembangunan",subtitle:"Reka Bentuk & Pembangunan",items:[{title:"Saluran pengumpulan dan pemprosesan awal data",description:"Pengekstrakan dan pembersihan data daripada PDF, Word, Excel, HTML, Markdown, Slack, Confluence, SharePoint, dan lain-lain. Sokongan berbilang mod termasuk OCR, analisis struktur jadual, dan penjanaan kapsyen imej. Menjaga kesegaran dengan kemas kini berperbeza dan pelaksanaan terjadual."},{title:"Reka bentuk sistem carian berketepatan tinggi",description:"Membina carian berketepatan tinggi yang menggabungkan Carian Vektor + Carian Hibrid BM25, Reranking oleh Cohere Rerank / Cross-Encoder, serta Pengembangan dan Pemecahan Query. Penapisan metadata dan kawalan akses berasaskan kebenaran juga dilaksanakan."},{title:"Penghasilan respons dan kawalan kualiti",description:"Meningkatkan kualiti jawapan dengan pengoptimuman konteks, arahan Few-shot, dan inferens Chain-of-Thought. Penjelasan sumber, paparan skor keyakinan, dan pengesanan halusinasi telah dilaksanakan untuk memastikan kebolehpercayaan bagi kegunaan perniagaan."},{title:"Kitaran operasi dan penambahbaikan",description:"Kami menyediakan sokongan operasi termasuk analisis log penggunaan, pemantauan kadar kejayaan carian dan kepuasan tindak balas, penambahbaikan berterusan melalui gelung maklum balas, infrastruktur ujian A/B, penyepaduan kemas kini model, dan semakan strategi pembahagian kepada bahagian-bahagian."}]},techStack:{title:"Susunan teknologi: Vektor / RAG",subtitle:"Platform & Teknologi",categories:{embeddingModels:{title:"Penyisipan Model",items:["OpenAI text-embedding-3-large","Cohere Embed v3","Pelayaran AI","Jina Embeddings v3","BGE-M3 (pelbagai bahasa)","pelbagai-bahasa-e5-besar","Model khusus Jepun"]},vectorDatabases:{title:"Pangkalan Data Vektor",items:["Pinecone (Dikelola)","Weaviate (Pencarian Hibrid)","Qdrant (prestasi tinggi)","Milvus (skala besar)","Chroma (ringan)","PostgreSQL pgvector","FAISS (lokal)"]},chunking:{title:"Pengagihan & Pemprosesan",items:["pembahagian berperingkat","Pengelompokan Semantik","Kepingan yang bertindih","Tetingkap Ayat","Pemulihan Kontekstual","Pemprosesan jadual dan carta","Sokongan multimodal"]},search:{title:"Pencarian & Pengambilan",items:["Pencarian Hibrid (Vektor + BM25)","Cohere Rerank","Penarafan Semula Pengkod Salib","Pengembangan dan dekomposisi pertanyaan","HyDE (Pengenerasian Dokumen Hipotesis)","Pemulihan Semula Soalan Diri","Pemulihan Pelbagai Permintaan"]}},updateNote:{title:"Titik-titik utama kemas kini untuk 2026",description:"Dalam model Embedding, text-Embedding-3-large dan Cohere Embed v3 menjadi piawaian perusahaan, dan prestasi bahasa Jepun bertambah baik dengan ketara; Contextual Retrieval terbukti berkesan, dan menambah konteks pada ketulan menjadi kaedah piawai. Carian Hibrid + Reranking telah menjadi konfigurasi wajib untuk carian, mengurangkan bilangan konfigurasi carian vektor ringkas sahaja, dan Agentic RAG (agensi memilih strategi carian secara dinamik) sedang digunakan secara praktikal. Dari segi operasi, penekanan diberikan kepada penubuhan kitaran pengukuran kualiti dan penambahbaikan menggunakan rangka kerja penilaian RAG (RAGAS, LangSmith)."}},cta:{title:"Hubungi Kami",description:"Kami boleh berunding dengan anda bermula daripada penyusunan keperluan. PoC dan konfigurasi LLM tempatan juga boleh dipertimbangkan.",primaryButton:"Hubungi Kami",secondaryButton:"Perundingan Teknikal"}}

243,qA={hero:{badges:["Susunan Penuh","Integrasi LLM","Reka Bentuk API"],title:"Pembangunan Aplikasi AI",description:"Reka dan bina aplikasi praktikal yang memanfaatkan LLM."},mission:{title:"Rangkuman Perkhidmatan Pembangunan Aplikasi AI",paragraphs:["Kami menyediakan reka bentuk dan pembangunan aplikasi AI yang mencipta nilai perniagaan dengan menganalisis data yang diperoleh daripada API luaran menggunakan LLM, menghubungkan API dengan pelbagai sumber data seperti CRM, ERP, SaaS, sensor IoT, dan media sosial, serta memasukkan data yang diperoleh ke dalam LLM secara masa nyata. Kami membolehkan analisis data lanjutan yang tidak dapat dicapai dengan alat BI konvensional, seperti penjanaan laporan analitik menggunakan bahasa semula jadi, pengesanan anomali, ramalan trend, dan pengelasan automatik.","Bukan sekadar integrasi API ringkas, tetapi juga integrasi pelbagai sumber data, pembersihan data, dan penukaran kepada format yang mudah untuk LLM tafsirkan melalui pemprosesan berstruktur. Kejuruteraan prompt memaksimumkan ketepatan analisis dan menghasilkan keluaran yang khusus untuk keperluan perniagaan. Kami membina aplikasi praktikal yang menggabungkan data dan AI, seperti data jualan untuk meramalkan rundingan perniagaan, log sokongan pelanggan untuk menjalankan analisis VoC, dan data sensor untuk memberikan cadangan penyelenggaraan peralatan.","Kami menyokong pelbagai antara muka seperti aplikasi web, aplikasi mudah alih, integrasi Slack/Teams, dan pemprosesan kelompok berkala. Kami menyediakan sokongan berterusan dari penentuan keperluan hingga reka bentuk API, pelaksanaan integrasi LLM, dan operasi pengeluaran."]},summary:{title:"Tren dalam Pembangunan Aplikasi AI",paragraphs:['Dari 2025 hingga 2026, pembangunan aplikasi AI memasuki fasa praktikal berasaskan "integrasi API x analisis LLM". Analisis data tidak berstruktur dan penjanaan wawasan bahasa semula jadi, yang tidak dapat diuruskan oleh alat BI dan papan pemuka tradisional, mula dimasukkan ke dalam sistem perusahaan.',"Yang perlu diberi perhatian khusus ialah kematangan fungsi Output Berstruktur LLM, yang membolehkan kawalan output yang ketat selaras dengan skema JSON, menjayakan saluran automatik hujung ke hujung, di mana data yang diperoleh melalui API dianalisis oleh LLM dan hasilnya dihantar terus ke sistem seterusnya. Pelaksanaan berbilang peringkat Pemanggilan Fungsi membolehkan pengumpulan data, integrasi, dan analisis merentas pelbagai API dikendalikan dalam satu aliran kerja.","Dalam infrastruktur pemprosesan data, Polars telah muncul sebagai piawaian untuk pemprosesan berkelajuan tinggi, dan apabila digabungkan dengan pemprosesan penstriman, ia memudahkan analisis masa nyata.","Dalam pelaksanaan korporat, keperluan telah beralih daripada pembangunan bukti konsep sekali sahaja kepada reka bentuk berskala untuk operasi pengeluaran, yang memerlukan reka bentuk seni bina menyeluruh yang merangkumi pengoptimuman kos, pengendalian ralat, dan sokongan log audit."],icons:[{label:"AI Agensial"},{label:"Protokol MCP"},{label:"Multi ejen"},{label:"Manusia-dalam-Gelung"}]},features:{title:"Ciri reka bentuk dan pembangunan",subtitle:"Reka Bentuk & Pembangunan",items:[{title:"Reka bentuk integrasi API dan pengumpulan data",description:"Bina infrastruktur pengumpulan data yang menyokong pelbagai protokol seperti REST API, GraphQL, WebSocket, dan Webhook. Kami mereka bentuk saluran pemerolehan data yang stabil dengan melaksanakan proses pengesahan (OAuth, kunci API, JWT), had kadar, dan mekanisme percubaan semula. Kami juga menyokong integrasi data, penjadualan, dan pemerolehan perbezaan daripada pelbagai API."},{title:"Pemprosesan dan penyusunan data",description:"Menukar data mentah ke dalam format yang boleh dianalisis dengan cekap oleh LLM, termasuk pemarseman JSON, pembersihan data, penormalan, dan pemprosesan nilai yang hilang, serta melaksanakan segmentasi dan pengringkasan data yang optimum dengan mengambil kira sekatan tetingkap konteks. Penugasan metadata memastikan kebolehjejakan keputusan analisis."},{title:"Analisis LLM dan reka bentuk arahan",description:"Maksimumkan ketepatan analisis data de

243ngan mereka bentuk templat arahan khusus mengikut keperluan perniagaan anda. Kami membina saluran analisis untuk pengelasan, penyusunan ringkasan, analisis sentimen, pengesanan anomali, penjanaan ramalan, dan lain-lain. Kami menggunakan Panggilan Fungsi dan Output Berstruktur untuk menjana hasil dalam format yang mudah disepadukan dengan sistem seterusnya."},{title:"Pelaksanaan dan operasi aplikasi",description:"Kami menyediakan keputusan analisis melalui pelbagai antara muka, termasuk FastAPI, aplikasi web menggunakan Next.js, Bot Slack/Teams, dan pelaksanaan pukal terjadual. Paparan pemantauan, amaran pemberitahuan, dan penjanaan laporan automatik dilaksanakan dan disampaikan dalam bentuk yang boleh digunakan serta-merta oleh pengguna perniagaan. Kami juga menyediakan pemantauan operasi, pengoptimuman kos, dan penambahbaikan berterusan."}]},techStack:{title:"Susunan Teknologi: Pembangunan Aplikasi AI",subtitle:"Platform & Teknologi",categories:{apiIntegration:{title:"Integrasi API",items:["REST / GraphQL / WebSocket","OAuth 2.1 / API key / JWT","Webhook / Pengecaman","Apache Airflow / Prefect","Seleri / Temporal","permintaan / httpx / aiohttp","Had Kelajuan / Cuba Semula","Pengesahan Data (Pydantic)"]},dataProcessing:{title:"Pemprosesan Data",items:["Beruang / Kutub","Parser JSON / JSONL","Saluran Pemurnian Data","Apache Spark (Pemprosesan berskala besar)","dbt (penukaran data)","Harapan Yang Besar","Tasik Delta / Gunung ais","Streaming (Kafka / Flink)"]},llmCloud:{title:"LLM Awan",items:["OpenAI GPT-4o / GPT-4.1","Claude 3.5 / Claude 4","Gemini 2.0 / Gemini 2.5","Perintah R+ (Cohere)","Mistral Large"]},llmLocal:{title:"LLM Tempatan",items:["Llama 3.3 / Llama 4","Mistral / Mixtral","Qwen 2.5 / Qwen 3","DeepSeek V3 / R1","Gemma 2 / Gemma 3","Phi-4","ELYZA / Swallow (khusus dalam bahasa Jepun)","Perintah R (Cohere)"]},inference:{title:"Inferens / Kerangka",items:["vLLM / SGLang","Ollama / llama.cpp","LiteLLM Proksi","Panggilan Fungsi","Keluaran Berstruktur","Enjin Templat Prompt","LangChain / LlamaIndex","Saluran Paip Analisis Milik Perusahaan"]},application:{title:"Lapisan Aplikasi",items:["FastAPI / Litestar","Next.js / React","Slack SDK / Teams SDK","Streamlit / Gradio","Tugas Jadual (Cron / Cloud Scheduler)","PostgreSQL / MongoDB","Redis (Cache / Queue)","Docker / Kubernetes"]}},updateNote:{title:"Titik-titik utama kemas kini untuk 2026",description:"Dalam bidang analisis LLM, fungsi Output Berstruktur GPT-4.1 dan Claude 4 telah matang untuk menstandardkan kawalan output yang ketat selaras dengan Skema JSON, dan pelaksanaan berbilang peringkat Panggilan Fungsi membolehkan pengumpulan dan analisis data kompleks merentas pelbagai API dalam satu saluran. Pelaksanaan berbilang peringkat Panggilan Fungsi membolehkan pengumpulan dan analisis data kompleks merentas pelbagai API dalam satu saluran. Dalam pemprosesan data, Polars telah menjadi alternatif yang mantap kepada Pandas untuk pemprosesan berkelajuan tinggi, dan digabungkan dengan pemprosesan penstriman, ia memudahkan pembinaan infrastruktur analisis masa nyata. Pada lapisan aplikasi, integrasi alat luaran menggunakan MCP telah menjadi perkara biasa, mempercepat pembangunan aplikasi agenik di mana LLM memanggil API secara autonomi untuk memperoleh dan menganalisis data."}},cta:{title:"Hubungi Kami",description:"Kami boleh berunding dengan anda bermula daripada penyusunan keperluan. PoC dan konfigurasi LLM tempatan juga boleh dipertimbangkan.",primaryButton:"Hubungi Kami",secondaryButton:"Perundingan Teknikal"}},UA={seo:vA,header:AA,hero:xA,mission:LA,businessServices:PA,features:wA,businessProcess:SA,story:IA,contact:MA,footer:jA,cookiePolicy:CA,about:DA,portfolio:zA,common:TA,privacyPolicy:EA,termsOfService:RA,weatherMap:NA,agentDevelopment:GA,faq:OA,pricing:BA,aiTranscription:KA,keywordTracking:VA,onPremiseLLM:FA,vectorRAG:WA,aiAppDevelopment:qA},_A={home:{title:"Platform Solusi Berbasis Kecerdasan Buatan - telet.ai",description:"Transformasikan percakapan pelanggan Anda dengan analitik panggilan yang didukung oleh kecerdasan buatan (AI). Dapatkan wawasan real-time, analisis sentimen, dan pelacakan kinerja untu

243k meningkatkan hasil tim Anda."},about:{title:"Informasi Organisasi - telet.ai",description:"Learn about telet.ai's mission to transform customer conversations with AI. Meet our team and explore our journey."},privacy:{title:"Kebijakan Privasi - telet.ai",description:"Review telet.ai's privacy policy to understand how we handle your data and protect your privacy."},terms:{title:"Syarat dan Ketentuan - telet.ai",description:"Read telet.ai's terms of service for using this website and contacting us for consultations."},cookies:{title:"Kebijakan Cookie - telet.ai",description:"Learn how telet.ai uses cookies and similar technologies (Cloudflare and Google Analytics)."},portfolio:{title:"Portofolio - telet.ai",description:"Jelajahi portofolio solusi kecerdasan buatan (AI) kami yang dirancang untuk meningkatkan operasional bisnis dan pengalaman pelanggan Anda."},agentDevelopment:{title:"Desain dan Pengembangan Agen Kecerdasan Buatan (AI) untuk Perusahaan - telet.ai",description:"Desain dan bangun agen AI berkualitas produksi yang disesuaikan dengan alur kerja, alat, dan kebutuhan kepatuhan Anda."},onPremiseLLM:{title:"Operasi LLM di Lokasi - telet.ai",description:"Jalankan model bahasa besar (LLM) modern di lingkungan Anda untuk kontrol data yang kuat, biaya yang dapat diprediksi, dan inferensi dengan latensi rendah."},vectorRAG:{title:"Desain Vektor RAG & Indeks - telet.ai",description:"Desain alur kerja dan indeks yang disesuaikan dengan setiap alur kerja untuk meningkatkan jawaban yang didukung data."},aiAppDevelopment:{title:"Pengembangan Aplikasi Kecerdasan Buatan - telet.ai",description:"Bangun aplikasi praktis yang didukung oleh LLMs—terintegrasi dengan API, data, dan alur kerja bisnis Anda."}},HA={brand:"telet.ai",nav:{services:"Layanan",portfolio:"Portofolio",about:"Informasi Organisasi",contact:"Kontak"}},$A={brandName:"telet.ai",tagline:"Komunikasi multi AI",badge:"Kecerdasan Panggilan Bertenaga AI",title:{line1:"Lokal dan defensif,",line2:"Perluas di awan,",line3:"Otomatis oleh agen."},description:"Edge inference dalam SLM memastikan kedaulatan data, dan integrasi cloud dalam LLM meningkatkan daya pemrosesan hingga tak terbatas; adopsi arsitektur Agentic AI mulai menunjukkan hasil dari perusahaan-perusahaan terkemuka."},QA={badge:"Misi Kami",title:"Membuat LLM lebih mudah diakses",paragraphs:['Apakah Anda benar-benar bisa melangkah sejauh ini?" --Kejutan itulah yang menjadi tujuan kami.','Potensi pemodelan bahasa berskala besar (LLM) terus berkembang setiap hari. Namun, kami ingin mend

243obrak hambatan seperti "Saya khawatir tentang pengiriman data rahasia ke API eksternal," "Aplikasi AI tingkat lanjut hanya untuk perusahaan besar," dan "Biaya implementasi tidak sepadan.',"Kami mengkhususkan diri dalam membangun LLM lokal yang diselesaikan di lingkungan Anda. Informasi pelanggan yang sangat sensitif dapat digunakan dengan aman tanpa terekspos ke dunia luar. Kami dapat memaksimalkan manfaat AI sekaligus memastikan keamanan data.","Sistem ini menangani segala sesuatu mulai dari pembuatan dokumen rutin dan organisasi data hingga analisis khusus dan dukungan keputusan. Teknologi agen milik kami memberikan akurasi yang sebanding dengan model API.",'LLM tidak menggantikan manusia. Ini adalah alat yang ampuh yang memungkinkan orang untuk menggunakan lebih banyak kreativitas, penilaian, dan keahlian. Kami membawa teknologi dari hal yang istimewa menjadi hal yang biasa - sebuah "konsumerisasi" yang sesungguhnya dari teknologi.','Jangan ragu untuk berkonsultasi dengan kami. Kami akan bekerja sama dengan Anda untu

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243an dan ringkasan yang dibuat secara alami berdasarkan pemahaman kontekstual."},{title:"Manajer AI Pemantauan Log Keamanan",description:"Pemantauan log keamanan secara real-time, deteksi dan analisis ancaman oleh LLM, dan peringatan langsung dengan mencocokkan dengan pola serangan sebelumnya oleh RAG."}]},XA={title:"Cara melanjutkan proyek",headline:"Yang Anda perlukan hanyalah sebuah visi.",paragraphs:['Sudah lewatlah sudah hari-hari ketika perbedaan bahasa menjadi penghalang dalam bisnis, dan meskipun kami menggunakan penerjemahan AI, kami dapat menangani negosiasi dan pengembangan bisnis dalam 14 bahasa melalui alur kerja unik kami yang memanfaatkan sepenuhnya berbagi gambar. Bahkan spesifikasi yang rumit pun dapat diperiksa dengan contoh gambar untuk menghindari miskomunikasi. Satu-satunya hal yang penting adalah "apa yang ingin Anda buat.',"Proyek ini dimulai dengan audiensi awal dalam aplikasi bisnis. Setelah berbagi visi dan pemahaman umum tentang masalah yang dihadapi klien, NDA (Non-Disclosure Agreement) ditandatangani. Setelah menciptakan lingkungan di mana informasi rahasia dapat didiskusikan secara rahasia, kami melakukan wawancara terperinci dan mengonfirmasi persyaratan. Berdasarkan konten, kami menyiapkan proposal dan mengkonfirmasi arah proyek dengan menggunakan contoh gambar. Dengan pemahaman yang ada, kami menyiapkan spesifikasi dan memulai pengembangan.","Alur ini hanya merupakan pengantar umum. Kami akan secara fleksibel beradaptasi dengan situasi dan jadwal Anda. Di mana pun Anda berada di dunia, meskipun Anda tidak berbicara dalam bahasa yang sama, gambar dan AI akan mendukung komunikasi Anda. Mengapa Anda tidak bergabung dengan kami untuk memasuki pasar perbatasan yang belum pernah dijamah oleh siapa pun? Biarkan telet.ai menjadi mitra Anda untuk mewujudkan ide-ide Anda."],cards:[{title:"Sidang / NDA",description:"Setelah pemeriksaan awal melalui email, perjanjian kerahasiaan ditandatangani. Anda dapat memberikan detail kepada kami dengan tenang."},{title:"Perencanaan & CA",description:"Proposal dan contoh gambar disediakan. Tersedia 14 bahasa yang didukung, sehingga Anda dapat melanjutkan tanpa hambatan bahasa."},{title:"Pengambilan Sampel & Mulai",description:"Kami akan menyiapkan spesifikasi dan memulai bisnis setelah ada kesepakatan."},{title:"Pengembangan, pengujian, dan pengoperasian",description:"Implementasi yang cepat melalui pengembangan yang gesit. Kami memberikan dukungan yang konsisten dari pengujian hingga dukungan operasional."}]},YA={badge:"Studi Kasus",title:"Kisah Pengenalan AI",items:[{title:"Solusi Efisiensi Bisnis dan Pengurangan Biaya",subtitle:"Kurangi beban kerja harian dan ciptakan lingkungan yang memungkinkan Anda untu

243k fokus pada bisnis inti Anda.",description:'Banyak usaha kecil dan menengah (UKM) menghadapi kekurangan tenaga kerja dan kesulitan perekrutan. Bahkan ketika personil baru dipekerjakan, biaya pelatihan tinggi dan ada masalah dengan tingkat retensi. Solusi AI kami mendukung tugas-tugas harian seperti mengotomatiskan manajemen anggaran, entri dan pengaturan data, pengambilan dokumen internal, dan pembuatan laporan dan dokumen secara otomatis. Asisten AI menangani tugas-tugas rutin yang biasanya menghabiskan banyak waktu, 24 jam sehari, 365 hari setahun, tanpa kesalahan. Perusahaan yang telah memperkenalkan AI telah menyadari pengurangan jam kerja yang signifikan. Dari "meningkatkan sumber daya manusia" menjadi "berkolaborasi dengan AI. Kami akan melakukan yang terbaik untuk mendukung reformasi gaya kerja perusahaan Anda.'},{title:"Otomatisasi Dukungan Pelanggan",subtitle:"Layanan 24 jam meningkatkan kepuasan pelanggan dan mengurangi biaya tenaga kerja.",description:"Chatbot AI memberikan respons otomatis untuk pertanyaan yang sering diajukan, respons utama dan penyortiran pertanyaan, serta respons waktu nyata dalam berbagai bahasa. Tanggapan waktu nyata dalam berbagai bahasa. Operator manusia dapat fokus pada kasus-kasus yang kompleks dan meningkatkan kualitas layanan pelanggan. Tingkatkan kualitas layanan Anda ke level berikutnya dengan dukungan pelanggan AI."},{title:"AI Pemanfaatan Pengetahuan dan Dokumen",subtitle:"Aset pengetahuan internal dalam bentuk yang dapat segera dimanfaatkan oleh siapa saja",description:'Di mana buku panduan itu?" Ada informasi yang hanya diketahui oleh pendahulunya" - pengetahuan yang tidak personal menghambat pertumbuhan perusahaan. Teknologi RAG (search extension generation) kami memungkinkan Anda untu

243k mengambil informasi yang diperlukan dari manual internal, FAQ, dan dokumen terdahulu hanya dengan mengajukan pertanyaan dalam bahasa alami. Teknologi ini menyederhanakan pelatihan karyawan baru, mendigitalkan pengetahuan tacit dari para veteran, dan memungkinkan berbagi pengetahuan antar departemen. Dokumen yang sudah ada dapat digunakan sebagaimana adanya, sehingga tidak perlu membuat dokumen baru.'},{title:"AI analisis gambar untuk perawatan medis dan keperawatan",subtitle:"Mata yang waspada, dukungan 24 jam oleh AI",description:"Dalam bidang perawatan medis dan keperawatan, penting untuk mengawasi sejumlah besar pasien dan pengguna dengan staf yang terbatas. AI analisis gambar/video kami mendeteksi ketidaknormalan secara real time dari gambar kamera. Secara otomatis mendeteksi penyakit menular dengan mendeteksi demam, memberi tahu segera jika pasien jatuh atau mengembara, dan mendeteksi perubahan kondisi fisik dengan menganalisis ekspresi wajah. Dengan mengurangi beban patroli malam hari, merespons keadaan darurat dengan cepat, dan mengotomatiskan tugas pencatatan, staf dapat fokus pada tugas perawatan utama mereka. Instalasi di tempat juga tersedia untuk memastikan privasi."}]},ex={title:"Hubungi Kami",description:"Kami dapat berkonsultasi dengan Anda mulai dari tahap perencanaan kebutuhan, dan juga dapat mempertimbangkan konfigurasi PoC dan LLM lokal.",openForm:"Formulir Terbuka",closeForm:"Tutup formulir",form:{name:"Nama Anda",namePlaceholder:"Taro Yamada",email:"Alamat email",emailPlaceholder:"[email protected]",subject:"subjek",subjectPlaceholder:"Perihal pertanyaan Anda",message:"Pesan",messagePlaceholder:"Silakan isi pertanyaan Anda",required:"*",submit:"Kirim",submitting:"Mengirim...",turnstileMessage:'Otorisasi "Saya bukan robot" diperlukan untuk pengiriman.',turnstileError:"Gagal memuat otorisasi keamanan. Silakan muat ulang.",errorRetry:"Silakan coba lagi."}},ax={brand:"telet.ai",tagline:"Komunikasi multi AI",description:"Inovasikan bisnis Anda dengan agen AI. Ciptakan nilai dari dialog.",sections:{product:{title:"Produk",features:"Fitur",benefits:"pengantar",testimonials:"Testimoni Pelanggan"},services:{title:"Layanan",agentDev:"Desain dan Pengembangan Agen AI untuk Perusahaan",onPremise:"server LLM sesuai janji",vectorRag:"RAG vektor dan desain indeks",aiApp:"Pengembangan Aplikasi AI"},company:{title:"Informasi Organisasi",about:"Tentang telet.ai",profile:"Profil Perwakilan",contact:"Hubungi Kami"},process:{title:"Bagaimana kami menjalankan bisnis",hearing:"Sidang / NDA",planning:"Perencanaan & CA",sampling:"Pengambilan Sampel & Mulai",development:"Pengembangan, pengujian, dan pengoperasian"}},copyright:"© 2026 telet.ai. Semua hak cipta dilindungi undang-undang.",legal:{privacy:"Kebijakan Privasi",terms:"Ketentuan Penggunaan",cookies:"Kebijakan Cookie"}},nx={title:"Kebijakan Cookie",subtitle:"Bagian ini menjelaskan tentang cookie dan teknologi lain (Cloudflare / Google Analytics) yang digunakan di situs ini.",lastUpdated:"Pembaruan terakhir: 22 Januari 2026",intro:'telet.ai ("kami", "kita", "milik kita") ("Kami") dapat menggunakan cookie dan teknologi serupa untuk menyediakan dan meningkatkan Situs ini dan memastikan keamanannya. Kami tidak mengatur cookie pelacakan khusus kami sendiri untuk tujuan periklanan (meskipun Cloudflare dan Google Analytics dapat mengatur cookie atau teknologi serupa sesuai dengan spesifikasi layanan masing-masing).',sections:{what:{title:"1. Apa yang dimaksud dengan cookie?",description:"Cookie adalah file teks kecil yang disimpan di browser Anda saat Anda mengunjungi situs web."},howWeUse:{title:"Tujuan penggunaan cookie kami, dll.",description:"Kami dapat menggunakan cookie, dll. terutama untuk tujuan berikut",items:["Untuk menstabilkan tampilan dan pengoperasian situs","Untuk mendeteksi dan mencegah akses yang tidak sah dan lalu lintas berbahaya","Untuk memahami status akses dan membantu meningkatkan situs"]},googleAnalytics:{title:"3. Penggunaan Google Analytics",description:"Kami menggunakan Google Analytics 4 (GA4) yang disediakan oleh Google LLC untuk analisis akses.",items:["Informasi yang dikumpulkan dapat mencakup halaman yang dilihat, waktu yang dihabiskan, sumber rujukan, informasi perangkat dan browser, dll.","Kami tidak menggunakan informasi yang dapat diidentifikasi secara langsung (nama, alamat email, dll.) untuk tujuan mengirimkannya ke Google Analytics.","Perlakuan data oleh Google tunduk pada persyaratan dan kebijakan Google"]},cloudflare:{title:"4. Penggunaan Cloudflare",description:"Kami menggunakan layanan dari Cloudflare, Inc. untuk pengiriman dan langkah-langkah keamanan Situs ini. Cloudflare dapat mengatur dan merujuk pada cookie, dll. untuk tujuan seperti perlindungan DDoS dan pencegahan bot.",items:["Tujuan utamanya adalah untu

243k keamanan dan pengoptimalan komunikasi, dan tidak dimaksudkan untuk dilacak untuk penayangan iklan","Perlakuan Cloudflare terhadap data tunduk pada ketentuan dan kebijakan Cloudflare"]},noSpecialCookies:{title:"5. Kue-kue spesial kami sendiri",description:"Kami tidak menetapkan cookie khusus kami sendiri untuk tujuan iklan penargetan perilaku, dll. Penggunaan cookie, dll. terutama terbatas pada ruang lingkup operasi situs, keamanan, dan analisis akses."},choices:{title:"6. Manajemen cookie (memilih keluar)",description:"Jika cookie dinonaktifkan, beberapa fungsi mungkin tidak tersedia.",items:["Cookie dapat dihapus atau dinonaktifkan di pengaturan browser","Jika Anda ingin menonaktifkan pengukuran oleh Google Analytics, silakan gunakan metode penyisihan yang disediakan oleh Google","Penggunaan mode privat browser, dll., dapat membatasi informasi yang disimpan."]},changes:{title:"7. perubahan terhadap kebijakan ini",description:"Kami dapat merevisi kebijakan ini sebagai tanggapan atas perubahan peraturan perundang-undangan atau perubahan layanan yang digunakan. Kebijakan yang direvisi akan berlaku efektif ketika diposting di halaman ini."},contact:{title:"8. hubungi kami",description:"Jika Anda memiliki pertanyaan mengenai kebijakan ini, silakan hubungi kami di",company:"telet.ai",contactLink:"Buka Formulir Pertanyaan"}}},tx={hero:{title:"Tentang telet.ai",subtitle:"Dukungan pengembangan AI yang cermat yang hanya dapat diberikan oleh tim kecil yang dipersonalisasi."},story:{title:"Tentang kami",content:"telet.ai dimulai dengan sebuah pertanyaan. Mengapa begitu banyak perusahaan gagal memanfaatkan wawasan yang ada dalam interaksi yang tak terhitung jumlahnya antara perusahaan dan pelanggan mereka? Tim yang dikepalai oleh seorang perwakilan yang berpengalaman dalam penelitian AI dan pengembangan perangkat lunak perusahaan, didirikan untuk mengatasi masalah ini secara langsung. Kami bukanlah organisasi yang besar. Itulah sebabnya kami mampu menghadapi tantangan Anda secara mendalam dan hanya mengusulkan solusi yang benar-benar diperlukan. Kekuatan kami adalah kemampuan kami untuk merancang arsitektur hibrida yang mengintegrasikan LLM lokal dan LLM cloud. Kami dapat melindungi data sensitif perusahaan Anda di lingkungan cloud khusus sambil melakukan inferensi AI tingkat lanjut dengan cara yang terukur. Kami menyediakan CLI untuk pengembang dan UI obrolan untuk staf garis depan, menciptakan fondasi untuk penggunaan AI di seluruh organisasi Anda. Dan kami fokus untuk menjadi solusi yang optimal, bukan yang paling canggih. Membuang fungsi sesuai dengan anggaran dan meninjau hanya satu konfigurasi dapat membuat perbedaan besar dalam biaya. Kami memberikan apa yang Anda butuhkan, dalam jumlah yang Anda butuhkan. Dukungan pengembangan AI kami yang penuh perhatian hanya mungkin dilakukan karena kami adalah perusahaan kecil dan personal. Untuk mengatasi kekurangan tenaga kerja dan mendukung pertumbuhan perusahaan Anda. Itulah janji telet.ai."},representative:{title:"Perwakilan dari telet.ai labo",name:"Keita Okuizumi",tagline:"Ke langit, ke laut, dan ke kode - hasrat untuk pengembangan AI yang lahir dari karier yang tidak biasa",paragraphs:['Sebagai mantan pilot swasta (PPL) dan pemegang lisensi kapal, saya telah berurusan dengan cuaca baik di udara maupun di laut selama bertahun-tahun. Di dalam kokpit dan ruang kemudi, saya mengamati perubahan pada barometer, menguraikan garis tekanan isobarik, dan membandingkannya dengan pergerakan angin dan awan. Perasaan "membaca data, menilai, dan bertindak" yang saya kembangkan melalui proses yang berulang-ulang ini, merupakan titik awal bagi saya saat ini.','Saya mulai belajar pemrograman Python pada usia 57 tahun. Dorongannya adalah rasa ingin tahu yang sederhana, tetapi begitu saya mulai menulis kode, saya terpesona oleh kemungkinan agen AI. Saya membuat aplikasi dengan tangan saya sendiri, menjalankannya, dan memperbaikinya. Dalam prosesnya, saya menyadari sesuatu. Dalam proyek-proyek sebelumnya, saya terlibat sebagai seorang non-insinyur, dan pengembangan selalu dilakukan oleh orang lain. Namun, begitu saya mulai menulis kode sendiri, saya mulai melihat esensi masalahnya. Inilah hambatannya di sini" atau "Desain ini tidak dapat diperluas" - dengan memiliki sudut pandang seorang insinyur, kecepatan mewujudkan apa yang ingin saya lakukan telah meningkat secara dramatis.',"Mengembangkan layanan cuaca dengan menggabungkan pengalaman lapangan dengan ilmu data, membangun agen AI untuk meningkatkan efisiensi operasional, dan berbagai proyek eksperimental. Ketika saya terus bekerja dengan tangan saya, saya menjadi yakin tidak hanya ba

243hwa teknologinya menarik, tetapi juga bahwa pasti ada orang yang membutuhkannya.","Tujuan kami bukan untuk mengejar yang paling mutakhir. Kami menghadapi masalah dan anggaran pelanggan, dan hanya memberikan apa yang benar-benar diperlukan dengan biaya yang masuk akal. Kami bukanlah organisasi besar, sehingga kami dapat memberikan dukungan pengembangan yang disesuaikan dengan kebutuhan masing-masing pelanggan. Kami ingin menggunakan teknologi ini untuk kepentingan masyarakat - itulah sebabnya kami meluncurkan telet.ai."]},contact:{title:"Informasi Organisasi",company:"telet.ai labo",ceo:"Perwakilan: OKUIZUMI Keita",button:"Hubungi Kami"},studios:{tokyo:{title:"Tokyo Studio",address:"#301 First Hills A Musashikoganei, 3-3-21 Nukuiminamicho, Koganei-shi, Tokyo 184-0014, Jepang"}}},ix={title:"Portofolio",subtitle:"Lihat rekam jejak kami dalam memanfaatkan teknologi AI",techStackLabel:"Tumpukan Teknologi",viewProject:"Lihat Proyek",privateProject:"Proyek Swasta",visualizationLabel:"visualisasi data",cta:{title:"Hubungi Kami",description:"Jangan ragu untuk menghubungi kami untuk mendiskusikan proyek Anda atau untuk pertanyaan teknis.",button:"Hubungi Kami"},projects:{"traffic-route-ai":{title:"Sistem Pemilihan Rute Transportasi AI",subtitle:"Perencanaan rute transportasi umum di Malaysia",description:"Sistem yang mencari kombinasi rute bus dan kereta antara titik keberangkatan dan tujuan serta membandingkan pilihan berdasarkan waktu tiba, durasi perjalanan, dan jumlah perpindahan. Filter khusus bus atau kereta, pencarian stasiun dan halte terdekat, serta antarmuka multibahasa membantu memilih rute untuk perjalanan sehari-h

243ari maupun wisata.",techStack:["Rute Transportasi Umum","Integrasi Bus dan Kereta","Perbandingan Rute","Antarmuka Multibahasa"]},"multilingual-assistant":{title:"Asisten Multibahasa AI",description:"Asisten obrolan cerdas untuk pertanyaan multi-bahasa, membangun 1.573 indeks vektor dan mengimplementasikan kontrol jawaban yang tepat melalui percabangan bersyarat, pemfilteran kata NG, dan pemicuan kata kunci. Jawaban yang konsisten dan sangat akurat untuk pertanyaan pengguna dalam berbagai bahasa, termasuk bahasa Jepang, Inggris, dan Mandarin, dihasilkan melalui pencarian semantik lintas bahasa.",techStack:["RAG","Pencarian Vektor","Penyematan Multibahasa","LLM","logika cabang bersyarat"]},"weather-forecast":{title:"Prakiraan Cuaca AI dengan API Cuaca",description:"Mengembangkan sistem AI yang belajar dari data cuaca masa lalu dan membuat prakiraan waktu nyata dari kondisi cuaca terkini. LLM menganalisis data masa lalu yang diperoleh dari JMA API, OpenWeatherMap, dll., dan menghasilkan prakiraan jangka pendek untuk suhu, probabilitas curah hujan, kecepatan angin, dll. Menggabungkan pengenalan pola data deret waktu dengan pencarian pola kemiripan di masa lalu dengan RAG, sistem ini menghasilkan prakiraan yang sangat akurat yang melengkapi model statistik konvensional.",techStack:["Tautan API Cuaca","LLM","RAG","Analisis Data Deret Waktu","pemrosesan waktu nyata"]},"local-llm":{title:"Server Internal / On-Prem Local LLM",description:"Kami memberikan AI yang disesuaikan dengan operasi bisnis Anda, siap digunakan di dalam perusahaan Anda, dengan Open WebUI sebagai titik masuk umum, jalur inferensi cepat dengan Ollama/MLX, dan domain data yang sepenuhnya terpisah untuk setiap UID. Dokumen internal ditangkap dalam RAG dengan penyematan bahasa Inggris dan terjemahan kueri bahasa Jepang otomatis, dan keterampilan bisnis seperti ringkasan berita, organisasi dokumen, dan tugas rutin dijalankan dengan aman secara berdampingan. Sistem eksekusi keterampilan diisolasi sebagai wadah OpenClaw dan dilindungi oleh tiga lapisan Cloudflare edge dan Nginx lokal. Kami menyediakan dukungan komprehensif untuk pemilihan model, desain keterampilan, dan kebijakan operasional, dan memulai AI khusus perusahaan Anda dalam bentuk yang dapat digunakan sejak hari pertama digunakan.",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"Prediksi Kedatangan Orbit Antarplanet (AI)",description:"Mengembangkan sistem untuk menganalisis titik keberangkatan yang optimal untuk transfer antar planet dengan memperoleh data posisi planet, elemen orbit, dan parameter gravitasi dari API NASA. Menghitung perhitungan orbit dengan mempertimbangkan orbit transfer Hohmann dan bantuan gravitasi menggunakan API dan Python, serta menampilkan jendela peluncuran, waktu yang dibutuhkan, dan efisiensi bahan bakar. Sistem ini digunakan sebagai alat simulasi untuk lembaga pendidikan dan perusahaan yang berhubungan dengan ruang angkasa. *Fungsi API saat ini telah dihapus, perhitungan sedang berlangsung dengan API dan Python.",techStack:["NASA API (Horizons, SPICE)","Perhitungan Dinamika Orbital","Python","visualisasi data"]},"real-estate":{title:"Analisis AI Data Real Estat",description:"Mengembangkan sistem yang mengumpulkan informasi real estat untuk wilayah tertentu melalui penggalian web dan melakukan analisis data tingkat lanjut menggunakan LLM dan Python. Variabel seperti harga properti, harga unit tsubo, usia bangunan, dan jarak stasiun diekstraksi, dan analisis tren harga, prakiraan pasar, dan simulasi hasil investasi dihasilkan secara otomatis. Fitur-fitur juga diekstraksi dari deskripsi properti yang tidak terstruktur menggunakan LLM untuk memberikan analisis pasar yang komprehensif yang dikombinasikan dengan data kuantitatif. *Sekarang tersedia dalam versi kedua (versi LENGKAP tersedia secara terpisah)",techStack:["Pengikisan web (Scraping, Playwright)","Python","Panda","Analisis LLM","visualisasi data"]},"fortune-ai":{title:"Sistem Penilaian AI Empat Pilar",subtitle:"Mereproduksi kearifan tradisional dengan teknologi.",paragraphs:["Empat Pilar Ramalan adalah puncak dari ramalan Timur yang telah berusia ribuan tahun. Sistem ini mereproduksi logika ramalan tradisional sepenuhnya dalam bahasa Python, dikombinasikan dengan interpretasi dan pembuatan saran menggunakan LLM (Large Language Models). AI mereproduksi dengan tepat proses penilaian yang dilakukan oleh peramal ahli.","Setelah memasukkan tanggal, waktu, dan tempat lahir, sistem mengambil waktu matahari terbit dan terbenam serta mengkoordinasikan informasi pada saat itu dari Astronomy API. Berdasarkan data ini, sistem secara akurat menghitung tanda zodiak, sepuluh dewa, dan keseimbangan lima elemen. Logika berdasarkan ilmu ramalan Empat Pilar Takdir diindeks dalam bahasa Python dan digabungkan dengan data statistik dan informasi RAG (Retrieval Augmentation Generation) untuk meningkatkan keakuratan analisis rumus kehidupan.","Data gaya hidup yang dihitung dijelaskan dalam bahasa alami oleh LLM dengan cara yang mudah dipahami. Hasil penilaian yang dipersonalisasi dihasilkan, termasuk aliran keberuntungan, kecocokan hubungan interpersonal, dan pekerjaan yang cocok untuk Anda. Anda dapat memperoleh jawaban dengan kedalaman yang sama seperti jika Anda berkonsultasi dengan peramal profesional.","Saat ini tersedia sebagai versi kedua. Anda bisa mencoba analisis kumishiki dasar dan analisis keberuntungan. Versi lengkapnya, yang mencakup analisis yang lebih terperinci dan saran dari berbagai sisi, tersedia secara terpisah."],techStack:["Python","API Astronomi (Matahari Terbit-Matahari Terbenam, TimeZoneDB)","Logika Empat Pilar","LLM","Perhitungan koordinat dan waktu"]}

243,"google-workspace":{title:"Google Workspace AI integration",description:"Mengembangkan sistem yang menghubungkan Google Workspace (Gmail, Drive, Kalender, Dokumen, Spreadsheet) dengan LLM untuk membaca, mengedit, dan memproses data bisnis secara otomatis. AI membantu tugas sehari-hari seperti meringkas konten email, membuat draf untuk balasan otomatis, mencari dan menganalisis file Drive, mengekstrak jadwal dari Kalender, dan mengusulkan penyesuaian. Pengoperasian ruang kerja dari Agen juga diwujudkan melalui koneksi API yang aman dengan otentikasi OAuth dan desain alat yang sesuai dengan MCP. Versi kedua sekarang tersedia (versi LENGKAP akan disediakan secara terpisah).",techStack:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"]},"world-news":{title:"BERITA DUNIA Multibahasa",description:"Pengembangan sistem yang mengumpulkan artikel secara real time dari sumber berita di seluruh dunia dan melakukan penerjemahan multibahasa, peringkasan, dan kategorisasi menggunakan LLM. Menerjemahkan artikel asli dalam bahasa Inggris, Mandarin, Prancis, Arab, dan bahasa lainnya ke dalam bahasa Jepang dan mengaturnya berdasarkan topik. Pemantauan berita internasional yang efisien tidak dapat dicapai dengan terjemahan mesin yang sederhana, tetapi dengan terjemahan alami berdasarkan pemahaman kontekstual menggunakan LLM dan pembuatan ringkasan yang mengekstrak poin-poin utama dari artikel. *Sekarang tersedia dalam versi kedua (versi LENGKAP tersedia secara terpisah).",techStack:["API Berita","Koleksi RSS","Terjemahan dan Ringkasan LLM","pemrosesan multibahasa","Kategorisasi otomatis","pemrosesan batch terjadwal"]},"security-monitoring":{title:"Manajer AI Pemantauan Log Keamanan",description:"Mengembangkan sistem untuk pemantauan log keamanan perusahaan secara real-time serta deteksi dan analisis ancaman tingkat lanjut menggunakan LLM. Berbagai sumber log seperti firewall, IDS / IPS, log otentikasi, dan log aplikasi dikumpulkan dan dianalisis oleh modul Python, dan dibandingkan dengan pola serangan masa lalu, informasi kerentanan, dan kasus insiden yang terkumpul dalam RAG. Ketika pola abnormal terdeteksi, LLM menjelaskan tingkat ancaman, cakupan dampak, dan respons yang direkomendasikan dalam bahasa alami, dan segera memberi tahu orang yang bertanggung jawab. Jika pola yang sama terulang kembali, peringatan prioritas tinggi secara otomatis dikeluarkan untuk mempercepat respons insiden.",techStack:["Modul Pemantauan Python","Pengumpulan log (Fluentd, Logstash)","RAG","Pola Ancaman DB","Analisis LLM","Pemberitahuan peringatan (Slack, PagerDuty)"]}}},sx={learnMore:"Baca lebih lanjut",getStarted:"mulai ...",contactUs:"Hubungi Kami",backToHome:"Kembali ke Rumah"},rx={title:"Kebijakan Privasi",subtitle:"Kebijakan Privasi",lastUpdated:"Pembaruan terakhir: 23 Januari 2026",intro:'telet.ai ("Layanan") menganggap perlindungan informasi pribadi Anda sebagai tanggung jawab penting dan telah menetapkan kebijakan privasi berikut.',sections:{collection:{title:"1. Informasi yang akan dikumpulkan",description:"Layanan dapat mengumpulkan informasi berikut ini ketika Anda menghubungi kami atau menggunakan Layanan.",items:["Alamat email","Nama Anda (opsional)","Detail Pertanyaan","Informasi lain yang diberikan secara sukarela oleh Anda"]},purpose:{title:"2. tujuan penggunaan informasi",description:"Informasi pribadi yang dikumpulkan hanya akan digunakan untuk tujuan berikut",items:["Menjawab dan menanggapi pertanyaan","Kontak dan informasi tentang layanan kami","Komunikasi yang diperlukan untuk kemajuan proyek","Analisis untuk peningkatan layanan (dalam format yang tidak mengidentifikasi individu)"]},thirdParty:{title:"3. Penyediaan kepada pihak ketiga",description:"Layanan tidak akan memberikan informasi pribadi Anda kepada pihak ketiga kecuali dalam kasus-kasus berikut",items:["Jika pelanggan menyetujui","Ketika ada permintaan pengungkapan berdasarkan peraturan perundang-undangan","Jika diperlukan untuk melindungi nyawa, tubuh, atau properti seseorang"]},security:{title:"4. manajemen informasi",description:"Layanan akan mengelola informasi pribadi yang dikumpulkan dengan baik dan mengambil langkah-langkah keamanan yang wajar untuk mencegah akses yang tidak sah, kehilangan, kebocoran, dan pemalsuan."},email:{title:"5. Penanganan email",description:"Layanan akan menangani email sesuai dengan kebijakan berikut.",items:["Kami akan menggunakan isi email Anda hanya untuk tujuan menanggapi pertanyaan Anda dan menyediakan layanan kami.","Kami tidak akan menjual, menyewakan, atau membagikan alamat email Anda kepada pihak ketiga mana pun","Kami mungkin akan mengirimkan email informasi tentang layanan kami. Jika Anda ingin berhenti berlangganan, silakan gunakan tautan berhenti berlangganan di email atau hubungi kami menggunakan informasi kontak di bawah ini.","Enkripsi SSL/TLS digunakan untuk komunikasi email untuk memastikan komunikasi yang aman","Kami akan menghapus email Anda dengan aman setelah menyimpannya dalam jangka waktu yang sesuai."]},cookies:{title:"6. penggunaan cookie",description:"Cookie dapat dinonaktifkan di pengaturan browser Anda."},external:{title:"7. penggunaan layanan eksternal",description:"Layanan ini dapat menggunakan layanan eksternal berikut ini. Silakan lihat tautan masing-masing untuk mengetahui kebijakan privasi 

243setiap layanan.",items:["Google Analytics (access analysis)","Layanan cloud lainnya yang diperlukan untuk operasi bisnis"]},changes:{title:"8. perubahan pada kebijakan privasi",description:"Kebijakan ini dapat direvisi tanpa pemberitahuan sesuai dengan perubahan peraturan perundang-undangan atau perubahan konten layanan. Kebijakan yang telah direvisi akan berlaku efektif ketika diposting di halaman ini."},contact:{title:"9. hubungi kami",description:"Jika Anda memiliki pertanyaan mengenai kebijakan ini, silakan hubungi kami di",company:"telet.ai",contactLink:"Buka Formulir Pertanyaan"}}},ox={title:"Ketentuan Penggunaan",subtitle:"Ini adalah syarat dan ketentuan untuk menggunakan situs ini dan untuk memesan konsultasi, membuat pertanyaan, dan meminta informasi dari telet.ai.",lastUpdated:"Pembaruan terakhir: 22 Januari 2026",intro:'Ketentuan Penggunaan ini (selanjutnya disebut sebagai "Ketentuan") Ketentuan Penggunaan ini (selanjutnya disebut sebagai "Ketentuan") berlaku untuk situs web yang dioperasikan oleh telet.ai (selanjutnya disebut sebagai "kami", "kita", "milik kami", atau "milik kita"). (selanjutnya disebut sebagai "kami", "kita", atau "milik kami"). (selanjutnya disebut sebagai "Situs") yang dioperasikan oleh telet.ai (selanjutnya disebut sebagai "Kami"). Situs ini disediakan terutama sebagai halaman arahan untuk janji konsultasi, pertanyaan, dan permintaan dokumen, dan tidak menyediakan pembuatan akun atau fungsi penagihan berbayar di Situs.',sections:{scope:{title:"1. ruang lingkup aplikasi",description:'Syarat dan Ketentuan ini berlaku untuk semua pengunjung dan pengguna Situs ("Pengguna"). Syarat Penggunaan berlaku untuk semua pengunjung dan pengguna Situs (selanjutnya disebut sebagai "Pengguna"). Dalam hal NDA terpisah, perjanjian outsourcing, kontrak individu, dll. disepakati antara kami dan Pengguna, dokumen tersebut akan diutamakan daripada Syarat dan Ketentuan.'},prohibited:{title:"2. barang yang dilarang",description:"Dalam menggunakan Situs, Pengguna tidak boleh melakukan tindakan berikut ini",items:["Tindakan yang melanggar hukum dan peraturan atau ketertiban umum dan moral","Tindakan apa pun yang melanggar hak kekayaan intelektual, privasi, atau hak atau kepentingan kami atau pihak ketiga mana pun","Tindakan yang mengganggu pengoperasian situs ini atau sistem terkait melalui akses yang tidak sah, beban yang berlebihan, sabotase, dll.","Mengirimkan informasi palsu atau menyamar sebagai pihak ketiga dalam formulir pertanyaan, dll.","Mengikis, merekayasa balik, atau akses otomatis yang berlebihan tanpa izin sebelumnya dari kami"]},disclaimer:{title:"3. Penafian",description:'kami menyediakan situs ini atas dasar "sebagaimana adanya" dan "sebagaimana tersedia". Sejauh yang diizinkan oleh hukum, kami menyangkal semua jaminan sehubungan dengan situs ini.',items:["Informasi di situs ini dapat berubah atau dihapus tanpa pemberitahuan.","Kami tidak menjamin keakuratan, kelengkapan, atau kesesuaian untuk tujuan tertentu dari informasi yang ada di situs ini.","Kami tidak bertanggung jawab atas segala kerusakan yang diakibatkan oleh penggunaan situs ini atau ketergantungan pada informasi yang terkandung di sini.","Tanggapan terhadap konsultasi dan pertanyaan hanya untuk informasi umum dan bukan merupakan nasihat hukum, pajak, medis, atau nasihat profesional lainnya."]},ip:{title:"4. Hak kekayaan intelektual",description:"Semua hak atas teks, gambar, desain, merek dagang, logo, dan lain-lain yang ada di situs ini adalah milik kami atau pemegang hak yang sah. Anda tidak boleh menggunakannya tanpa izin dari pemegang hak."},links:{title:"5. Tautan ke situs eksternal",description:"Situs web ini mungkin berisi tautan ke situs web pihak ketiga. Kami tidak bertanggung jawab atas konten atau pengoperasian situs yang ditautkan."},changes:{title:"6. Modifikasi Syarat dan Ketentuan ini",description:"Kami berhak untuk mengubah Syarat dan Ketentuan ini dari waktu ke waktu sesuai kebutuhan. Syarat dan Ketentuan yang telah diubah akan berlaku efektif ketika diposting di Situs."},governingLaw:{title:"7. Hukum dan yurisdiksi yang mengatur",description:"Syarat dan Ketentuan ini diatur oleh dan ditafsirkan sesuai dengan hukum Jepang. Jika timbul sengketa sehubungan dengan Situs ini, Pengadilan Distrik Tokyo memiliki yurisdiksi eksklusif sebagai pengadilan tingkat pertama."},contact:{title:"8. hubungi kami",description:"Jika Anda memiliki pertanyaan mengenai Syarat dan Ketentuan ini, silakan hubungi",company:"telet.ai",contactLink:"Buka Formulir Pertanyaan"}}},lx={title:"gerakan teratur",subtitle:"AI memprediksi",description1:"Informasi cuaca disediakan secara real-time oleh setiap negara. Negara Anda ditampilkan di tengah peta. Cuaca kemarin dan cuaca hari ini adalah catatan aktual - awan hujan seharusnya ada di langit Anda pada tanggal dan waktu tersebut.",description2:"Namun, animasi ini juga menganimasikan pergerakan cuaca di masa depan.",description3:"

243Vektor melewati data dan mencari pola dengan kecepatan tinggi. Pola cuaca juga dapat diprediksi dari lingkungan sekitar. Demikian pula, dimungkinkan untuk memprediksi panen, produksi, dan penjualan dengan menghitung elemen (i) dari lingkungan dan nilai statistik lanjutan dari kinerja masa lalu dan meminta AI menganalisisnya.",refresh:"menyegarkan (mis. memori)",loading:"Memuat peta cuaca..."},dx={hero:{badges:["Perusahaan","LLM lokal","Agen Khusus"],title:"Desain dan Pengembangan Agen AI untuk Perusahaan",description:"Merancang dan membangun Agen AI khusus yang disesuaikan dengan kebutuhan bisnis"},mission:{title:"Layanan Pengembangan Agen AI Khusus",description:"Kami secara konsisten mengembangkan Agen AI khusus yang menangani masalah bisnis unik perusahaan Anda, mulai dari desain hingga implementasi dan operasi. Desain arsitektur kami menekankan kompatibilitas dengan sistem mission-critical dan lingkungan SaaS yang ada, memungkinkan integrasi yang lancar dengan API, database, dan alat internal. Teknisi kami terbiasa dengan LangChain, AutoGen, CrewAI, dan kerangka kerja utama lainnya, Teknisi kami terbiasa dengan LangChain, AutoGen, CrewAI, dan kerangka kerja utama lainnya, dan akan mengusulkan solusi terbaik untuk tumpukan teknologi dan struktur operasional perusahaan Anda. Kami tidak hanya mengembangkan chatbot sekali pakai, tetapi juga membangun sistem multi-agen di mana beberapa Agen bekerja sama satu sama lain untuk melakukan tugas, dan Alur Kerja Agen yang bekerja bersama dengan alat eksternal. Kami mendukung memaksimalkan nilai bisnis yang dibawa oleh Agen AI melalui kemitraan jangka panjang yang mencakup peningkatan berkelanjutan setelah implementasi, mengikuti pembaruan model, dan pemeliharaan sistem pemantauan operasional.",imageAlt:"Layanan Pengembangan Agen AI Khusus"},summary:{title:"Tren Pengembangan Agen",paragraphs:['Dari tahun 2025 hingga 2026, pengembangan Agen AI bergerak dari fase demonstrasi ke fase implementasi perusahaan berskala penuh. Konsep "Agentic AI" telah mengakar, dan sistem eksekusi bisnis otonom yang lebih dari sekadar chatbot telah mulai beroperasi di berbagai industri.','Agen Pengguna Komputer" yang secara langsung mengontrol browser dan desktop, seperti Operator dari OpenAI, Penggunaan Komputer dari Anthropic, dan Project Mariner dari Google, telah menjadi praktis. Model Context Protocol (MCP) adalah ekosistem yang sedang berkembang, memungkinkan integrasi plug-and-play dengan ribuan alat.',"Arsitektur multi-agen telah mencapai kematangan, dengan Agen khusus yang bekerja sama untuk melakukan tugas-tugas kompleks seperti manajemen proyek, dukungan pelanggan, dan analisis data. Standarisasi protokol A2A (Agen-ke-Agen) juga telah membuat kemajuan dalam kolaborasi Agen di antara vendor yang berbeda.",'Model tata kelola telah berevolusi dari "Human-in-the-Loop" menjadi "Human-on-the-Loop" untuk implementasi perusahaan, dengan mekanisme audit keputusan pasca-agen dan eskalasi otomatis berdasarkan tingkat risiko sebagai fitur standar, yang mempercepat adopsi di industri yang diatur seperti keuangan, perawatan kesehatan, dan manufaktur. Adopsi sistem ini semakin cepat di industri yang diatur seperti keuangan, medis, dan manufaktur.']},features:{title:"Desain dan Pengembangan",subtitle:"Desain & Pengembangan",items:[{title:"Desain agen berdasarkan kebutuhan bisnis",description:"Mengekstrak kasus penggunaan dari analisis aliran bisnis, menguraikan tugas, dan memperjelas tanggung jawab Agen. Kami menentukan apakah pemrosesan dapat diselesaikan oleh satu Agen atau memerlukan koordinasi multi-Agen, dan memilih pola inferensi yang optimal seperti ReAct, Plan-and-Execute, dan Refleksi."},{title:"Integrasi RAG / Alat / Alur Kerja",description:"Pipeline RAG yang sangat akurat yang menggabungkan Embedding Generation, Chunk Strategy, dan Hybrid Search, koneksi terstandardisasi ke API eksternal dan database dengan definisi Tool yang sesuai dengan MCP, dan orkestrasi alur kerja multi-langkah menggunakan LangGraph dan CrewAI. Mengatur alur kerja multi-langkah dengan LangGraph dan CrewAI."},{title:"Desain keamanan dan otorisasi",description:"Kontrol akses fungsi Agen oleh RBAC/ABAC, penanggulangan Injeksi yang Cepat, serta deteksi dan penyembunyian PII diimplementasikan. Log audit dari semua input dan output disimpan, dan operasi lingkungan yang sesuai dengan SOC2 dan ISO27001 didukung."},{title:"Implementasi termasuk operasi dan pemantauan",description:"Menerapkan visualisasi Trace dengan LangSmith, Langfuse, dll., dan pemantauan latensi, konsumsi token, dan tingkat kesalahan secara real-time. Membangun siklus peningkatan berkelanjutan termasuk peringatan deteksi anomali, infrastruktur pengujian A/B, dan Uji Regresi saat memperbarui model."}]},techStack:{title:"Tumpukan Teknologi",subtitle:"Platform & Teknologi",categories:{agentFramework:{title:"Kerangka Kerja Agen",items:["AutoGen 0.4 / AG2","CrewAI","LangGraph","OpenAI Agents SDK","MCP Antropik","Google ADK (Agent Development Kit)","Kernel Semantik","Platform Agen Kepemilikan"]},apiLayer:{title:"Lapisan API",items:["FastAPI / Litestar","gRPC / Hubungkan","Federasi GraphQL","Peristiwa yang Dikirim Server (SSE)","Soket Web / NATS","OAuth 2.1 / OIDC","Gerbang API (Kong / Utusan)","Pembatasan Laju Adaptif"]},vectorDb:{title:"Vektor DB / RAG",items:["Pinecone","Menenun","Qdrant","Milvus","ChromaDB","pgvector","FAISS","Turbopuffer"]}

243,infrastructure:{title:"Infrastruktur",items:["Kubernetes (EKS / GKE / AKS)","Docker / Podman","Cluster GPU On-Premise","Cloud Pribadi","Tanpa Server (Lambda / Cloud Run)","OpenTelemetri / Langfuse","Terraform / Pulumi","ArgoCD / Fluks"]}},updateNote:{title:"Poin pembaruan utama untuk tahun 2026",description:"Dalam Agent Framework, SDK/protokol resmi dari vendor-vendor besar seperti OpenAI Agents SDK, Anthropic MCP, dan Google ADK mulai bermunculan; dalam API Layer, gRPC dan SSE telah menjadi standar untuk komunikasi Agen-ke-Agen, dan transisi ke OAuth 2.1 sedang berlangsung. Di Vector DB, Qdrant dan Turbopuffer mendapatkan perhatian untuk pencarian berkecepatan tinggi."}},cta:{title:"Hubungi Kami",description:`Kami dapat berkonsultasi dengan Anda mulai dari pengaturan persyaratan.
244Konfigurasi PoC dan LLM lokal juga dapat dipertimbangkan.`,primaryButton:"Hubungi Kami",secondaryButton:"Konsultasi Teknis"}},ux={title:"Pertanyaan yang Sering Diajukan",subtitle:"PERTANYAAN YANG SERING DIAJUKAN",items:[{question:"Dapatkah saya menggunakan manual dan FAQ yang sudah ada tanpa modifikasi?",answer:"Ya, Anda dapat menggunakannya; mendukung sebagian besar format, termasuk PDF, Word, Excel, HTML, dan file teks. Cukup unggah dokumen internal Anda yang sudah ada, dan AI akan mempelajari kontennya dan menggunakannya untuk menjawab pertanyaan Anda. Tidak diperlukan konversi format atau pemrosesan khusus."},{question:"Apakah aman digunakan dengan data yang mengandung informasi sensitif?",answer:"Ya, Anda bisa menggunakan layanan kami dengan aman. Di lokasi (server internal) atau di lingkungan cloud pribadi, data tidak pernah dikirimkan ke luar perusahaan. Kami dapat mengusulkan konfigurasi untuk memenuhi kebutuhan keamanan Anda."},{question:"Berapa lama waktu yang dibutuhkan untuk menerapkannya?",answer:"Bot FAQ standar dapat diimplementasikan hanya dalam waktu 2-4 minggu. Jika integrasi dengan sistem yang sudah ada atau kustomisasi tingkat lanjut diperlukan, kami memperkirakan 1-3 bulan. Kami juga dapat memulai dengan PoC (bukti konsep) skala kecil."},{question:"Apakah Anda menggunakan API eksternal seperti ChatGPT atau Claude?",answer:"Pilihan tersedia berdasarkan permintaan. Kami mendukung konfigurasi yang menggunakan API eksternal (OpenAI, Anthropic, Google, dll.) dan konfigurasi yang menggunakan LLM open source (Llama, Mistral, Qwen, dll.) yang berjalan sepenuhnya secara lokal. Kami dapat mengusulkan konfigurasi terbaik untuk kebutuhan biaya, keamanan, dan kinerja Anda."},{question:"Seberapa akuratkah respons AI?",answer:"Tergantung pada aplikasi dan kualitas data, sistem RAG yang dirancang dengan baik dapat memberikan jawaban yang akurat untuk pertanyaan-pertanyaan yang memiliki dokumen yang relevan dengan akurasi 90% atau lebih. Mekanisme untuk mengontrol halcination (pembuatan informasi yang salah) juga dibangun sebagai standar."},{question:"Apakah Anda mendukung bahasa selain bahasa Jepang?",answer:"Ya, dukungan multibahasa tersedia. Kami mendukung semua bahasa utama, termasuk bahasa Jepang, Inggris, Mandarin, dan Korea. Alih-alih memiliki model terpisah untuk setiap bahasa, satu sistem dapat menangani pertanyaan multibahasa."},{question:"Dapatkah Anda mengintegrasikan dengan sistem yang ada (CRM, alat bantu chatting, dll.)?",answer:"Ya, ini dapat diintegrasikan; mendukung integrasi API dengan alat bisnis utama seperti Salesforce, HubSpot, Slack, Microsoft Teams, dan LINE WORKS. Anda bisa menggunakan asisten AI tanpa mengubah alur kerja yang sudah ada."},{question:"Bagaimana struktur biaya Anda?",answer:"Tergantung pada ukuran dan persyaratan proyek. Meskipun biaya konstruksi awal dan biaya operasi bulanan umumnya digabungkan, kami juga menerima permintaan untuk pengembangan di tempat atau layanan konsultasi saja. Harap beri tahu kami kebutuhan Anda terlebih dahulu. Kami akan memberikan perkiraan kasar."}]},cx={title:"Paket Tarif",subtitle:"Paket Harga",description:"Paket yang fleksibel agar sesuai dengan ukuran bisnis Anda",plans:[{name:"starter",price:"29.800 yen",period:"Bulanan/pengguna",description:"Paket terbaik untuk tim kecil. Fungsi AI dasar dengan harga terjangkau.",features:["Hingga 100 panggilan per bulan","Transkripsi Dasar","Deteksi Kata Kunci","Dukungan Email","Penyimpanan data 7 hari"],cta:"Mulailah secara gratis",popular:!1},{name:"profesional",price:"Â¥79,800",period:"Bulanan/pengguna",description:"Ideal untuk tim yang sedang berkembang. Fitur analitik tingkat lanjut.",features:["Hingga 500 panggilan per bulan","Transkripsi AI presisi tinggi","Analisis Emosional","Hubungan CRM","Dukungan Prioritas","Penyimpanan data 30 hari","Laporan Khusus"],cta:"Uji coba gratis 14 hari",popular:!0},{name:"perusahaan",price:"Hubungi Kami",period:"adat",description:"Paket berfitur lengkap untuk organisasi besar",features:["Panggilan tak terbatas","Model AI Khusus","dukungan di tempat","Dukungan Khusus","Kolaborasi khusus","Jaminan SLA","Audit Keamanan","Penyimpanan data tanpa batas"],cta:"Konsultasikan dengan bagian Penjualan",popular:!1}],faq:{title:"Pertanyaan yang Sering Diajukan",items:[{question:"Apakah ada uji coba gratis?",answer:"Ya, uji coba gratis 14 hari tersedia untuk paket Profesional. Tidak diperlukan kartu kredit untuk memulai."},{question:"Dapatkah saya mengubah paket saya kapan saja?",answer:"Ya, Anda dapat meningkatkan atau menurunkan paket Anda kapan saja. Perubahan akan diterapkan mulai dari siklus penagihan berikutnya."},{question:"Apakah ada jangka waktu kontrak yang terikat?",answer:"Paket bulanan tidak memiliki batasan jangka waktu kontrak. Jika Anda memilih kontrak tahunan, Anda akan menerima diskon 20%."}]}},mx={hero:{badge:"Teknologi yang Didukung AI",title:"Transkripsi AI",description:"Model pembelajaran mesin yang canggih memberikan akurasi 99,5%. AI yang dilatih pada jutaan data interaksi pelanggan mengubah suara menjadi teks.",features:["Keakuratan 99,5","pemrosesan waktu nyata","Dukungan untuk lebih dari 40 bahasa"]},features:{title:"Fungsi Utama",subtitle:"Mesin transkripsi canggih yang melampaui pengenalan ucapan sederhana",items:[{title:"transkripsi waktu nyata",description:"Dapatkan transkrip waktu nyata selama percakapan. Dapatkan wawasan instan dan jangan pernah melewatkan kesempatan pelatihan waktu nyata."},{title:"dukungan multibahasa",description:"Mendukung lebih dari 40 bahasa dan mencakup tim global dengan deteksi bahasa otomatis."},{title:"pemahaman kontekstual",description:"Model AI yang dilatih dengan terminologi khusus industri secara akurat mengenali istilah teknis dan bisnis."},{title:"pemisahan speaker",description:"Secara otomatis mengidentifikasi dan memisahkan pembicara yang berbeda dalam percakapan dan memberikan atribut pembicara yang jelas."},{title:"leksikon khusus",description:"Mempelajari nama produk, nama layanan, dan istilah teknis perusahaan Anda yang unik akan memberikan akurasi yang lebih tinggi."},{title:"Jaminan Kualitas",description:"Pemeriksaan kualitas otomatis dan penilaian keandalan memastikan transkripsi dengan kualitas terbaik."}]},useCases:{title:"Adegan Penggunaan",subtitle:"Industri dan tim yang membutuhkan transkripsi panggilan yang akurat",items:[{title:"Tim Penjualan",description:"Merefleksikan panggilan penjualan dan mengidentifikasi pola keberhasilan. Tingkatkan efektivitas pembicaraan dan latih anggota tim dengan contoh-contoh spesifik."},{title:"Dukungan Pelanggan",description:"Mencatat masalah pelanggan secara akurat. Memelihara riwayat dukungan yang komprehensif untuk memastikan kualitas layanan yang konsisten."},{title:"kontrol kualitas",description:"Memantau kepatuhan dan memastikan kepatuhan naskah. Menyimpan catatan terperinci untuk audit dan pelatihan."}]},stats:{accuracy:{value:"99.5%",label:"akurasi transkripsi"},languages:{value:"40+",label:"Bahasa yang Didukung"},latency:{value:"<2 detik.",label:"penundaan pemrosesan"},availability:{value:"24/7",label:"pemrosesan berkelanjutan"}}},px={hero:{badge:"Deteksi Cerdas",title:"Pelacakan Kata Kunci",description:"Secara otomatis memantau penyebutan, bantahan, dan topik penting dari pesaing. Jangan pernah melewatkan sinyal penting dari percakapan pelanggan.",badges:["Pelacakan Kompetitif","Deteksi Re

244spons","Analisis Topik","kata kunci khusus"]},features:{title:"Melacak informasi penting",subtitle:"Deteksi otomatis dan analisis kata kunci dari semua percakapan",items:[{title:"Pemantauan Kompetitif",description:"Lacak sebutan kompetitor untuk memahami bagaimana prospek membandingkan solusi Anda dengan alternatif yang ada di pasar."},{title:"Deteksi Respons",description:"Secara otomatis mengidentifikasi keberatan dan kekhawatiran umum serta mengembangkan tanggapan dan pelatihan yang efektif."},{title:"kata kunci khusus",description:"Tentukan dan lacak kata kunci dan frasa khusus yang spesifik untuk bisnis, produk, atau industri Anda."},{title:"Analisis Tren",description:"Temukan tren yang muncul dari percakapan pelanggan dan sesuaikan strategi Anda berdasarkan sinyal pasar yang sebenarnya."},{title:"ekstraksi konteks",description:"Dapatkan konteks sebelum dan sesudah kata kunci, bukan hanya penyebutannya saja. Pahami tidak hanya apa yang dikatakan, tetapi juga mengapa dan bagaimana hal itu dikatakan."},{title:"Pencarian Cerdas",description:"Cari semua percakapan secara instan untuk menemukan dengan tepat di mana dan kapan topik tertentu dibahas."}]},applications:{title:"Adegan Penggunaan",items:{competitive:{title:"Kecerdasan Kompetitif",description:"Pemahaman mendalam tentang lanskap persaingan:",points:["Lacak pesaing Anda yang paling sering disebut","Memahami bagaimana calon pelanggan membandingkan fitur dan harga","Identifikasi keunggulan dan kelemahan kompetitif Anda","Menciptakan materi anti-persaingan yang efektif"]},objections:{title:"tanggapan terhadap sanggahan",description:"Ubah sanggahan menjadi peluang:",points:["Mengidentifikasi masalah pelanggan yang paling umum","Menganalisis tanggapan sanggahan dari para pemangku kepentingan","Playbook dengan respons yang telah terbukti.","Latih tim Anda dengan perputaran yang efektif"]},product:{title:"Wawasan Produk",description:"Mempromosikan pengembangan produk melalui umpan balik pelanggan:",points:["Mengidentifikasi fitur yang paling banyak diminta","Memahami tantangan terhadap fungsionalitas saat ini","Temukan kasus penggunaan yang tidak terduga","Memprioritaskan peta jalan berdasarkan kebutuhan pelanggan"]},compliance:{title:"Kepatuhan & Kualitas",description:"Memastikan kepatuhan terhadap peraturan dan kualitas:",points:["Memantau pengungkapan yang diperlukan dan bahasa hukum","Menandai percakapan yang mungkin melanggar kebijakan","Melacak kepatuhan skrip dan praktik terbaik","Membuat laporan kepatuhan untuk audit"]}}},advancedFeatures:{title:"Fitur Lanjutan",items:[{title:"pengambilan yang ambigu",description:"Secara otomatis mendeteksi variasi kata kunci, sinonim, dan istilah terkait tanpa konfigurasi manual."},{title:"analisis historis",description:"Menganalisis tren kata kunci dari waktu ke waktu untuk mengidentifikasi pola musiman dan topik yang muncul."},{title:"Peringatan waktu nyata",description:"Dapatkan notifikasi langsung ketika kata kunci penting disebutkan dalam percakapan langsung."}]}},gx={hero:{badges:["Perusahaan","LLM lokal","Pengoperasian server internal"],title:"Konstruksi LLM di Tempat (On-Premise)",description:"Mengoperasikan LLM di lingkungan Anda sendiri, menyimpan data di luar perusahaan"},mission:{title:"Gambaran Umum Layanan Konstruksi LLM di Lokasi",paragraphs:["Untuk perusahaan yang tidak dapat mengeluarkan data rahasia ke luar perusahaan, kami membangun lingkungan LLM yang dapat diselesaikan di server mereka sendiri atau di cloud pribadi. Solusi ini sangat ideal untuk organisasi yang membutuhkan manajemen informasi yang ketat, seperti lembaga keuangan, lembaga medis, lembaga pemerintah, dan lembaga penelitian, karena tidak menggunakan API publik dan semua pemrosesan diselesaikan di dalam infrastruktur yang dikelola pelanggan.","Kami menyediakan dukungan komprehensif mulai dari pemilihan model, konstruksi server, pengoptimalan mesin inferensi, dan pemantauan operasi, menggunakan model open-weight yang tersedia secara komersial seperti Llama 3.3, Qwen 2.5, Mistral, dan DeepSeek dengan mesin inferensi berkinerja tinggi seperti vLLM, Ollama, dan SGLang. Model-model ini dijalankan pada mesin inferensi berkinerja tinggi seperti vLLM, Ollama, dan SGLang untuk menyediakan lingkungan inferensi dengan latensi rendah dan throughput tinggi.","Kami mendukung berbagai macam kasus penggunaan seperti integrasi dengan sistem yang sudah ada, chatbot, RAG internal, ringkasan dokumen, dan pembuatan kode. Kami dapat mendesain untuk memenuhi kebutuhan perusahaan, termasuk spesialisasi bisnis melalui penyempurnaan model, pengoptimalan GPU NVIDIA / Apple Silicon, desain multi-penyewa, konstruksi gateway API, dan banyak lagi."]},summary:{title:"Tren LLM On-Premise",paragraphs:["Pada tahun 2025-2026, utilitas LLM lokal meningkat pesat. Kinerja model berbobot terbuka telah mengungguli model tertutup, dan jumlah model yang ditawarkan dengan lisensi y

244ang tersedia secara komersial telah secara signifikan menurunkan rintangan untuk operasi internal.","Model dengan parameter 8B hingga 70B telah mencapai tingkat praktis untuk banyak tugas bisnis, kuantisasi GGUF memungkinkan inferensi kecepatan tinggi bahkan pada GPU konsumen.","Untuk mesin inferensi, Continuous Batching dan PagedAttention dari vLLM secara dramatis meningkatkan hasil, dan RadixAttention dari SGLang mempercepat interaksi multi-belokan yang kompleks. Ollama telah menjadi standar de facto untuk lingkungan pengembangan lokal, dan llama.cpp adalah Apple Silicon yang dioptimalkan untuk inferensi cepat pada Mac seri M.","Dalam adopsi perusahaan, selain kedaulatan data dan kepatuhan, optimalisasi biaya jangka panjang (beralih dari penagihan API bayar sesuai penggunaan) semakin menjadi motivasi untuk adopsi. Konfigurasi hibrida (lokal untuk pemrosesan sensitif dan API cloud untuk pemrosesan tujuan umum) telah menjadi pilihan umum."],icons:[{label:`Pribadi
245Awan`},{label:`Data
246Kedaulatan`},{label:`Biaya
247Optimalisasi`},{label:`Perusahaan
248Keamanan`}]},features:{title:"Fitur desain dan pengembangan",subtitle:"Desain & Pengembangan",items:[{title:"Desain infrastruktur dan pengoptimalan GPU",description:"Rancang konfigurasi perangkat keras yang optimal untuk beban kerja dan anggaran Anda, termasuk NVIDIA A100/H100, RTX 4090/5090, dan Apple Silicon (M2 Ultra/M3 Max/M4 Max), dan ciptakan lingkungan inferensi yang memanfaatkan kinerja GPU sepenuhnya melalui pengoptimalan CUDA, MPS, dan Metal. Pengoptimalan CUDA, MPS, dan Metal untuk memaksimalkan performa GPU."},{title:"Pemilihan dan pengoptimalan model",description:"Pilih model optimal dari Llama, Qwen, Mistral, DeepSeek, dll. dengan mempertimbangkan karakteristik tugas, persyaratan bahasa, dan keterbatasan sumber daya. Kuantisasi (GGUF/AWQ/GPTQ), optimalisasi cache KV, dan penyetelan pemrosesan batch digunakan untuk mencapai akurasi dan keluaran."},{title:"Konstruksi mesin inferensi",description:"Pilih dan buat mesin inferensi seperti vLLM, SGLang, Ollama, TGI, dll. sesuai dengan aplikasi Anda, dan terapkan teknologi terbaru seperti Continuous Batching, Speculative Decoding, dan Prefix Caching untuk menyediakan API inferensi dengan latensi rendah dan throughput tinggi. Kami menyediakan API inferensi latensi rendah dan throughput tinggi."},{title:"Operasi, Pemantauan dan Keamanan",description:"Menerapkan pemantauan metrik inferensial dengan Prometheus/Grafana, kontrol akses dengan API gateway, log audit, penskalaan otomatis, dan pemulihan kegagalan. Membangun lingkungan operasi produksi yang memenuhi persyaratan keamanan perusahaan."}]},techStack:{title:"Tumpukan Teknologi: LLM di tempat",subtitle:"Platform & Teknologi",categories:{openWeightModels:{title:"Model Berat Terbuka",items:["Llama 3.3 70B / 8B","Qwen 2.5 72B / 32B / 7B","Mistral Large / Nemo","DeepSeek V3 / R1","Gemma 2 27B / 9B","Phi-4","Perintah R (Cohere)"]},japaneseModels:{title:"Model khusus Jepang",items:["ELYZA-jepang-Llama-2","Menelan 70B / 13B","Jepang-Llama-3","Qwen-2.5-Japanese","Karakuri-LM"]},inferenceEngine:{title:"Mesin Inferensi",items:["vLLM (Continuous Batching)","SGLang (RadixAttention)","Ollama (pengembangan lokal)","llama.cpp (Apple Silicon)","TGI (Wajah Memeluk)","TensorRT-LLM (NVIDIA)","ExLlamaV2 (spesialisasi kuantisasi)"]},quantization:{title:"Kuantisasi",items:["GGUF (standar llama.cpp)","AWQ (presisi tinggi 4bit)","GPTQ (pengoptimalan GPU)","bit dan byte (8bit / 4bit)","EETQ (Kuantisasi Efisiensi Efisien)"]},hardware:{title:"Perangkat keras",items:["NVIDIA H100 / A100","NVIDIA L40S / A10G","RTX 4090 / 5090","Apple Silicon M2 Ultra","Apple Silicon M3/M4 Max","AMD MI300X"]}

248,infrastructure:{title:"Infrastruktur",items:["Docker / Kubernetes","Server NVIDIA Triton","Gerbang API (Kong / Utusan)","Prometheus / Grafana","Cloud Pribadi (VMware / OpenStack)","Dukungan Lingkungan yang Terhalang Udara"]}},updateNote:{title:"Poin pembaruan utama untuk tahun 2026",description:"Pada model bobot terbuka, Llama 3.3 mencapai performa tujuan umum kelas GPT-4 dan sangat meningkatkan performa bahasa Jepang; seri Qwen 2.5 memperluas pilihan dengan berbagai ukuran dan performa yang seimbang; DeepSeek V3/R1 menarik perhatian karena performa biaya inferensinya; serta mesin inferensi vLLM 2.0 Chunked Prefill dan SGLang's RadixAttention mempercepat dialog multi-belokan. Pada mesin inferensi, Chunked Prefill vLLM 2.0 dan RadixAttention SGLang mempercepat interaksi multi-belokan; optimalisasi Apple Silicon dari llama.cpp membuat inferensi model 70B menjadi praktis di M4 Max; dan teknologi Quantization menawarkan ketepatan 4-bit dan kinerja tinggi. Teknik kuantisasi mencapai presisi 4-bit dan kecepatan inferensi, dan GGUF menjadi format standar de facto."}},cta:{title:"Hubungi Kami",description:`Kami dapat berkonsultasi dengan Anda mulai dari pengaturan persyaratan.
249Konfigurasi PoC dan LLM lokal juga dapat dipertimbangkan.`,primaryButton:"Hubungi Kami",secondaryButton:"Konsultasi Teknis"}},hx={hero:{badges:["Perusahaan","RAG","pembuatan ekstensi pencarian"],title:"Pengembangan Vektor / RAG",description:"Membangun sistem RAG untuk menghubungkan pengetahuan internal dengan LLM"},mission:{title:"Ikhtisar Layanan Pengembangan Vektor / RAG",paragraphs:["Kami akan membangun sistem RAG (search extension generation) yang memungkinkan karyawan untuk mencari dan mengajukan pertanyaan dalam bahasa alami dengan menghubungkan aset pengetahuan yang terkumpul di perusahaan dengan LLM. Kami mengintegrasikan berbagai sumber data seperti manual, notulen, dokumen teknis, FAQ, riwayat Slack, Confluence, SharePoint, dan lain-lain untuk menciptakan lingkungan di mana pengetahuan organisasi dapat segera digunakan.","Kami memaksimalkan akurasi pencarian dan kualitas jawaban dengan menerapkan teknologi terbaru seperti Pencarian Hibrida (pencarian vektor + pencarian kata kunci), Pemeringkatan Ulang, pemotongan hierarki, penyaringan metadata, dan lain-lain, di samping pencarian dokumen sederhana. Kami dengan jelas menunjukkan sumber jawaban untuk menekan halcinasi dan memastikan keandalan.","Kami mendukung berbagai lingkungan termasuk AWS, GCP, Azure, dan on-premise, serta berintegrasi dengan infrastruktur autentikasi dan manajemen otoritas yang ada. Kami memberikan dukungan yang konsisten untuk menjaga kesegaran data (pembaruan diferensial dan pengindeksan ulang otomatis), menganalisis log penggunaan, dan terus meningkatkan akurasi."]},summary:{title:"Tren Teknologi RAG",paragraphs:['Antara tahun 2025 dan 2026, teknologi RAG akan memasuki fase "implementasi dasar hingga kualitas produksi". Selain pencarian vektor sederhana, Pencarian Hibrida, yang menggabungkan beberapa metode pencarian, Pemeringkatan Ulang, yang menyusun ulang hasil pencarian, dan Penguraian Kueri, yang menguraikan dan memproses pertanyaan yang rumit, telah menjadi konfigurasi standar.',"Strategi pemotongan juga telah berkembang, dengan pemotongan hirarkis yang mempertimbangkan struktur dokumen, pemotongan yang tumpang tindih, dan pemotongan multimodal yang memproses tabel dan gambar secara terpisah, yang semuanya berkontribusi pada peningkatan akurasi. Pengambilan Kontekstual (menambahkan informasi kontekstual ke dalam potongan-potongan) juga telah diakui secara luas sebagai sesuatu yang efektif.","Untuk pemilihan DB vektor, selain layanan terkelola seperti Pinecone, Weaviate, Qdrant, dan Milvus, PostgreSQL pgvector dan FAISS lokal menawarkan berbagai pilihan untuk memenuhi kebutuhan Anda. Model penyematan juga tersedia, termasuk text-Embedding-3-besar, Cohere Embed v3, dan model multibahasa.",'In enterprise deployments, authority management (ACL) linkage, PII masking, audit logs, and multi-tenant design are essential requirements, and there is a need to move from a "working PoC" to a "production-ready production system.'],icons:[{label:`Hibrida
250Pencarian`},{label:`Multi-modal
251RAG`},{label:`Agen
252RAG`},{label:`Perusahaan
253Keamanan`}]},features:{title:"Fitur desain dan pengembangan",subtitle:"Desain & Pengembangan",items:[{title:"Pengumpulan data dan pipeline prapemrose

253san",description:"Data extraction and cleansing from PDF, Word, Excel, HTML, Markdown, Slack, Confluence, SharePoint, etc. Multi-modal support including OCR, table structure analysis, and image caption generation. Maintain freshness with differential updates and scheduled execution."},{title:"Desain sistem pencarian presisi tinggi",description:"Construct high-precision search combining Vector Search + BM25 Hybrid Search, Reranking by Cohere Rerank / Cross-Encoder, and Query Expansion and Decomposition. Metadata filtering and permission-based access control are also implemented."},{title:"Response generation and quality control",description:"Improved answer quality with context optimization, Few-shot prompts, and Chain-of-Thought inference. Source clarification, confidence score display, and halcination detection are implemented to ensure reliability for business use."},{title:"Operation and improvement cycle",description:"We provide operational support including usage log analysis, search hit rate and response satisfaction monitoring, continuous improvement through feedback loops, A/B testing infrastructure, Embedding model updates, and chunking strategy reviews."}]},techStack:{title:"Technology stack: Vector / RAG",subtitle:"Platform & Technology",categories:{embeddingModels:{title:"Embedding Models",items:["OpenAI text-embedding-3-large","Cohere Embed v3","Voyage AI","Jina Embeddings v3","BGE-M3 (multilingual)","multilingual-e5-large","Japanese-specific models"]},vectorDatabases:{title:"Vector Databases",items:["Pinecone (Managed)","Weaviate (Hybrid Search)","Qdrant (high performance)","Milvus (large scale)","Chroma (lightweight)","PostgreSQL pgvector","FAISS (local)"]},chunking:{title:"Chunking & Processing",items:["hierarchical chunking","Semantic Chunking","Overlapping Chunks","Sentence Window","Contextual Retrieval","Table and chart processing","Multimodal support"]},search:{title:"Search & Retrieval",items:["Hybrid Search (Vector + BM25)","Cohere Rerank","Cross-Encoder Reranking","Query expansion and decomposition","HyDE (Hypothetical Document Generation)","Self-Query Retrieval","Multi-Query Retrieval"]}},updateNote:{title:"Poin-poin pembaruan utama untuk tahun 2026",description:"Dalam model Embedding, text-Embedding-3-large dan Cohere Embed v3 menjadi standar industri, dan kinerja bahasa Jepang meningkat secara signifikan; Contextual Retrieval terbukti efektif, dan menambahkan konteks ke chunk menjadi metode standar. Hybrid Search + Reranking menjadi konfigurasi wajib untuk pencarian, mengurangi jumlah konfigurasi pencarian vektor sederhana, dan Agentic RAG (agen secara dinamis memilih strategi pencarian) mulai diterapkan secara praktis. Dari sisi operasional, penekanan ditempatkan pada pembentukan siklus pengukuran dan perbaikan kualitas menggunakan kerangka kerja evaluasi RAG (RAGAS, LangSmith)."}},cta:{title:"Contact Us",description:`We can consult with you from the requirements arrangement.

254PoC and local LLM configurations can also be considered.`,primaryButton:"Contact Us",secondaryButton:"Technical Consultation"}},fx={hero:{badges:["Full Stack","Integrasi LLM","Desain API"],title:"Pengembangan Aplikasi Kecerdasan Buatan",description:"Desain dan bangun aplikasi praktis yang memanfaatkan LLM."},mission:{title:"Ringkasan Layanan Pengembangan Aplikasi Kecerdasan Buatan (AI)",paragraphs:["Kami menyediakan layanan desain dan pengembangan aplikasi kecerdasan buatan (AI) yang menciptakan nilai bisnis dengan menganalisis data yang diperoleh dari API eksternal menggunakan LLM, menghubungkan API dengan berbagai sumber data seperti CRM, ERP, SaaS, sensor IoT, dan media sosial, serta memasukkan data yang diperoleh ke dalam LLM secara real-time. Kami memfasilitasi analisis data canggih yang tidak dapat dicapai dengan alat BI konvensional, seperti pembangkitan laporan analitis menggunakan bahasa alami, deteksi anomali, prediksi tren, dan klasifikasi otomatis.","Tidak hanya integrasi API sederhana, tetapi juga integrasi sumber data yang beragam, pembersihan data, dan konversi ke format yang mudah diinterpretasikan oleh LLM melalui pemrosesan terstruktur. Prompt engineering memaksimalkan akurasi analisis dan menghasilkan output yang disesuaikan dengan kebutuhan bisnis. Kami mengembangkan aplikasi praktis yang menggabungkan data dan AI, seperti data penjualan untuk memprediksi negosiasi bisnis, log dukungan pelanggan untuk melakukan analisis VoC, dan data sensor untuk memberikan rekomendasi pemeliharaan peralatan.","Kami mendukung berbagai antarmuka, seperti aplikasi web, aplikasi seluler, integrasi Slack/Teams, dan pemrosesan batch berkala. Kami menyediakan dukungan yang konsisten mulai dari definisi persyaratan hingga desain API, implementasi integrasi LLM, dan operasi produksi."]},summary:{title:"Tren dalam Pengembangan Aplikasi Kecerdasan Buatan",paragraphs:["Dari tahun 2025 hingga 2026, pengembangan aplikasi kecerdasan buatan (AI) memasuki fase praktis berdasarkan integrasi API dan analisis model bahasa besar (LLM). Analisis data tidak terstruktur dan pembangkitan wawasan bahasa alami, yang sebelumnya tidak dapat ditangani oleh alat BI tradisional dan dashboard, mulai diintegrasikan ke dalam sistem perusahaan.","Yang patut diperhatikan adalah kematangan fungsi Output Terstruktur LLM, yang memungkinkan kontrol output yang sepenuhnya sesuai dengan JSON Schema, sehingga memungkinkan implementasi pipeline otomatis end-to-end. Dalam pipeline ini, data yang diperoleh melalui API dianalisis oleh LLM, dan hasilnya langsung diteruskan ke sistem berikutnya. Eksekusi multi-tahap dari Function Calling memungkinkan pengumpulan, integrasi, dan analisis data dari berbagai API ditangani dalam satu alur kerja.","Dalam infrastruktur pemrosesan data, Polars telah menjadi standar untuk pemrosesan berkecepatan tinggi, dan ketika digabungkan dengan pemrosesan streaming, hal ini memudahkan analisis real-time.","Dalam implementasi korporat, kebutuhan telah bergeser dari pengembangan PoC (Proof of Concept) satu kali menjadi desain yang skalabel untuk operasi produksi, yang memerlukan desain arsitektur komprehensif yang mencakup optimasi biaya, penanganan kesalahan, dan dukungan log audit."],icons:[{label:"Kecerdasan Buatan Agensi"},{label:"Protokol MCP"},{label:"Multi Agen"},{label:"Manusia dalam Lingkaran"}]},features:{title:"Fitur desain dan pengembangan",subtitle:"Desain & Pengembangan",items:[{title:"Integrasi API dan desain pengumpulan data",description:"Bangun infrastruktur pengumpulan data yang mendukung berbagai protokol seperti REST API, GraphQL, WebSocket, dan Webhook. Kami merancang alur pengumpulan data yang stabil dengan mengimplementasikan proses autentikasi (OAuth, API key, JWT), pembatasan laju, dan mekanisme retry. Kami juga mendukung integrasi data, penjadwalan, dan pengumpulan data secara selektif dari berbagai API."},{title:"Prasunting dan pengorganisasian data",description:"Mengonversi data mentah ke dalam format yang dapat dianalisis secara efisien oleh LLM, termasuk pemrosesan JSON, pembersihan data, normalisasi, dan penanganan nilai yang hilang, serta melakukan segmentasi dan ringkasan data secara o

254ptimal dengan mempertimbangkan batasan jendela konteks. Penugasan metadata memastikan jejak audit hasil analisis."},{title:"Analisis LLM dan desain prompt",description:"Maksimalkan akurasi analisis data dengan merancang templat prompt yang disesuaikan dengan kebutuhan bisnis Anda. Kami membangun pipeline analisis untuk klasifikasi, ringkasan, analisis sentimen, deteksi anomali, pembangkitan prediksi, dan sebagainya. Kami menggunakan Function Calling dan Structured Output untuk menghasilkan output dalam format yang dapat diintegrasikan dengan mudah ke sistem berikutnya."},{title:"Implementasi dan pengoperasian aplikasi",description:"Kami menyediakan hasil analisis melalui berbagai antarmuka, termasuk FastAPI, aplikasi web menggunakan Next.js, dll., bot Slack/Teams, dan eksekusi batch terjadwal. Dashboard, pemberitahuan peringatan, dan pembangkitan laporan otomatis diimplementasikan dan disediakan dalam bentuk yang dapat langsung digunakan oleh pengguna bisnis. 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264»­æ”¹è¿›ã€A/B 测试基础设施、嵌入模型更新和分块策略审查。"}]},techStack:{title:"技术堆栈：Vector / RAG",subtitle:"平台与技术",categories:{embeddingModels:{title:"嵌入模型",items:["OpenAI 文本嵌入-3-大号","Cohere Embed v3","航程人工智能","Jina 嵌入 v3","BGE-M3（多语种）","多语种-E5-大","日本专用机型"]},vectorDatabases:{title:"矢量数据库",items:["松果（托管）","Weaviate（混合搜索）","Qdrant（高性能）","米尔沃斯（大型）","色度（轻量级）","PostgreSQL pgvector","FAISS（当地）"]},chunking:{title:"分块和处理",items:["分层分块","语义分块","重叠块","句子窗口","上下文检索","表格和图表处理","多模式支持"]},search:{title:"搜索与检索",items:["混合搜索（矢量 + BM25）","Cohere Rerank","交叉编码器重新排名","查询扩展和分解","HyDE（假设文档生成）","自查询检索","多查询检索"]}},updateNote:{title:"2026 年的主要更新点",description:"在嵌入模型中，text-Embedding-3-large 和 Cohere Embed v3 成为企业标准，日语性能显著提高；上下文检索被证明是有效的，为块添加上下文成为标准方法。混合搜索 + 重排已成为搜索的必备配置，减少了单纯矢量搜索配置的数量，Agentic RAG（代理动态选择搜索策略）正在投入实际使用。在操作方面，重点是利用 RAG 评估框架（RAGAS、LangSmith）建立质量测量和改进周期。"}},cta:{title:"联系我们",description:`我们可以从需求安排开始与您协商。
265也可以考虑 PoC 和本地 LLM 配置。`,primaryButton:"联系我们",secondaryButton:"技术咨询"}},Fx={hero:{badges:["全栈","LLM 整合","应用程序接口设计"],title:"人工智能应用开发",description:"设计和构建利用 LLM 的实用应用程序"},mission:{title:"人工智能应用程序开发服务概述",paragraphs:["我们通过 LLM 分析从外部 API 获取的数据，将 API 与 CRM、ERP、SaaS、IoT 传感器和社交媒体等各种数据源连接起来，并将获取的数据实时输入 LLM，从而提供可创造业务价值的人工智能应用程序的设计和开发。我们实现了传统商业智能工具无法实现的高级数据分析，例如使用自然语言生成分析报告、异常检测、趋势预测和自动分类。","不仅仅是简单的应用程序接口集成，还包括多个数据源的集成、数据清理，以及通过结构化处理转换为便于 LLM 解释的格式。及时的工程设计可最大限度地提高分析的准确性，并根据业务需求生成专门的输出。我们建立了数据与人工智能相结合的实际应用，例如预测业务谈判的销售数据、执行 VoC 分析的客户支持日志，以及提供设备维护建议的传感器数据。","我们支持各种接口，如网络应用程序、移动应用程序、Slack/Teams 集成和定期批量处理。从需求定义到应用程序接口设计、LLM 集成实施以及生产运营，我们都能提供始终如一的支持。"]},summary:{title:"人工智能应用开发的趋势",paragraphs:['从 2025 年到 2026 年，人工智能应用开发正在进入基于 "API 集成 x LLM 分析 "的实用阶段。传统商业智能工具和仪表盘无法处理的非结构化数据分析和自然语言洞察力生成开始被纳入企业系统。',"特别值得一提的是，LLM 的结构化输出功能日趋成熟，可实现严格的 JSON 模式兼容输出控制，从而使端到端自动化管道成为现实，通过 API 获取的数据可由 LLM 进行分析，并将结果直接输入后续系统。功能调用的多阶段执行可在一个工作流中处理多个 API 的数据收集、集成和分析。","在数据处理基础设施中，Polars 已成为高速处理的标准，它与流式处理相结合，为实时分析提供了便利。","在企业实施过程中，需求已从一次性 PoC 开发转变为生产运营的可扩展设计，这就要求进行全面的架构设计，包括成本优化、错误处理和审计日志支持。"],icons:[{label:`代理
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270„구 및 규정 준수 요구 사항에 맞춤화된 생산 등급 AI 에이전트를 설계하고 구축하십시오."},onPremiseLLM:{title:"온프레미스 대규모 언어 모델 운영 - telet.ai",description:"강력한 데이터 제어, 예측 가능한 비용, 저지연 추론을 위해 현대적인 대규모 언어 모델(LLM)을 자체 환경 내에서 운영하세요."},vectorRAG:{title:"벡터 RAG 및 인덱스 설계 - telet.ai",description:"각 워크플로우에 맞춤화된 검색 파이프라인과 인덱스를 설계하여 근거 기반 답변의 품질을 향상시킵니다."},aiAppDevelopment:{title:"인공지능 애플리케이션 개발 - telet.ai",description:"LLM 기반의 실용적인 애플리케이션을 구축하세요—API, 데이터 및 비즈니스 워크플로와 통합됩니다."}},Ux={brand:"telet.ai",nav:{services:"서비스",portfolio:"포트폴리오",about:"조직 정보",contact:"연락처"}},_x={brandName:"telet.ai",tagline:"멀티 AI 커뮤니케이션",badge:"AI 기반 통화 인텔리전스",title:{line1:"로컬 및 방어,",line2:"클라우드에서 확장하세요,",line3:"에이전트에 의해 자동화됩니다."},description:"SLM의 엣지 추론은 데이터 주권을 보장하고, LLM의 클라우드 통합은 처리 능력을 무한대로 확장하며, 에이전틱 AI 아키텍처의 채택은 선도적인 기업에서 성과를 보이기 시작했습니다."},Hx={badge:"우리의 사명",title:"LLM의 접근성 향상",paragraphs:['정말 이렇게까지 할 수 있을까요?" --이러한 놀라움이 바로 우리가 목표로 하는 것입니다.',"대규모 언어 모델링(LLM)의 잠재력은 날로 확장되고 있습니다. 하지만 '기밀 데이터를 외부 API로 전송하는 것이 걱정된다', '고급 AI 애플리케이션은 대기업만 사용할 수 있다', '구현 비용이 만만치 않다' 등의 장벽을 허물고자 합니다.","저희는 고객의 환경 내에서 완성되는 로컬 LLM 구축을 전문으로 합니다. 매우 민감한 고객 정보를 외부에 노출시키지 않고 안전하게 활용할 수 있습니다. 데이터 보안을 보장하면서 AI의 이점을 극대화할 수 있습니다.","일상적인 문ì

270„œ 작성과 데이터 정리부터 전문 분석 및 의사 결정 지원까지 모든 작업을 처리합니다. telet.ai의 독자적인 에이전트 기술은 API 모델에 필적하는 정확도를 제공합니다.","LLM은 사람을 대체하지 않습니다. 사람들이 더 많은 창의력, 판단력, 전문성을 발휘할 수 있게 해주는 강력한 도구입니다. 우리는 기술을 특별한 것에서 일상적인 것으로, 즉 기술의 진정한 '소비자화'를 실현합니다.",'부담 없이 상담해 주세요. 고객과 함께 "무엇을 할 수 있을까요?"를 찾기 위해 함께 노력하겠습니다.'],animation:{label:"LLM",sublabel:"기술"}},$x={title:"서비스",subtitle:"비즈니스 서비스",services:[{title:"기업용 AI 에이전트 설계 및 개발",titleEn:"엔터프라이즈 AI 에이전트",description:"비즈니스 요구 사항, 내부 규칙 및 데이터 범위에 최적화된 AI 에이전트를 설계 및 개발하세요. 외부 API 유형 LLM부터 로컬 LLM까지 지원합니다.",badges:["개별 디자인","LLM 호환","비즈니스 전문화"],link:"세부 정보."},{title:"온-프로미스 LLM 서버",titleEn:"온프레미스 LLM 서버",description:"회사 전용 서버 내에서 7B~120B 규모의 LLM을 실행합니다. 외부 데이터 전송의 위험을 피하면서 안전하고 유연한 LLM 사용 환경을 제공합니다.",badges:["7B-120B 지원","API 키 발급","안전 설계"],link:"세부 정보."},{title:"벡터 RAG 및 인덱스 디자인",titleEn:"벡터 RAG 아키텍처",description:"비즈니스 콘텐츠와 분석 로직에 따라 RAG 데이터를 분리하고 최적화하세요. 빠르고 정확한 검색을 위한 색인 설계.",badges:["인덱스 분리","고속 검색","다국어 지원"],link:"세부 정보."},{title:"AI 애플리케이션 개발",titleEn:"AI 애플리케이션 개발",description:"기존 API와 외부 데이터를 활용하여 LLM에 의한 분석 및 생성을 핵심으로 하는 실용적인 AI 애플리케이션을 개발하세요.",badges:["API 연동","LLM 분석","통합 디자인"],link:"세부 정보."}]},Qx={title:"AI 기술을 활용한 업적 프로젝트",subtitle:"다국어 비서, 일기 예보, 로컬 LLM, 보안 모니터링 등 다양한 분야의 AI 개발 경험이 있습니다.",viewDetails:"세부 정보 보기 →.",items:[{title:"AI 다국어 어시스턴트",description:"RAG + LLM의 다국어 채팅 도우미. 일본어, 영어, 중국어 등 다국어 문의에 대해 매우 정확하게 답변합니다."},{title:"일기 예보 시스템",description:"날씨 데이터를 학습하여 실시간 예보를 생성합니다. 시계열 패턴 인식과 RAG를 사용하여 과거에 유사한 패턴을 검색함으로써 매우 정확한 예측을 생성합니다."},{title:"사내 서버 / On-Prem Local LLM",description:"개방형 WebUI를 공통 진입점으로 삼아 회사 내에서 바로 사용할 수 있는 비즈니스 운영 맞춤형 AI를 제공하며, Ollama/MLX를 통한 빠른 추론 레인, 각 UID에 대해 완전히 분리된 데이터 도메인을 제공합니다. 사내 문서는 영어 임베딩과 일본어 쿼리 자동 번역 기능을 갖춘 RAG로 캡처되며, 뉴스 요약, 문서 정리, 일상 업무와 같은 비즈니스 스킬이 안전하게 나란히 실행됩니다. 스킬 실행 시스템은 OpenClaw 컨테이너로 격리되어 있으며 Cloudflare 에지 및 로컬 Nginx의 3계층으로 보호됩니다. 모델 선택, 스킬 설계, 운영 정책 등을 종합적으로 지원하며, 배포한 날부터 바로 사용할 수 있는 형태로 귀사의 전용 AI를 가동합니다."},{title:"행성 간 궤도 도착 예측(AI)",description:"NASA API에서 행성 데이터를 가져와 LLM에서 궤도 계산을 해석합니다. 발사 기간, 소요 시간, 연료 효율성에 대한 보고서를 생성합니다."},{title:"부동산 데이터 AI 분석",description:"웹 스크래핑으로 부동산 정보를 수집하고 LLM + Python으로 분석하세요. 가격 동향, 시장 예측, 투자 수익률을 자동으로 생성합니다."},{title:"운세 AI(네 개의 기둥 운세)",description:"네 가지 운명의 기둥 로직을 Python으로 재현하고 LLM으로 해석합니다. 천문학 API에서 좌표와 시간을 획득하여 운세와 궁합에 대한 개인화된 평가를 받습니다."}

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270scription:"출발지와 목적지를 기준으로 버스와 철도를 조합한 경로를 검색하고 도착 시각, 소요 시간, 환승 횟수로 후보를 비교하는 시스템입니다. 버스 전용·철도 전용 필터, 주변 역과 정류장 검색, 다국어 화면을 제공하여 일상 이동과 여행에 맞는 경로 선택을 돕습니다.",techStack:["대중교통 경로 검색","버스·철도 연계","경로 비교","다국어 UI"]},"multilingual-assistant":{title:"AI 다국어 어시스턴트",description:"다국어 문의를 위한 지능형 채팅 도우미로 1,573개의 벡터 인덱스를 구축하고 조건부 분기, NG 단어 필터링, 키워드 트리거링을 통해 정밀한 답변 제어를 구현합니다. 언어 간 시맨틱 검색을 통해 일본어, 영어, 중국어 등 여러 언어로 된 사용자 질문에 대해 일관되고 정확한 답변을 생성합니다.",techStack:["RAG","벡터 검색","다국어 임베딩","LLM","조건부 분기 논리"]},"weather-forecast":{title:"날씨 API를 사용한 AI 일기 예보",description:"과거 기상 데이터를 학습하여 최근 기상 상황을 실시간으로 예측하는 인공지능 시스템을 개발했습니다. LLM은 기상청 API, 오픈웨더맵 등에서 얻은 과거 데이터를 분석하여 기온, 강수 확률, 풍속 등에 대한 단기 예보를 생성합니다. 시계열 데이터의 패턴 인식과 RAG의 과거 유사ì

270„± 패턴 검색을 결합하여 기존 통계 모델을 보완하는 높은 정확도의 예측을 달성합니다.",techStack:["날씨 API 연동","LLM","RAG","시계열 데이터 분석","실시간 처리"]},"local-llm":{title:"사내 서버 / On-Prem Local LLM",description:"개방형 WebUI를 공통 진입점으로 삼아 회사 내에서 바로 사용할 수 있는 비즈니스 운영 맞춤형 AI를 제공하며, Ollama/MLX를 통한 빠른 추론 레인, 각 UID에 대해 완전히 분리된 데이터 도메인을 제공합니다. 사내 문서는 영어 임베딩과 일본어 쿼리 자동 번역 기능을 갖춘 RAG로 캡처되며, 뉴스 요약, 문서 정리, 일상 업무와 같은 비즈니스 스킬이 안전하게 나란히 실행됩니다. 스킬 실행 시스템은 OpenClaw 컨테이너로 격리되어 있으며 Cloudflare 에지 및 로컬 Nginx의 3계층으로 보호됩니다. 모델 선택, 스킬 설계, 운영 정책 등을 종합적으로 지원하며, 배포한 날부터 바로 사용할 수 있는 형태로 귀사의 전용 AI를 가동합니다.",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"행성 간 궤도 도착 예측(AI)",description:"NASA API에서 행성 위치 데이터, 궤도 요소, 중력 파라미터를 얻어 행성 간 이동을 위한 최적의 출발 지점을 분석하는 시스템을 개발했습니다. API와 Python을 사용하여 호만 이동 궤도 및 중력 보조를 고려한 궤도 계산을 수행하고 발사 기간, 소요 시간, 연료 효율을 표시합니다. 교육 기관 및 우주 관련 기업의 시뮬레이션 툴로 활용되고 있습니다. *AI 기능은 현재 제거되었으며, API와 Python으로 계산이 진행 중입니다.",techStack:["NASA API (Horizons, SPICE)","궤도 역학 계산","Python","데이터 시각화"]},"real-estate":{title:"부동산 데이터 AI 분석",description:"웹 스크래핑을 통해 특정 지역의 부동산 정보를 수집하고 LLMê³¼ Python을 활용해 고급 데이터 분석을 수행하는 시스템을 개발했습니다. 부동산 가격, 평당 단가, 건물 연식, 역세권 거리 등의 변수를 추출하여 가격 추이 분석, 시장 예측, 투자 수익률 시뮬레이션을 자동으로 생성합니다. 또한, 비정형화된 부동산 설명에서 LLM을 이용해 특징을 추출하여 정량적 데이터와 결합한 종합적인 시장 분석을 제공합니다. *현재 두 번째 버전으로 제공(정식 버전은 별도 제공)",techStack:["웹 스크래핑(스크랩, 플레이라이터)","Python","판다","LLM 분석","데이터 시각화"]},"fortune-ai":{title:"네 개의 기둥 AI 감정 시스템",subtitle:"기술로 전통의 지혜를 재현합니다.",paragraphs:["점술의 네 기둥은 수천 년에 걸친 동양 점술의 정점입니다. 이 시스템은 전통적인 점술 로직을 파이썬으로 완벽하게 재현하고 LLM(대규모 언어 모델)을 사용한 해석 및 조언 생성을 결합했습니다. AI는 전문 점쟁이가 수행하는 감정 과정을 충실히 재현합니다.","날짜, 시간, 생년월일을 입력하면 시스템은 천문학 API에서 해당 시간의 일출 및 일몰 시간과 좌표 정보를 검색합니다. 이러한 데이터를 기반으로 황도, 십이지신, 오행의 균형을 정확하게 계산합니다. 사주명리학에 기반한 로직은 Python으로 색인화되고 통계 데이터 및 RAG(검색 증강 생성) 정보와 결합되어 사주명리 분석의 정확도를 높입니다.","계산된 라이프스타일 데이터는 LLM이 자연어로 알기 쉽게 설명해줍니다. 운의 흐름, 대인관계의 궁합, 본인에게 적합한 직업 등 개인별 맞춤 평가 결과가 생성됩니다. 전문 점술가에게 상담한 것과 같은 깊이 있는 답변을 얻을 수 있습니다.","현재 두 번째 버전으로 제공되고 있습니다. 기본 사주 분석과 운세 분석을 체험해 ë³¼ 수 있습니다. 더 자세한 분석과 다각적인 조언이 포함된 정식 버전은 별도로 구매할 수 있습니다."],techStack:["Python","천문학 API(일출-일몰, 시간대DB)","네 가지 기둥 논리","LLM","좌표 및 시간 계산"]}

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270„¤ì •ì—ì„œ 비활성화할 수 있습니다."},external:{title:"7. 외부 서비스 사용",description:"본 서비스는 다음과 같은 외부 서비스를 이용할 수 있습니다. 각 서비스의 개인정보 처리방침은 해당 링크를 참조하세요.",items:["Google Analytics (access analysis)","비즈니스 운영에 필요한 기타 클라우드 서비스"]},changes:{title:"8. 개인정보 보호정책 변경",description:"본 정책은 법령의 개정 또는 서비스 내용의 변경에 따라 사전 고지 없이 개정될 수 있습니다. 개정된 정책은 본 페이지에 게시됨과 동시에 효력이 발생합니다."},contact:{title:"9. 문의하기",description:"이 정책과 관련하여 궁금한 점이 있으면 다음 연락처로 문의하시기 바랍니다.",company:"telet.ai",contactLink:"문의 양식으로 이동"}}},sL={title:"이용 약관",subtitle:"본 이용 약관은 본 사이트 이용 및 상담 예약, 문의, telet.ai 정보 요청에 대한 이용 약관입니다.",lastUpdated:"마지막 업데이트: 2026년 1월 22일",intro:'본 이용 약관(이하 "약관")은 telet.ai가 운영하는 웹사이트(이하 "사이트")에 적용됩니다. 사이트는 주로 상담 예약, 문의 및 문서 요청을 위한 랜딩 페이지로 제공되며, 사이트에서 계정 생성 또는 유료 결제 기능을 제공하지 않습니다.',sections:{scope:{title:"1. 적용 범위",description:'본 이용약관은 사이트의 모든 방문자 및 사용자(이하 "사용자")에게 적용됩니다. telet.ai와 사용자 간에 별도의 NDA, 아웃소싱 계약, 개별 계약 등이 체결된 경우 해당 문서가 본 약관보다 우선하여 적용됩니다.'},prohibited:{title:"2. 금지 품목",description:"이용자는 사이트를 이용함에 있어 다음 각 호에 해당하는 행위를 해서는 안 됩니다.",items:["법령 또는 공공질서 및 미풍양속에 위반되는 행위","telet.ai 또는 제3자의 지적 재산권, 개인정보 또는 기타 권리나 이익을 침해하는 행위","무단 접속, 과도한 부하, 방해 행위 등을 통해 본 사이트 또는 관련 시스템의 운영을 방해하는 행위.","문의 양식 등에 허위 정보를 제출하거나 제3자를 사칭하는 행위.","telet.ai의 사전 허가 없이 스크래핑, 리버스 엔지니어링 또는 과도한 자동 액세스 수행"]},disclaimer:{title:"3. 면책 조항",description:'telet.ai는 본 사이트를 "있는 그대로" 및 "사용 가능한 대로" 제공합니다. 법이 허용하는 최대 한도 내에서 telet.ai는 본 사이트와 관련된 모든 보증을 부인합니다.',items:["이 사이트의 정보는 사전 통지 없이 변경되거나 삭제될 수 있습니다.","telet.ai는 이 사이트에 있는 정보의 정확성, 완전성 또는 특정 목적에 대한 적합성을 보장하지 않습니다.","telet.ai는 귀하가 이 사이트를 사용하거나 여기에 포함된 정보에 의존함으로써 발생하는 어떠한 손해에 대해서도 책임을 지지 않습니다.","상담 및 문의에 대한 답변은 일반적인 정보 제공만을 목적으로 하며 법률, 세무, 의료 또는 기타 전문적인 조언이 될 수 없습니다."]},ip:{title:"4. 지적 재산권",description:"이 사이트의 텍스트, 이미지, 디자인, 상표, 로고 등에 대한 모든 권리는 telet.ai 또는 정당한 권리 보유자에게 있습니다. 권리자의 허가 없이 이를 사용할 수 없습니다."},links:{title:"5. 외부 사이트 링크",description:"이 웹사이트에는 타사 웹사이트로 연결되는 링크가 포함되어 있을 수 있습니다. telet.ai는 링크된 사이트의 콘텐츠나 운영에 대해 책임을 지지 않습니다."},changes:{title:"6. 본 이용약관의 변경",description:"telet.ai는 필요에 따라 수시로 본 이용약관을 변경할 수 있는 권리를 보유합니다. 개정된 이용약관은 사이트에 게시됨과 동시에 효력이 발생합니다."},governingLaw:{title:"7. 준거법 및 관할권",description:"본 이용약관은 일본법을 준거법으로 합니다. 본 사이트와 관련하여 분쟁이 발생하는 경우 도쿄지방법원을 제1심 전속적 합의관할 법원으로 합니다."},contact:{title:"8. 문의하기",description:"본 이용약관과 관련하여 궁금한 점이 있는 경우 다음 연락처로 문의하시기 바랍니다.",company:"telet.ai",contactLink:"문의 양식으로 이동"}}}

270,rL={title:"규칙적인 동작",subtitle:"AI가 예측하는",description1:"기상 정보는 각 국가에서 실시간으로 제공됩니다. 지도 중앙에 귀하의 국가가 표시됩니다. 어제 날씨와 오늘 날씨는 실제 기록입니다 - 그 비구름은 그 날짜와 시간에 당신의 하늘에 있었을 것입니다.",description2:"하지만 이 애니메이션은 미래의 날씨 움직임도 애니메이션으로 표현합니다.",description3:"과거의 데이터를 벡터화하여 빠른 속도로 패턴을 검색합니다. 주변 환경으로부터 날씨 패턴도 예측할 수 있습니다. 마찬가지로 환경의 요소(i)와 과거 실적의 고급 통계 값을 계산하고 AI가 이를 분석하도록 하여 수확량, 생산량, 매출을 예측할 수 있습니다.",refresh:"새로 고침(예: 메모리)",loading:"날씨 지도 로드 중..."},oL={hero:{badges:["엔터프라이즈","로컬 LLM","사용자 지정 에이전트"],title:"기업용 AI 에이전트 설계 및 개발",description:"비즈니스 요구 사항에 맞는 전용 AI 에이전트 설계 및 구축"},mission:{title:"맞춤형 AI 에이전트 개발 서비스",description:"설계부터 구현, 운영까지 귀사의 고유한 비즈니스 문제를 해결하는 맞춤형 AI 에이전트를 지속적으로 개발합니다. 기존 미션 크리티컬 시스템 및 SaaS 환경과의 호환성을 강조한 아키텍처 설계로 API, 데이터베이스, 내부 도구와의 원활한 통합이 가능합니다. telet.ai의 엔지니어들은 LangChain, AutoGen, CrewAI 및 기타 주요 프레임워크에 익숙하며, 귀사의 기술 스택과 운영 구조에 가장 적합한 솔루션을 제안할 것입니다. 일회성 챗봇 개발뿐만 아니라 여러 에이전트가 서로 협력하여 작업을 수행하는 멀티 에이전트 시스템과 외부 도구와 연동되는 에이전트 워크플로우도 구축합니다. 구축 후 지속적인 개선, 모델 업데이트, 운영 모니터링 시스템 유지보수 등 장기적인 파트너십을 통해 AI 에이전트가 제공하는 비즈니스 가치를 극대화할 수 있도록 지원합니다.",imageAlt:"맞춤형 AI 에이전트 개발 서비스"},summary:{title:"에이전트 개발 동향",paragraphs:["2025년부터 2026년까지 인공지능 에이전트 개발은 실증 단계에서 본격적인 기업 도입 단계로 넘어가고 있습니다. '에이전트 AI'라는 개념이 자리를 잡으면서 단순한 챗봇을 넘어선 자율적인 업무 수행 시스템이 다양한 산업에서 운영되기 시작했습니다.","브라우저와 데스크톱을 직접 제어하는 '컴퓨터 사용 에이전트'는 OpenAI의 Operator, Anthropic의 Computer Use, Google의 Project Mariner와 같이 실용화되고 있습니다. 모델 컨텍스트 프로토콜(MCP)은 급성장하는 에코시스템으로, 수천 개의 도구와 플러그 앤 플레이 방식으로 통합할 수 있습니다.","프로젝트 관리, 고객 지원, 데이터 분석과 같은 복잡한 작업을 수행하기 위해 전문 에이전트가 함께 작업하는 멀티 에이전트 아키텍처가 성숙기에 이르렀습니다. A2A(에이전트 간) 프로토콜 표준화로 인해 여러 공급업체 간의 에이전트 협업도 진전을 이루었습니다.","거버넌스 모델은 '휴먼 인 더 루프'에서 기업 구현을 위한 '휴먼 온 더 루프'로 진화했으며, 에이전트 사후 결정 감사 메커니즘과 위험 수준에 따른 자동 에스컬레이션을 표준 기능으로 채택하여 금융, 의료, 제조 등 규제 산업에서 채택이 가속화되고 있습니다. 금융, 의료, 제조 등 규제 대상 산업에서 이 시스템의 도입이 가속화되고 있습니다."]},features:{title:"디자인 및 개발",subtitle:"디자인 및 개발",items:[{title:"비즈니스 요구 사항에 따른 에이전트 설계",description:"비즈니스 흐름 분석에서 사용 사례를 추출하고, 업무를 세분화하여 에이전트의 책임을 명확히 합니다. 단일 에이전트로 처리를 완료할 수 있는지 아니면 여러 에이전트의 조정이 필요한지 판단하고 ReAct, Plan-and-Execute, Reflexion과 같은 최적의 추론 패턴을 선택합니다."},{title:"RAG/도구/워크플로 통합",description:"임베딩 생성, 청크 전략, 하이브리드 검색을 결합한 매우 정확한 RAG 파이프라인, MCP 호환 도구 정의로 외부 API 및 데이터베이스에 대한 표준화된 연결, LangGraph와 CrewAI를 사용한 다단계 워크플로우 오케스트레이션을 제공합니다. LangGraph와 CrewAI로 다단계 워크플로를 오케스트레이션하세요."},{title:"보안 및 권한 부여 설계",description:"에이전트 기능에 대한 접근 제어는 RBAC/ABAC, 프롬프트 인젝션 대응, PII 탐지 및 마스킹을 통해 구현됩니다. 모든 입출력에 대한 감사 로그가 보관되며 SOC2 및 ISO27001을 준수하는 환경 운영을 지원합니다."},{title:"운영 및 모니터링을 포함한 구현",description:"LangSmith, Langfuse 등으로 추적 시각화를 구현하고 지연 시간, 토큰 소비량, 오류율을 실시간으로 모니터링하세요. 이상 징후 탐지 알림, A/B 테스트 인프라, 모델 업데이트 시 회귀 테스트 등 지속적인 개선 주기를 구축하세요."}]},techStack:{title:"기술 스택",subtitle:"플랫폼 및 기술",categories:{agentFramework:{title:"에이전트 프레임워크",items:["AutoGen 0.4 / AG2","CrewAI","LangGraph","OpenAI 에이전트 SDK","인조 MCP","Google ADK (Agent Development Kit)","시맨틱 커널","독점 에이전트 플랫폼"]},apiLayer:{title:"API 레이어",items:["FastAPI / 라이트스타","gRP

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271…의 동기가 되고 있습니다. 하이브리드 구성(민감한 처리를 위한 로컬 및 범용 처리를 위한 클라우드 API)이 일반적인 옵션이 되었습니다."],icons:[{label:`비공개
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275,infrastructure:{title:"인프라",items:["도커/쿠버네티스","NVIDIA Triton 서버","API 게이트웨이(콩/엔보이)","프로메테우스 / 그라파나","프라이빗 클라우드(VMware/오픈스택)","에어 갭 환경 지원"]}},updateNote:{title:"2026년 주요 업데이트 사항",description:"개방형 모델에서 Llama 3.3은 GPT-4급 범용 성능을 달성하고 일본어 성능을 크게 향상시켰으며, Qwen 2.5 시리즈는 다양한 크기와 균형 잡힌 성능으로 선택의 폭을 넓히고, DeepSeek V3/R1은 추론 비용 성능으로 주목을 받고 있으며, vLLM 2.0 Chunked Prefill과 SGLang의 RadixAttention 추론 엔진은 멀티 턴 대화 속도를 높입니다. 추론 엔진에서 vLLM 2.0의 청크드 프리필과 SGLang의 RadixAttention은 멀티턴 상호작용 속도를 높이고, llama.cpp의 Apple Silicon 최적화는 M4 Max에서 70B 모델 추론을 실용적으로 구현하며, 양자화 기술은 4비트 정밀도와 고성능을 제공합니다. 양자화 기술은 4비트 정밀도와 추론 속도를 모두 달성했으며, GGUF는 사실상 표준 포맷이 되었습니다."}},cta:{title:"문의하기",description:`요구 사항 준비부터 상담해 드립니다.
276PoC 및 로컬 LLM 구성도 고려할 수 있습니다.`,primaryButton:"문의하기",secondaryButton:"기술 상담"}},pL={hero:{badges:["엔터프라이즈","RAG","검색 확장자 생성"],title:"벡터/RAG 개발",description:"내부 지식을 LLM과 연결하기 위한 RAG 시스템 구축"},mission:{title:"벡터/RAG 개발 서비스 개요",paragraphs:["사내에 축적된 지식 자산을 LLM과 연계하여 직원들이 자연어로 검색하고 질문할 수 있는 RAG(검색 확장 생성) 시스템을 구축합니다. 매뉴얼, 회의록, 기술문서, FAQ, 슬랙 히스토리, 컨플루언스, 쉐어포인트 등 다양한 데이터 소스를 통합하여 조직의 지식을 즉시 활용할 수 있는 환경을 조성합니다.","단순 문서 검색 외에도 하이브리드 검색(벡터 검색+키워드 검색), 리랭킹, 계층적 청킹, 메타데이터 필터링 등 최신 기술을 적용해 검색 정확도와 응답 품질을 극대화합니다. 답변의 출처를 명확히 표시하여 왜곡을 억제하고 신뢰성을 확보합니다.","AWS, GCP, Azure, 온프레미스 등 다양한 환경을 지원하며 기존 인증 인프라 및 권한 관리와 통합됩니다. 데이터 최신성 유지(차등 업데이트 및 자동 재인덱싱), 사용 로그 분석, 지속적인 정확도 향상을 위한 일관된 지원을 제공합니다."]},summary:{title:"RAG 기술 동향",paragraphs:["2025년에서 2026년 사이에 RAG 기술은 '기본 구현에서 생산 품질로' 단계로 이동하고 있습니다. 단순한 벡터 검색 외에도 여러 검색 방법을 결합한 하이브리드 검색, 검색 결과의 순위를 재조정하는 리랭킹, 복잡한 질문을 분해하여 처리하는 쿼리 분해가 표준 구성으로 자리 잡았습니다.","문서 구조를 고려한 계층적 청킹, 중복 청킹, 표와 그림을 따로 처리하는 멀티모달 청킹 등 청킹 전략도 진화하여 정확도 향상에 기여하고 있습니다. 문맥 검색(청크에 문맥 정보를 추가하는 기능)도 효과적인 것으로 널리 인정받고 있습니다.","벡터 DB 선택의 경우, Pinecone, Weaviate, Qdrant, Milvus와 같은 관리형 서비스 외에도 PostgreSQL pgvector 및 로컬 FAISS가 고객의 요구 사항을 충족할 수 있는 다양한 옵션을 제공합니다. 텍스트 임베딩-3-large, Cohere Embed v3 및 다국어 모델을 포함한 임베딩 모델도 사용할 수 있습니다.","엔터프라이즈 배포에서는 권한 관리(ACL) 연동, PII 마스킹, 감사 로그, 멀티테넌트 설계가 필수 요건이며, '작동 중인 PoC'에서 '프로덕션 준비된 프로덕션 시스템'으로 전환해야 할 필요가 있습니다."],icons:[{label:`하이브리드
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280,features:{title:"디자인 및 개발 기능",subtitle:"디자인 및 개발",items:[{title:"데이터 수집 및 전처리 파이프라인",description:"PDF, Word, Excel, HTML, Markdown, Slack, Confluence, SharePoint 등에서 데이터 추출 및 정리. OCR, 표 구조 분석, 이미지 캡션 생성을 포함한 멀티모달 지원. 차등 업데이트 및 예약 실행으로 최신성을 유지하세요."},{title:"고정밀 검색 시스템 설계",description:"벡터 검색 + BM25 하이브리드 검색, 코히어 리랭크/크로스 인코더를 통한 리랭크, 쿼리 확장 및 분해를 결합한 고정밀 검색을 구축합니다. 메타데이터 필터링과 권한 기반 액세스 제어도 구현됩니다."},{title:"응답 생성 및 품질 관리",description:"문맥 최적화, 단답형 프롬프트, 연쇄 추론으로 답변 품질이 향상되었습니다. 출처 설명, 신뢰도 점수 표시, 반감기 감지 기능이 구현되어 업무용으로 신뢰성을 보장합니다."},{title:"운영 및 개선 주기",description:"사용 로그 분석, 검색 적중률 및 응답 만족도 모니터링, 피드백 루프를 통한 지속적인 개선, A/B 테스트 인프라, 임베딩 모델 업데이트, 청킹 전략 검토 등의 운영 지원을 제공합니다."}]},techStack:{title:"기술 스택: 벡터/RAG",subtitle:"플랫폼 및 기술",categories:{embeddingModels:{title:"모델 임베딩",items:["OpenAI 텍스트 임베딩-3-large","Cohere Embed v3","Voyage AI","지나 임베딩 v3","BGE-M3(다국어)","다국어-E5-대형","일본어 전용 모델"]},vectorDatabases:{title:"벡터 데이터베이스",items:["솔방울(관리형)","Weaviate(하이브리드 검색)","Qdrant(고성능)","밀버스(대규모)","크로마(경량)","PostgreSQL pgvector","FAISS(현지)"]},chunking:{title:"청크 및 처리",items:["계층적 청킹","시맨틱 청킹","겹치는 청크","문장 창","문맥 검색","표 및 차트 처리","멀티모달 지원"]},search:{title:"검색 및 검색",items:["하이브리드 검색(벡터 + BM25)","코히어 리랭크","크로스 인코더 리랭크","쿼리 확장 및 분해","HyDE(가상 문서 생성)","자체 쿼리 검색","다중 쿼리 검색"]}},updateNote:{title:"2026년 주요 업데이트 사항",description:"임베딩 모델에서는 텍스트 임베딩-3-large와 Cohere Embed v3가 기업 표준이 되어 일본어 성능이 크게 향상되었고, 문맥 검색이 효과가 입증되어 청크에 문맥을 추가하는 것이 표준 방식이 되었습니다. 하이브리드 검색 + 재랭크가 검색의 필수 구성이 되어 단순 벡터 검색만 구성하는 경우가 줄어들었고, 에이전트 RAG(에이전트가 동적으로 검색 전략을 선택하는 방식)가 실용화되고 있습니다. 운영 측면에서는 RAG 평가 프레임워크(RAGAS, LangSmith)를 사용하여 품질 측정 및 개선 주기를 확립하는 데 중점을 두고 있습니다."}},cta:{title:"문의하기",description:`요구 사항 준비부터 상담해 드립니다.
281PoC 및 로컬 LLM 구성도 고려할 수 있습니다.`,primaryButton:"문의하기",secondaryButton:"기술 상담"}},gL={hero:{badges:["전체 스택","LLM 통합","API 디자인"],title:"AI 애플리케이션 개발",description:"LLM을 활용하는 실용적인 애플리케이션 설계 및 구축"},mission:{title:"AI 애플리케이션 개발 서비스 개요",paragraphs:["외부 API로부터 획득한 데이터를 LLM으로 분석하고, CRM, ERP, SaaS, IoT 센서, 소셜 미디어 등 다양한 데이터 소스와 API를 연동하며, 획득한 데이터를 실시간으로 LLM에 공급하여 비즈니스 가치를 창출하는 AI 애플리케이션의 설계 및 개발을 제공합니다. 자연어를 활용한 분석 보고서 생성, 이상 징후 탐지, 트렌드 예측, 자동 분류 등 기존 BI 도구로는 불가능한 고급 데이터 분석이 가능합니다.","단순한 API 통합뿐만 아니라 여러 데이터 소스의 통합, 데이터 정제, 구조화된 처리를 통해 LLM이 해석하기 쉬운 형식으로의 변환도 가능합니다. 신속한 엔지니어링을 통해 분석의 정확성을 극대화하고 비즈니스 요구사항에 특화된 결과물을 생성합니다. 비즈니스 협상 예측을 위한 영업 데이터, VoC 분석을 위한 고객 지원 로그, 장비 유지보수 권장 사항을 제공하기 위한 센서 데이터 등 데이터와 AI를 결합한 실용적인 애플리케이션을 구축합니다.","웹 애플리케이션, 모바일 애플리케이션, Slack/Teams 연동, 주기적 배치 처리 등 다양한 인터페이스를 지원합니다. 요구사항 정의부터 API 설계, LLM 통합 구현, 프로덕션 운영까지 일관된 지원을 제공합니다."]},summary:{title:"AI 애플리케이션 개발 동향",paragraphs:["2025년부터 2026년까지 'API 통합 x LLM 분석'을 기반으로 한 AI 애플리케이션 개발이 실용화 단계에 접어들고 있습니다. 기존의 BI 도구와 대시보드로는 처리할 수 없었던 비정형 데이터 분석과 자연어 인사이트 생성이 엔터프라이즈 시스템에 통합되기 시작합니다.","특히 주목할 만한 점은 엄격한 JSON 스키마를 준수하는 출력 제어를 가능하게 하는 LLM의 구조화된 출력 기능의 성숙으로, API를 통해 수집한 데이터를 LLM에서 분석하고 그 결과를 후속 시스템에 직접 공급하는 엔드투엔드 자동화 파이프라인을 실현할 수 있게 되었다는 것입니다. 함수 호출의 다단계 실행을 통해 여러 API에 걸친 데이터 수집, 통합 및 분석을 단일 워크플로우에서 처리할 수 있습니다.","데이터 처리 인프라에서 Polars는 고속 처리의 표준으로 부상했으며, 스트리밍 처리와 결합하여 실시간 분석을 용이하게 합니다.","기업 구현의 경우, 일회성 PoC 개발에서 프로덕션 운영을 위한 확장 가능한 설계로 요구가 바뀌면서 비용 최적화, 오류 처리 및 감사 로그 지원을 포함하는 포괄적인 아키텍처 설계가 필요해졌습니다."],icons:[{label:`에이전트
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Cree valor a partir del diálogo.",sections:{product:{title:"Producto",features:"Característica",benefits:"introducción",testimonials:"Testimonios de clientes"},services:{title:"Servicio",agentDev:"Diseño y desarrollo de agentes de IA para empresas",onPremise:"servidor LLM on-premise",vectorRag:"Vector RAG y diseño de índices",aiApp:"Desarrollo de aplicaciones de IA"},company:{title:"Información de la organización",about:"Acerca de telet.ai",profile:"Perfil del representante",contact:"Contacte con nosotros"},process:{title:"Cómo trabajamos",hearing:"Audiencia / NDA",planning:"Planificación y AC",sampling:"Muestreo e inicio",development:"Desarrollo, pruebas y funcionamiento"}},copyright:"2026 telet.ai. Todos los derechos reservados.",legal:{privacy:"Política de privacidad",terms:"Condiciones de uso",cookies:"Política de cookies"}},SL={title:"Política de cookies",subtitle:"Esta sección describe las cookies y otras tecnologías (Cloudflare / Google Analytics) utilizadas en este sitio.",lastUpdated:"Última actualización: 22 de enero de 2026",intro:'telet.ai ("nosotros", "nos", "nuestro") ("Nosotros") puede utilizar cookies y tecnologías similares para proporcionar y mejorar este Sitio y para garantizar su seguridad. No establecemos nuestras propias cookies de seguimiento especiales con fines publicitarios (aunque Cloudflare y Google Analytics pueden establecer cookies o tecnologías similares de acuerdo con las especificaciones de sus respectivos servicios).',sections:{what:{title:"1. ¿qué es una galleta?",description:"Las cookies son pequeños archivos de texto que se almacenan en su navegador cuando visita un sitio web."},howWeUse:{title:"Finalidad de nuestro uso de cookies, etc.",description:"Podemos utilizar cookies, etc. principalmente para los siguientes fines",items:["Para estabilizar la visualización y el funcionamiento del sitio","Detectar e impedir el acceso no autorizado y el tráfico malicioso","Para conocer el estado de los accesos y ayudar a mejorar el sitio"]},googleAnalytics:{title:"3. uso de Google Analytics",description:"Utilizamos Google Analytics 4 (GA4) proporcionado por Google LLC para el análisis de acceso.",items:["La información recopilada puede incluir páginas vistas, tiempo empleado, fuente de referencia, información sobre el dispositivo y el navegador, etc.","No utilizamos directamente información de identificación personal (nombre, dirección de correo electrónico, etc.) para enviarla a Google Analytics.","El tratamiento de los datos por parte de Google está sujeto a las condiciones y políticas de Google."]},cloudflare:{title:"4. uso de Cloudflare",description:"Utilizamos los servicios de Cloudflare, Inc. para la entrega y las medidas de seguridad de este Sitio. Cloudflare puede establecer y hacer referencia a cookies, etc. para fines como la protección DDoS y la prevención de bots.",items:["El propósito principal es la seguridad y la optimización de la comunicación, y no está destinado a ser rastreado para la entrega de anuncios","El tratamiento de datos por parte de Cloudflare está sujeto a los términos y políticas de Cloudflare"]},noSpecialCookies:{title:"5. nuestras galletas especiales",description:"No instalamos nuestras propias cookies especiales con fines de publicidad orientada al comportamiento, etc. El uso de cookies, etc. se limita principalmente al ámbito del funcionamiento del sitio, la seguridad y el análisis de acceso."},choices:{title:"6. gestión de cookies (opt-out)",description:"Si se desactivan las cookies, es posible que algunas funciones no estén disponibles.",items:["Las cookies pueden eliminarse o desactivarse en la configuración del navegador","Si desea inhabilitar la medición por parte de Google Analytics, utilice el método de inhabilitación proporcionado por Google.","El uso del modo privado de un navegador, etc., puede limitar la información almacenada."]},changes:{title:"7. cambios en esta política",description:"Podemos revisar esta política en respuesta a modificaciones legislativas y reglamentarias o a cambios en los servicios utilizados. La política revisada entrará en vigor cuando se publique en esta página."},contact:{title:"8. contacto",description:"Si tiene alguna pregunta sobre esta política, póngase en contacto con nosotros en",company:"telet.ai",contactLink:"Ir al formulario de consulta"}}},IL={hero:{title:"Acerca de telet.ai",subtitle:"Un cuidadoso apoyo al desarrollo de la IA que sólo puede proporcionar un equipo pequeño y personalizado."},story:{title:"Quiénes somos",content:"telet.ai comenzó con una pregunta. ¿Por qué hay tantas empresas que no aprovechan las innumerables interacciones entre ellas y sus clientes? El equipo, dirigido por un representante con experiencia en investigación de IA y desarrollo de softw

286are empresarial, se creó para abordar esta cuestión de frente. No somos una gran organización. Por eso somos capaces de afrontar sus retos en profundidad y proponer sólo las soluciones realmente necesarias. Nuestro punto fuerte es nuestra capacidad para diseñar arquitecturas híbridas que integran LLM local y LLM en la nube. Podemos proteger los datos confidenciales de su empresa en un entorno dedicado en la nube mientras realizamos inferencia avanzada de IA de forma escalable. Proporcionamos una CLI para desarrolladores y una interfaz de usuario de chat para el personal de primera línea, creando una base para el uso de la IA en toda su organización. Y nos centramos en ser la solución óptima en lugar de ser la más avanzada. Descartar funciones en función del presupuesto y revisar una sola configuración puede suponer una gran diferencia en el coste. Le entregamos lo que necesita, en la cantidad que necesita. Nuestro atento apoyo al desarrollo de IA sólo es posible porque somos pequeños y personales. Solucionar la escasez de personal y apoyar el crecimiento de su empresa. Esa es la promesa de telet.ai."},representative:{title:"Representante de telet.ai labo",name:"Keita Okuizumi",tagline:"Al cielo, al mar y al código: una pasión por el desarrollo de la inteligencia artificial nacida de una carrera inusual",paragraphs:['Como antiguo piloto privado (PPL) y titular de una licencia de barco, me he ocupado de la meteorología tanto en el aire como en el mar durante muchos años. En la cabina y en el puente de mando, observaba los cambios en el barómetro, descifraba las líneas de presión isobáricas y las comparaba con el movimiento del viento y las nubes. El sentido de "leer datos, juzgar y actuar" que desarrollé a través de este proceso repetitivo es el punto de partida para mí hoy en día.','Empecé a aprender a programar en Python a los 57 años. El impulso fue simple curiosidad, pero en cuanto empecé a escribir código me fascinaron las posibilidades de los agentes de IA. Creé aplicaciones con mis propias manos, las ejecuté y las mejoré. En el proceso, me di cuenta de algo. En proyectos anteriores, yo participaba como no ingeniero, y el desarrollo siempre lo hacía otra persona. Sin embargo, una vez que empecé a escribir el código yo mismo, empecé a ver la esencia del problema. Aquí está el cuello de botella" o "Este diseño no se puede ampliar": al tener el punto de vista de un ingeniero, la velocidad de realización de lo que quiero hacer ha aumentado drásticamente.',"Desarrollar servicios meteorológicos combinando la experiencia sobre el terreno con la ciencia de datos, construir agentes de IA para mejorar la eficiencia operativa y diversos proyectos experimentales. A medida que seguía trabajando con mis manos, me convencí no solo de que la tecnología era interesante, sino también de que tenía que haber gente que necesitara esto.","Nuestro objetivo no es perseguir la vanguardia. Nos enfrentamos a los problemas del cliente y a su presupuesto, y ofrecemos sólo lo que es realmente necesario a un coste razonable. No somos una gran organización, por lo que podemos ofrecer un apoyo al desarrollo adaptado a las necesidades de cada cliente. Queremos utilizar esta tecnología en beneficio de la sociedad: por eso lanzamos telet.ai."]},contact:{title:"Información de la organización",company:"telet.ai labo",ceo:"Representante: OKUIZUMI Keita",button:"Contacte con nosotros"},studios:{tokyo:{title:"Estudio de Tokio",address:"#301 First Hills A Musashikoganei, 3-3-21 Nukuiminamicho, Koganei-shi, Tokio 184-0014, Japón"}}},ML={title:"Cartera",subtitle:"Vea nuestro historial de utilización de la tecnología de IA",techStackLabel:"Pilas tecnológicas",viewProject:"Ver proyecto",privateProject:"Proyectos privados",visualizationLabel:"visualización de datos",cta:{title:"Contacte con nosotros",description:"No dude en ponerse en contacto con nosotros para hablar de su proyecto o para consultas técnicas.",button:"Póngase en contacto con nosotros"},projects:{"traffic-route-ai":{title:"Sistema de Selección de Rutas de Transporte con IA",subtitle:"Planificación de rutas de transporte público en Malasia",description:"Sistema que busca conexiones de autobús y tren entre un origen y un destino y compara las opciones por hora de llegada, duración y transbordos. Los filtros de solo autobús o tren, la búsqueda de estaciones y paradas cercanas y una interfaz multilingüe facilitan la elección de rutas para desplazamientos diarios y viajes.",techStack:["Rutas de Transporte Público","Integración de Autobús y Tren","Comparación de Rutas","Interfaz Multilingüe"]},"multilingual-assistant":{title:"Asistente multilingüe AI",description:"Asistente de chat inteligente para consultas en varios idiomas, que construye 1.573 índices vectoriales e implementa un control preciso de las respuestas mediante bifurcación condicional, filtrado de palabras NG y activación de palabras clave. Se generan respuestas coherentes y muy precisas a las preguntas de los usuarios en varios idiomas, c

286omo japonés, inglés y chino, gracias a la búsqueda semántica multilingüe.",techStack:["RAG","Búsqueda vectorial","Incrustación multilingüe","LLM","lógica de bifurcación condicional"]},"weather-forecast":{title:"Predicción meteorológica inteligente con la API meteorológica",description:"Desarrollado un sistema de IA que aprende de datos meteorológicos pasados y hace previsiones en tiempo real de las condiciones meteorológicas recientes. LLM analiza datos pasados obtenidos de JMA API, OpenWeatherMap, etc., y genera previsiones a corto plazo de temperatura, probabilidad de precipitaciones, velocidad del viento, etc. Combinando el reconocimiento de patrones de datos de series temporales con la búsqueda de patrones de similitud en el pasado mediante RAG, el sistema logra previsiones de gran precisión que complementan los modelos estadísticos convencionales.",techStack:["Enlace API meteorológica","LLM","RAG","Análisis de datos de series temporales","procesamiento en tiempo real"]},"local-llm":{title:"Servidor interno / On-Prem Local LLM",description:"Proporcionamos IA adaptada a sus operaciones empresariales, lista para usar en su empresa, con Open WebUI como punto de entrada común, vías de inferencia rápidas con Ollama/MLX y dominios de datos completamente separados para cada UID. Los documentos internos se capturan en un RAG con incrustación en inglés y traducción automática de consultas al japonés, y las habilidades empresariales como el resumen de noticias, la organización de documentos y las tareas rutinarias se ejecutan de forma segura codo con codo. El sistema de ejecución de habilidades está aislado como un contenedor OpenClaw y protegido por tres capas de Cloudflare edge y Nginx local. Proporcionamos asistencia completa para la selección de modelos, el diseño de habilidades y las políticas operativas, y ponemos en marcha la IA dedicada de su empresa de forma que pueda utilizarse desde el mismo día de su despliegue.",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"Predicción de llegada a órbita interplanetaria (AI)",description:"Desarrollado un sistema para analizar los puntos de partida óptimos para la transferencia interplanetaria mediante la obtención de datos de posición planetaria, elementos orbitales y parámetros de gravedad de la API de la NASA. Realiza cálculos orbitales considerando la órbita de transferencia de Hohmann y la asistencia gravitatoria mediante API y Python, y muestra la ventana de lanzamiento, el tiempo necesario y la eficiencia del combustible. El sistema se utiliza como herramienta de simulación para instituciones educativas y empresas relacionadas con el espacio. *La función AI está actualmente eliminada, el cálculo está en curso con API y Python.",techStack:["NASA API (Horizons, SPICE)","Cálculo de la dinámica orbital","Python","visualización de datos"]},"real-estate":{title:"Análisis AI de datos inmobiliarios",description:"Desarrollo de un sistema que recopila información inmobiliaria de una región específica mediante web scraping y realiza análisis avanzados de datos utilizando LLM y Python. Se extraen variables como el precio de la propiedad, el precio unitario tsubo, la antigüedad del edificio y la distancia a la estación, y se generan automáticamente análisis de tendencias de precios, previsiones de mercado y simulaciones de rentabilidad de la inversión. También se han extraído características de descripciones no estructuradas de propiedades utilizando LLM para proporcionar un análisis exhaustivo del mercado combinado con datos cuantitativos. *Oferta ahora disponible como segunda versión (la versión COMPLETA se proporciona por separado)",techStack:["Web scraping (Scrapy, Playwright)","Python","Pandas","Análisis LLM","visualización de datos"]},"fortune-ai":{title:"Sistema de evaluación de la IA basado en cuatro pilares",subtitle:"Reproducir la sabiduría tradicional con tecnología.",paragraphs:["Four Pillars of Divination es la culminación de miles de años de adivinación oriental. Este sistema reproduce íntegramente en Python su lógica de adivinación tradicional, combinada con la interpretación y generación de consejos mediante LLM (Large Language Models). La IA reproduce fielmente el proceso de valoración realizado por un adivino experto.","Tras introducir la fecha, la hora y el lugar de nacimiento, el sistema recupera de la API de Astronomía las horas de salida y puesta del sol y la información de las coordenadas en ese momento. A partir de estos datos, el sistema calcula con precisión el signo del zodiaco, los diez dioses y el equilibrio de los cinco elementos. La lógica basada en la ciencia adivinatoria de los Cuatro Pilares del Destino se indexa en Python y se combina con datos estadísticos e información RAG (Retrieval Augmentation Generation) para mejorar la precisión del análisis de la fórmula de la vida.","Los datos calculados sobre el estilo de vida son explicados en lenguaje natural por LLM de forma fácil de entender. Se generan resultados de valoración personalizados, incluyendo el flujo de la fortuna, la compatibilidad de las relaciones interpersonales y la ocupación adecuada para usted. Puedes obtener respuestas con la misma profundidad que si hubieras consultado a un adivino profesional.","Actualmente está disponible como segunda versión. Puede probar el análisis kumishiki básico y el análisis de la fortuna. La versión completa, que incluye análisis más detallados y consejos multifacéticos, está disponible por separado."],techStack:["Python","API de astronomía (amanecer-atardecer, TimeZoneDB)","Lógica de los cuatro pilares","LLM","Cálculo de coordenadas y hora"]}

286,"google-workspace":{title:"Google Workspace AI integration",description:"Desarrollo de un sistema que vincula Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) con LLM para leer, editar y procesar automáticamente datos empresariales. La IA ayuda con tareas diarias como resumir el contenido de los correos electrónicos, generar borradores para respuestas automáticas, buscar y analizar archivos de Drive, extraer horarios de Calendar y proponer ajustes. El funcionamiento del espacio de trabajo desde el Agente también se realiza a través de una conexión API segura con autenticación OAuth y un diseño de herramientas compatible con MCP. La segunda versión ya está disponible (la versión FULL se proporcionará por separado).",techStack:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"]},"world-news":{title:"Multilingüismo en World NEWS",description:"Desarrollo de un sistema que recopila artículos en tiempo real de fuentes de noticias de todo el mundo y realiza su traducción multilingüe, resumen y categorización mediante LLM. Traduce al japonés artículos originales en inglés, chino, francés, árabe y otros idiomas y los organiza por temas. El seguimiento eficaz de las noticias internacionales no se consigue con una simple traducción automática, sino con una traducción natural basada en la comprensión contextual mediante LLM y la generación de resúmenes que extraen los puntos principales del artículo. *Oferta ahora disponible como segunda versión (la versión COMPLETA se proporciona por separado).",techStack:["Noticias API","Colección RSS","LLM Traducción y resumen","tratamiento multilingüe","Auto-categorización","tratamiento programado por lotes"]},"security-monitoring":{title:"Supervisión de registros de seguridad AI Manager",description:"Desarrollo de un sistema de supervisión en tiempo real de los registros de seguridad de la empresa y de detección y análisis avanzados de amenazas mediante LLM. Un módulo Python recopila y analiza varias fuentes de registros, como cortafuegos, IDS/IPS, registros de autenticación y registros de aplicaciones, y las compara con patrones de ataques anteriores, información sobre vulnerabilidades y casos de incidentes acumulados en RAG. Cuando se detecta un patrón anormal, LLM explica el nivel de amenaza, el alcance del impacto y la respuesta recomendada en lenguaje natural, y alerta inmediatamente al responsable. Si se repite el mismo patrón, se emite automáticamente una alerta de alta prioridad para acelerar la respuesta al incidente.",techStack:["Módulo de supervisión de Python","Recopilación de registros (Fluentd, Logstash)","RAG","Base de datos de patrones de amenazas","Análisis LLM","Notificaciones de alerta (Slack, PagerDuty)"]}}},jL={learnMore:"Seguir leyendo",getStarted:"empezar a ...",contactUs:"Contacte con nosotros",backToHome:"Volver a la página de inicio"},CL={title:"Política de privacidad",subtitle:"Política de privacidad",lastUpdated:"Última actualización: 23 de enero de 2026",intro:'telet.ai (el "Servicio") considera que la protección de su información personal es una responsabilidad importante y ha establecido la siguiente política de privacidad.',sections:{collection:{title:"1. información que debe recogerse",description:"El Servicio puede recopilar la siguiente información cuando usted se pone en contacto con nosotros o utiliza el Servicio.",items:["Dirección de correo electrónico","Su nombre (opcional)","Detalles de la consulta","Otros datos facilitados voluntariamente por usted"]},purpose:{title:"2. finalidad del uso de la información",description:"La información personal recopilada sólo se utilizará para los siguientes fines",items:["Contestar y responder a las consultas","Contacto e información sobre nuestros servicios","Comunicación necesaria para el avance del proyecto","Análisis para la mejora del servicio (en un formato que no identifique a las personas)"]},thirdParty:{title:"3. suministro a terceros",description:"El Servicio no facilitará sus datos personales a terceros salvo en los siguientes casos",items:["Si el cliente da su consentimiento","Cuando exista una solicitud de divulgación basada en leyes y reglamentos","Cuando sea necesario para la protección de la vida, el cuerpo o los bienes de una persona"]},security:{title:"4. gestión de la información",description:"El Servicio gestionará adecuadamente la información personal recopilada y adoptará medidas de seguridad razonables para evitar el acceso no autorizado, la pérdida, la filtración y la falsificación."},email:{title:"5. gestión del correo electrónico",description:"El Servicio tratará los correos electrónicos de acuerdo con la siguiente política.",items:["Utilizaremos el contenido de su correo electrónico únicamente para responder a su consulta y prestarle nuestros servicios.","No venderemos, alquilaremos ni compartiremos su dirección de correo electrónico con terceros.","Es posible que le enviemos correos electrónicos informativos sobre nuestros servicios. Si desea darse de baja, utilice el enlace para darse de baja que aparece en el correo electrónico o póngase en contacto con nosotros a través de la información de contacto que figura a continuación.","El cifrado SSL/TLS se utiliza en las comunicaciones por correo electrónico para garantizar la seguridad de las comunicaciones.","Eliminaremos de forma segura su correo electrónico después de almacenarlo durante un periodo de tiempo adecuado."]},cookies:{title:"6. uso de cookies",description:"Las cookies pueden desactivarse en la configuración de su navegador."},external:{title:"7. utilización de servicios externos",description:"Este servicio puede utilizar los siguientes servicios externos. Consulte los enlaces correspondientes para conocer la política de privacidad de cada servicio.",items:["Google Analytics (access analysis)","Otros servicios en la nube necesarios para las operaciones empresariales"]},changes:{title:"8. cambios en la política de privacidad",description:"Esta política está sujeta a revisión sin previo aviso de acuerdo con las modificaciones de las leyes y reglamentos o los cambios en el contenido del servicio. La política revisada entrará en vigor cuando se publique en esta página."},contact:{title:"9. contacto",description:"Si tiene alguna pregunta sobre esta política, póngase en contacto con nosotros en",company:"telet.ai",contactLink:"Ir al formulario de consulta"}}},DL={title:"Condiciones de uso",subtitle:"Estas son las condiciones de uso de este sitio web y para reservar una consulta, realizar consultas y solicitar información a telet.ai.",lastUpdated:"Última actualización: 22 de enero de 2026",intro:'Estas Condiciones de uso (en lo sucesivo, las "Condiciones") Estas Condiciones de uso (en lo sucesivo, las "Condiciones") se aplican al sitio web operado por telet.ai (en lo sucesivo, "nosotros", "nos", "nuestro" o "nuestra"). (en lo sucesivo, "nosotros", "nos" o "nuestro"). (en lo sucesivo, el "Sitio") operado por telet.ai (en lo sucesivo, el "Nosotros"). El Sitio se ofrece principalmente como página de aterrizaje para citas de consulta, consultas y solicitudes de documentos, y no prevé la creación de cuentas ni funciones de facturación de pago en el Sitio.',sections:{scope:{title:"1. ámbito de aplicación",description:'Las presentes Condiciones Generales se aplican a todos los visitantes y usuarios del Sitio Web (en adelante, los "Usuarios"). Las Condiciones de Uso se aplicarán a todos los visitantes y usuarios del Sitio Web (en adelante, los "Usuarios"). En caso de que se celebre un acuerdo de confidencialidad, un acuerdo de subcontratación, un contrato individual, etc. por separado entre nosotros y un Usuario, dicho documento prevalecerá sobre las Condiciones.'},prohibited:{title:"2. artículos prohibidos",description:"Al utilizar el Sitio, los Usuarios no realizarán ninguno de los siguientes actos",items:["Actos que violen las leyes y reglamentos o el orden y la moral públicos","Cualquier acto que infrinja nuestros derechos de propiedad intelectual, privacidad u otros derechos o intereses, o los de terceros.","Actos que interfieran en el funcionamiento de este sitio o sistemas relacionados mediante acceso no autorizado, carga excesiva, sabotaje, etc.","Presentar información falsa o suplantar la identidad de un tercero en formularios de consulta, etc.","Scraping, ingeniería inversa o acceso automatizado excesivo sin nuestro permiso previo."]},disclaimer:{title:"3. descargo de responsabilidad",description:'proporcionamos este sitio "tal cual" y "según disponibilidad". En la máxima medida permitida por la ley, renunciamos a todas las garantías con respecto a este sitio.',items:["La información de este sitio está sujeta a cambios o supresiones sin previo aviso.","No garantizamos la exactitud, integridad o idoneidad para un fin determinado de la información contenida en este sitio.","No seremos responsables de ningún daño resultante del uso de este sitio o de la confianza depositada en la información contenida en él.","Las respuestas a las consultas y preguntas son sólo para información general y no constituyen asesoramiento jurídico, fiscal, médico o profesional de otro tipo."]},ip:{title:"4. derechos de propiedad intelectual",description:"Todos los derechos sobre los textos, imágenes, diseños, marcas, logotipos, etc. de este sitio nos pertenecen a nosotros o a los legítimos titulares de los derechos. No puede utilizarlos sin el permiso de los titulares de los derechos."},links:{title:"5. enlaces a sitios externos",description:"Este sitio web puede contener enlaces a sitios web de terceros. No somos responsables del contenido ni del funcionamiento de los sitios enlazados."},changes:{title:"6. modificación de las presentes condiciones generales",description:"Nos reservamos el derecho a modificar las presentes Condiciones Generales de vez en cuando según sea necesario. Las Condiciones de Uso modificadas entrarán en vigor cuando se publiquen en el Sitio."},governingLaw:{title:"7. ley aplicable y jurisdicción",description:"Las presentes Condiciones Generales se regirán e interpretarán de conformidad con las leyes de Japón. En caso de cualquier disputa relacionada con este Sitio, el Tribunal de Distrito de Tokio tendrá jurisdicción exclusiva como tribunal de primera instancia."},contact:{title:"8. contacto",description:"Si tiene alguna pregunta sobre estas condiciones, póngase en contacto con",company:"telet.ai",contactLink:"Ir al formulario de consulta"}}},zL={title:"moción ordinaria",subtitle:"La IA predice la",description1:"La información meteorológica es proporcionada en tiempo real por cada país. Su país se muestra en el centro del mapa. El clima de ayer y el de hoy son registros reales: las nubes de lluvia deberían haber estado en su cielo en esa fecha y hora.",description2:"Sin embargo, esta animación también anima los futuros movimientos del tiempo.",description3:"

286Vectoriza datos pasados y busca patrones a gran velocidad. También se pueden predecir patrones meteorológicos a partir del entorno. Del mismo modo, es posible predecir la cosecha, la producción y las ventas calculando elementos (i) del entorno y valores estadísticos avanzados a partir del rendimiento pasado y haciendo que la IA los analice.",refresh:"refrescar (por ejemplo, la memoria)",loading:"Cargando mapa del tiempo..."},TL={hero:{badges:["Empresa","LLM local","Agente de aduanas"],title:"Diseño y desarrollo de agentes de IA para empresas",description:"Diseño y creación de agentes de IA específicos adaptados a las necesidades de la empresa"},mission:{title:"Servicios de desarrollo de agentes de IA personalizados",description:"Desarrollamos de forma coherente agentes de IA personalizados que abordan los problemas empresariales exclusivos de su empresa, desde el diseño hasta la implementación y el funcionamiento. Nuestro diseño arquitectónico hace hincapié en la compatibilidad con los sistemas de misión crítica 

286y los entornos SaaS existentes, lo que permite una integración fluida con API, bases de datos y herramientas internas. Nuestros ingenieros están familiarizados con LangChain, AutoGen, CrewAI y otros marcos importantes, Nuestros ingenieros están familiarizados con LangChain, AutoGen, CrewAI y otros marcos importantes, y propondrán la mejor solución para la pila tecnológica y la estructura operativa de su empresa. No sólo desarrollamos chatbots puntuales, sino que también construimos sistemas multiagente en los que varios Agentes cooperan entre sí para realizar tareas, y Flujos de Trabajo Agenticos que funcionan en conjunción con herramientas externas. Apoyamos la maximización del valor empresarial aportado por los Agentes de IA a través de una asociación a largo plazo que incluye la mejora continua tras la implementación, el seguimiento de las actualizaciones del modelo y el mantenimiento de los sistemas de supervisión operativa.",imageAlt:"Servicios de desarrollo de agentes de IA personalizados"},summary:{title:"Tendencias en el desarrollo de agentes",paragraphs:['De 2025 a 202

2866, el desarrollo de agentes de IA está pasando de la fase de demostración a la de implantación corporativa a gran escala. El concepto de "IA agenética" ha arraigado, y los sistemas autónomos de ejecución empresarial que van más allá de meros chatbots han empezado a funcionar en diversos sectores.','Los "agentes usuarios de ordenadores" que controlan directamente navegadores y escritorios, como Operator de OpenAI, Computer Use de Anthropic y Project Mariner de Google, se han convertido en algo práctico. El Model Context Protocol (MCP) es un ecosistema en expansión que permite la integración plug-and-play con miles de herramientas.',"Las arquitecturas multiagente han alcanzado la madurez, con Agentes especializados que trabajan juntos para realizar tareas complejas como gestión de proyectos, atención al cliente y análisis de datos. La estandarización del protocolo A2A (agente a agente) también ha hecho avanzar la colaboración entre agentes de distintos proveedores.",'El modelo de gobernanza ha evolucionado de "Human-in-the-Loop" a "Human-on-the-Loop" para implantaciones corporativas, con un mecanismo de auditoría posterior a la decisión del agente y escalado automático basado en el nivel de riesgo como características estándar, lo que acelera la adopción en industrias reguladas como la financiera, la médica y la manufacturera. La adopción de este sistema se está acelerando en sectores regulados como el financiero, el médico y el manufacturero.']},features:{title:"Diseño y desarrollo",subtitle:"Diseño y desarrollo",items:[{title:"Diseño de agentes basado en los requisitos de la empresa",description:"Extraemos casos de uso del análisis del flujo de negocio, descomponemos tareas y aclaramos las responsabilidades de los Agentes. Determinamos si el procesamiento puede ser completado por un único Agente o requiere la coordinación de varios Agentes, y seleccionamos los patrones de inferencia óptimos, como ReAct, Plan-and-Execute y Reflexion."},{title:"Integración de GAR / herramientas / flujos de trabajo",description:"Canalizaciones RAG de gran precisión que combinan la generación de incrustaciones, la estrategia de trozos y la búsqueda híbrida, la conexión estandarizada a API y bases de datos externas con definiciones de herramientas compatibles con MCP y la orquestación de flujos de trabajo de varios pasos mediante LangGraph y CrewAI. Orqueste flujos de trabajo de varios pasos con LangGraph y CrewAI."},{title:"Diseño de seguridad y autorización",description:"Se implementa el control de acceso a las funciones del Agente mediante RBAC/ABAC, contramedidas de Inyección de Peticiones y detección y enmascaramiento de PII. Se conservan registros de auditoría de todas las entradas y salidas, y se admite el funcionamiento en entornos conformes con SOC2 e ISO27001."},{title:"Ejecución, incluidos el funcionamiento y la supervisión",description:"Implementar la visualización de trazas con LangSmith, Langfuse, etc., y la supervisión en tiempo real de la latencia, el consumo de tokens y la tasa de errores. Construir un ciclo de mejora continua que incluya alertas de detección de anomalías, infraestructura de pruebas A/B y pruebas de regresión al actualizar los modelos."}]},techStack:{title:"Pilas tecnológicas",subtitle:"Plataforma y tecnología",categories:{agentFramework:{title:"Marco de agentes",items:["AutoGen 0.4 / AG2","CrewAI","LangGraph","SDK de agentes de OpenAI","MCP antrópico","Google ADK (Agent Development Kit)","Núcleo semántico","Plataforma de agentes propia"]},apiLayer:{title:"Capa API",items:["FastAPI / Litestar","gRPC / Conectar","Federación GraphQL","Eventos enviados por el servidor (SSE)","WebSocket / NATS","OAuth 2.1 / OIDC","Pasarela API (Kong / Envoy)","Limitación de velocidad adaptativa"]},vectorDb:{title:"Vector DB / RAG",items:["Piña","Weaviate","Qdrant","Milvus","ChromaDB","pgvector","FAISS","Turbopuffer"]}

286,infrastructure:{title:"Infraestructura",items:["Kubernetes (EKS / GKE / AKS)","Docker / Podman","Clúster de GPU local","Nube privada","Sin servidor (Lambda / Cloud Run)","OpenTelemetry / Langfuse","Terraform / Pulumi","ArgoCD / Flux"]}},updateNote:{title:"Principales puntos de actualización para 2026",description:"En Agent Framework, están surgiendo SDK/protocolos oficiales de los principales proveedores, como OpenAI Agents SDK, Anthropic MCP y Google ADK; en API Layer, gRPC y SSE se han convertido en el estándar para la comunicación agente-agente, y la transición a OAuth 2.1 está en marcha. En Vector DB, Qdrant y Turbopuffer están llamando la atención por su búsqueda de alta velocidad."}},cta:{title:"Contacte con nosotros",description:`Podemos consultarle desde la disposición de los requisitos.
287También se pueden considerar configuraciones PoC y LLM locales.`,primaryButton:"Contacte con nosotros",secondaryButton:"Consulta técnica"}},EL={title:"Preguntas frecuentes",subtitle:"PREGUNTAS FRECUENTES",items:[{question:"¿Puedo utilizar mis manuales y FAQ actuales sin modificarlos?",answer:"Sí, puedes utilizarlo; es compatible con la mayoría de formatos, incluidos PDF, Word, Excel, HTML y archivos de texto. Sólo tiene que cargar sus documentos internos tal cual, y AI aprenderá el contenido y lo utilizará para responder a sus preguntas. No es necesaria ninguna conversión de formato ni ningún tratamiento especial."},{question:"¿Es seguro utilizarlo con datos que contengan información sensible?",answer:"Sí, puede utilizar nuestro servicio de forma segura. En las instalaciones (servidores internos) o en un entorno de nube privada, los datos nunca se transmiten fuera de la empresa. Podemos proponerle configuraciones que respondan a sus necesidades de seguridad."},{question:"¿Cuánto tarda en aplicarse?",answer:"Un bot de FAQ estándar puede implementarse en tan sólo 2-4 semanas. Si se requiere la integración con sistemas existentes o una personalización avanzada, estimamos 1-3 meses. También podemos empezar con una PoC (prueba de concepto) a pequeña escala."},{question:"¿Utiliza API externas como ChatGPT o Claude?",answer:"La elección está disponible bajo petición. Admitimos tanto configuraciones que utilizan API externas (OpenAI, Anthropic, Google, etc.) como configuraciones que utilizan LLM de código abierto (Llama, Mistral, Qwen, etc.) que se ejecutan de forma completamente local. Podemos proponerle la mejor configuración para sus requisitos de coste, seguridad y rendimiento."},{question:"¿Hasta qué punto es precisa la respuesta de la IA?",answer:"Dependiendo de la aplicación y de la calidad de los datos, un sistema GAR correctamente diseñado puede devolver respuestas precisas a preguntas para las que existen documentos relevantes con una precisión del 90% o superior. También se incorporan de serie mecanismos para controlar la halcinación (generación de información errónea)."},{question:"¿Admite otros idiomas además del japonés?",answer:"Sí, ofrecemos asistencia multilingüe. Admitimos los principales idiomas, como japonés, inglés, chino y coreano. En lugar de tener modelos distintos para cada idioma, un único sistema puede gestionar consultas multilingües."},{question:"¿Puede integrarse con los sistemas existentes (CRM, herramientas de chat, etc.)?",answer:"Sí, se puede integrar; admite la integración de API con las principales herramientas empresariales, como Salesforce, HubSpot, Slack, Microsoft Teams y LINE WORKS. Puedes incorporar un asistente de IA sin cambiar tu flujo de trabajo actual."},{question:"¿Cuál es su estructura de tarifas?",answer:"Depende del tamaño y los requisitos del proyecto. Aunque las tarifas de construcción inicial y explotación mensual suelen ser combinadas, también aceptamos solicitudes de desarrollo puntual o servicios de consultoría únicamente. Por favor, comuníquenos primero sus necesidades. Le facilitaremos un presupuesto aproximado."}]},RL={title:"Planes de tarifas",subtitle:"Planes de precios",description:"Planes flexibles que se adaptan al tamaño de su empresa",plans:[{name:"arranque",price:"29.800 yenes",period:"Mensual/usuario",description:"El mejor plan para equipos pequeños. Funciones básicas de IA a un precio asequible.",features:["Hasta 100 llamadas al mes","Transcripción básica","Detección de palabras clave","Asistencia por correo electrónico","7 días de conservación de datos"],cta:"Empiece gratis",popular:!1},{name:"profesional",price:"¥79,800",period:"Mensual/usuario",description:"Ideal para equipos en crecimiento. Funciones analíticas avanzadas.",features:["Hasta 500 llamadas al mes","Transcripción AI de alta precisión","Análisis emocional","Enlace CRM","Asistencia prioritaria","30 días de retención de datos","Informes personalizados"],cta:"14 días de prueba gratuita",popular:!0},{name:"empresa",price:"Contacte con nosotros",period:"personalizado",description:"Planes completos para grandes organizaciones",features:["Llamadas ilimitadas","Modelo de IA dedicado","asistencia in situ","Asistencia dedicada","Colaboración personalizada","Garantía de SLA","Auditorías de seguridad","Conservación ilimitada de datos"],cta:"Consultar con Ventas",popular:!1}],faq:{title:"Preguntas frecuentes",items:[{question:"¿Hay una prueba gratuita?",answer:"Sí, hay disponible una prueba gratuita de 14 días para los planes Profesionales. No se necesita tarjeta de crédito para empezar."},{question:"¿Puedo cambiar de plan en cualquier momento?",answer:"Sí, puede cambiar de plan en cualquier momento. Los cambios se aplicarán a partir del siguiente ciclo de facturación."},{question:"¿Hay algún plazo contractual vinculado?",answer:"Los planes mensuales no tienen restricciones de duración de contrato. Si eliges un contrato anual, recibirás un 20% de descuento."}]}},NL={hero:{badge:"Tecnología basada en IA",title:"Transcripción AI",description:"Los modelos avanzados de aprendizaje automático proporcionan una precisión del 99,5%. La IA entrenada en millones de datos de interacción con clientes convierte la voz en texto.",features:["99,5% de precisión","procesamiento en tiempo real","Compatibilidad con más de 40 idiomas"]},features:{title:"Funciones principales",subtitle:"Motor de transcripción avanzado que va más allá del simple reconocimiento de voz",items:[{title:"transcripción en tiempo real",description:"Obtenga transcripciones en tiempo real durante las conversaciones. Obtenga una visión instantánea y no pierda nunca una oportunidad de coaching en tiempo real."},{title:"soporte multilingüe",description:"Admite más de 40 idiomas y cubre equipos globales con detección automática de idiomas."},{title:"comprensión contextual",description:"Los modelos de IA entrenados en terminología específica del sector reconocen con precisión términos técnicos y empresariales."},{title:"separación de altavoces",description:"Identifica y separa automáticamente a los distintos interlocutores de una conversación y les asigna atributos claros."},{title:"léxico personalizado",description:"Aprender los nombres de productos, servicios y términos técnicos exclusivos de su empresa proporciona una precisión aún mayor."}

287,{title:"Garantía de calidad",description:"Los controles de calidad automáticos y la puntuación de fiabilidad garantizan la máxima calidad de transcripción."}]},useCases:{title:"Escenas de uso",subtitle:"Sectores y equipos que requieren una transcripción precisa de las llamadas",items:[{title:"Equipo de ventas",description:"Reflexionar sobre las llamadas de ventas e identificar patrones de éxito. Mejorar la eficacia de las conversaciones y preparar a los miembros del equipo con ejemplos concretos."},{title:"Atención al cliente",description:"Registrar con precisión los problemas de los clientes. Mantener un historial de asistencia completo para garantizar una calidad de servicio constante."},{title:"control de calidad",description:"Supervisar el cumplimiento y garantizar la observancia de los guiones. Mantener registros detallados para auditorías y formación."}]},stats:{accuracy:{value:"99.5%",label:"precisión de la transcripción"},languages:{value:"40+",label:"Idiomas admitidos"},latency:{value:"<2 seg.",label:"retraso en el procesamiento"},availability:{value:"24/7",label:"procesamiento continuo"}}},GL={hero:{badge:"Detección inteligente",title:"Seguimiento de palabras clave",description:"Supervise automáticamente las menciones de la competencia, las refutaciones y los temas importantes. No pierda nunca señales importantes de las conversaciones con los clientes.",badges:["

287Seguimiento de la competencia","Detección de respuestas","Análisis de temas","palabra clave personalizada"]},features:{title:"Seguimiento de la información crítica",subtitle:"Detección y análisis automáticos de palabras clave de todas las conversaciones",items:[{title:"Vigilancia de la competencia",description:"Siga las menciones de la competencia para saber cómo comparan los clientes potenciales su solución con las alternativas del mercado."},{title:"Detección de respuestas",description:"Identifique automáticamente las objeciones y preocupaciones más comunes y desarrolle respuestas y formación eficaces."},{title:"palabra clave personalizada",description:"Defina y rastree palabras clave y frases personalizadas específicas de su empresa, producto o sector."},{title:"Análisis de tendencias",description:"Descubra las tendencias emergentes a partir de las conversaciones con los clientes y adapte su estrategia en función de las señales reales del mercado."},{title:"extracción de contexto",description:"Obtenga el contexto antes y después de la palabra clave, no sólo su mención. Entender no solo lo que se dijo, sino por qué y cómo se dijo."},{title:"Búsqueda inteligente",description:"Busca instantáneamente en todas las conversaciones para encontrar exactamente dónde y cuándo se habló de un tema concreto."}]},applications:{title:"Escenas de uso",items:{competitive:{title:"Inteligencia competitiva",description:"Profundo conocimiento del panorama competitivo:",points:["Siga a sus competidores más mencionados","Entender cómo comparan los clientes potenciales las características y los precios","Identifique sus ventajas y debilidades competitivas","Creación de material anticompetitivo eficaz"]},objections:{title:"respuesta a una refutación",description:"Convierta una refutación en una oportunidad:",points:["Identificar las preocupaciones más comunes de los clientes","Analizar las respuestas de refutación de los mejores","Libro de jugadas con respuesta probada.","Forme a su equipo con turnos eficaces"]},product:{title:"Información sobre el producto",description:"Promover el desarrollo de productos a través de las opiniones de los clientes:",points:["Identificar las funciones más solicitadas","Comprender los retos de la funcionalidad actual","Descubra casos de uso inesperados","Priorizar la hoja de ruta en función de las necesidades de los clientes"]},compliance:{title:"Cumplimiento y calidad",description:"Garantizar el cumplimiento de la normativa y la calidad:",points:["Supervisar las divulgaciones obligatorias y el lenguaje jurídico","Marcar las conversaciones que puedan infringir la política","Seguimiento del cumplimiento de los guiones y de las mejores prácticas","Generar informes de cumplimiento para auditoría"]}}},advancedFeatures:{title:"Funciones avanzadas",items:[{title:"recuperación ambigua",description:"Detecta automáticamente variaciones de palabras clave, sinónimos y términos relacionados sin necesidad de configuración manual."},{title:"análisis histórico",description:"Analice las tendencias de las palabras clave a lo largo del tiempo para identificar patrones estacionales y temas emergentes."},{title:"Alertas en tiempo real",description:"Reciba notificaciones inmediatas cuando se mencionen palabras clave importantes en conversaciones en directo."}]}},OL={hero:{badges:["Empresa","LLM local","Funcionamiento interno del servidor"],title:"Construcción LLM in situ",description:"Opere LLM en su propio entorno, manteniendo los datos fuera de la empresa"},mission:{title:"Visión general del servicio de construcción LLM in situ",paragraphs:["Para las empresas que no pueden divulgar datos confidenciales fuera de la empresa, construimos un entorno LLM que puede completarse en sus propios servidores o en una nube privada. Esta solución es ideal para organizaciones que requieren una gestión estricta de la información, como instituciones financieras, instituciones médicas, agencias gubernamentales e institutos de investigación, porque no utiliza ninguna API pública y todo el procesamiento se completa dentro de la infraestructura gestionada por el cliente.","Proporcionamos un apoyo integral desde la selección del modelo, la construcción del servidor, la o

287ptimización del motor de inferencia y la supervisión del funcionamiento, utilizando modelos de peso abierto disponibles en el mercado como Llama 3.3, Qwen 2.5, Mistral y DeepSeek con motores de inferencia de alto rendimiento como vLLM, Ollama y SGLang. Estos modelos se ejecutan en motores de inferencia de alto rendimiento como vLLM, Ollama y SGLang para proporcionar un entorno de inferencia de baja latencia y alto rendimiento.","Damos soporte a una amplia gama de casos de uso, como la integración con sistemas existentes, chatbots, RAG internos, resumen de documentos y generación de código. Podemos diseñar para satisfacer los requisitos de la empresa, incluida la especialización empresarial mediante el ajuste fino del modelo, la optimización de la GPU NVIDIA / Apple Silicon, el diseño multiinquilino, la construcción de pasarelas API y mucho más."]},summary:{title:"Tendencias en LLM in situ",paragraphs:["En 2025-2026, la utilidad de los LLM in situ está mejorando rápidamente. El rendimiento de los modelos ponderados abiertos ha flanqueado al de los modelos cerrados, y el número de modelos ofrecidos con licencias disponibles comercialmente ha reducido significativamente el obstáculo para las operaciones internas.","Los modelos con parámetros de 8B a 70B han alcanzado un nivel práctico para muchas tareas empresariales, la cuantización GGUF permite una inferencia de alta velocidad incluso en GPU de consumo.","Para los motores de inferencia, Continuous Batching y PagedAttention de vLLM aumentan drásticamente el rendimiento, y RadixAttention de SGLang acelera las complejas interacciones multivuelta. Ollama se ha convert

287ido en el estándar de facto para los entornos de desarrollo locales, y llama.cpp está optimizado con Apple Silicon para una inferencia rápida en los Mac de la serie M.","En la adopción por parte de las empresas, además de la soberanía de los datos y el cumplimiento de la normativa, la optimización de los costes a largo plazo (alejándose de la facturación de API de pago por uso) se está convirtiendo cada vez más en una motivación para la adopción. Las configuraciones híbridas (local para procesamiento sensible y API en la nube para procesamiento de propósito general) se han convertido en una opción común."],icons:[{label:`Privado
288Nube`},{label:`Datos
289Soberanía`},{label:`Coste
290Optimización`},{label:`Empresa
291Seguridad`}]},features:{title:"Características de diseño y desarrollo",subtitle:"Diseño y desarrollo",items:[{title:"Diseño de infraestructuras y optimización de GPU",description:"Diseñe la configuración de hardware óptima para su carga de trabajo y presupuesto, incluidos NVIDIA A100/H100, RTX 4090/5090 y Apple Silicon (M2 Ultra/M3 Max/M4 Max), y cree un entorno de inferencia que aproveche al máximo el rendimiento de la GPU mediante la optimización CUDA, MPS y Metal. Optimización CUDA, MPS y Metal para maximizar el rendimiento de la GPU."},{title:"Selección y optimización de modelos",description:"Selecciona el modelo óptimo entre Llama, Qwen, Mistral, DeepSeek, etc. teniendo en cuenta las características de la tarea, los requisitos de lenguaje y las limitaciones de recursos. La cuantificación (GGUF/AWQ/GPTQ), la optimización de la caché KV y el ajuste del procesamiento por lotes se utilizan para lograr precisión y rendimiento."},{title:"Construcción del motor de inferencia",description:"Seleccione y construya motores de inferencia como vLLM, SGLang, Ollama, TGI, etc. según su aplicación, y aplique las últimas tecnologías como Continuous Batching, Speculative Decoding y Prefix Caching para proporcionar API de inferencia de baja latencia y alto rendimiento. Proporcionamos API de inferencia de baja latencia y alto rendimiento."},{title:"Funcionamiento, supervisión y seguridad",description:"Implemente la supervisión de métricas inferenciales con Prometheus/Grafana, el control de acceso con la pasarela API, los registros de auditoría, el escalado automático y la recuperación ante fallos. Cree un entorno de operaciones de producción que cumpla los requisitos de seguridad de la empresa."}]},techStack:{title:"Pila tecnológica: LLM local",subtitle:"Plataforma y tecnología",categories:{openWeightModels:{title:"Modelos de peso abierto",items:["Llama 3.3 70B / 8B","Qwen 2.5 72B / 32B / 7B","Mistral Large / Nemo","DeepSeek V3 / R1","Gemma 2 27B / 9B","Phi-4","Mando R (Cohere)"]},japaneseModels:{title:"Modelo específico japonés",items:["ELYZA-japonés-Llama-2","Golondrina 70B / 13B","Llama japonesa-3","Qwen-2.5-Japanese","Karakuri-LM"]},inferenceEngine:{title:"Motor de inferencia",items:["vLLM (dosificación continua)","SGLang (RadixAttention)","Ollama (desarrollo local)","llama.cpp (Apple Silicon)","TGI (Cara de abrazo)","TensorRT-LLM (NVIDIA)","ExLlamaV2 (especialización en cuantificación)"]},quantization:{title:"Cuantización",items:["GGUF (llama.cpp estándar)","AWQ (alta precisión de 4 bits)","GPTQ (optimización de GPU)","bitsybytes(8bit/4bit)","EETQ (Cuantización de Eficiencia Eficaz)"]},hardware:{title:"Hardware",items:["NVIDIA H100 / A100","NVIDIA L40S / A10G","RTX 4090 / 5090","Apple Silicon M2 Ultra","Apple Silicon M3/M4 Max","AMD MI300X"]}

291,infrastructure:{title:"Infraestructura",items:["Docker / Kubernetes","Servidor NVIDIA Triton","Pasarela API (Kong / Envoy)","Prometheus / Grafana","Nube privada (VMware / OpenStack)","Soporte ambiental con cámara de aire"]}},updateNote:{title:"Principales puntos de actualización para 2026",description:"En los modelos de peso abierto, Llama 3.3 alcanza un rendimiento de propósito general de clase GPT-4 y mejora notablemente el rendimiento en japonés; la serie Qwen 2.5 amplía la oferta con una variedad de tamaños y un rendimiento bien equilibrado; DeepSeek V3/R1 llama la atención por su rendimiento en costes de inferencia; y los motores de inferencia vLLM 2.0 Chunked Prefill y SGLang's RadixAttention aceleran el diálogo multivuelta. En el motor de inferencia, Chunked Prefill de vLLM 2.0 y RadixAttention de SGLang aceleran las interacciones multivuelta; la optimización de Apple Silicon de llama.cpp hace que la inferencia de modelos 70B sea práctica en M4 Max; y la tecnología Quantization ofrece precisión de 4 bits y alto rendimiento. Las técnicas de cuantificación consiguieron tanto precisión de 4 bits como velocidad de inferencia, y GGUF se convirtió en el formato estándar de facto."}},cta:{title:"Contacte con nosotros",description:`Podemos consultarle desde la disposición de los requisitos.
292También se pueden considerar configuraciones PoC y LLM locales.`,primaryButton:"Contacte con nosotros",secondaryButton:"Consulta técnica"}},BL={hero:{badges:["Empresa","RAG","generación de extensiones de búsqueda"],title:"Vector / RAG Desarrollo",description:"Crear un sistema RAG para vincular los conocimientos internos con el LLM"},mission:{title:"Visión general de los servicios de desarrollo Vector / RAG",paragraphs:["Construiremos un sistema RAG (generación de extensiones de búsqueda) que permita a los empleados realizar búsquedas y preguntas en lenguaje natural vinculando los activos de conocimiento acumulados en la empresa con LLM. Integramos diversas fuentes de datos como manuales, actas, documentos técnicos, preguntas frecuentes, historial de Slack, Confluence, SharePoint, etc. para crear un entorno en el que el conocimiento organizativo pueda utilizarse de forma inmediata.","Maximizamos la precisión de la búsqueda y la calidad de la respuesta aplicando las últimas tecnologías, como la búsqueda híbrida (búsqueda vectorial + búsqueda por palabras clave), el reordenamiento, el chunking jerárquico, el filtrado de metadatos, etc., además de la búsqueda simple de documentos. Indicamos claramente la fuente de la respuesta para suprimir la halcinación y garantizar la fiabilidad.","Admitimos diversos entornos, como AWS, GCP, Azure y on-premise, y nos integramos con la infraestructura de autenticación y la gestión de autoridades existentes. Proporcionamos un soporte coherente para mantener la frescura de los datos (actualizaciones diferenciales y reindexación automática), analizar los registros de uso y mejorar continuamente la precisión."]},summary:{title:"Tendencias tecnológicas RAG",paragraphs:['Entre 2025 y 2026, la tecnología GAR está pasando a la fase de "implementación básica a calidad de producción". Además de la búsqueda vectorial simple, la búsqueda híbrida, que combina múltiples métodos de búsqueda, el reordenamiento, que reordena los resultados de la búsqueda, y la descomposición de consultas, que descompone y procesa preguntas complejas, se han convertido en configuraciones estándar.',"Las estrategias de fragmentación también han evolucionado: la fragmentación jerárquica, que tiene en cuenta la estructura del documento, la superposición de fragmentos y la fragmentación multimodal, que procesa tablas y figuras por separado, contribuyen a mejorar la precisión. También se ha reconocido ampliamente la eficacia de la recuperación contextual (que añade información contextual a los trozos).","Para la selección de BD vectoriales, además de servicios gestionados como Pinecone, Weaviate, Qdrant y Milvus, PostgreSQL pgvector y FAISS local ofrecen una amplia gama de opciones para satisfacer sus necesidades. También hay disponibles modelos de incrustación, como text-Embedding-3-large, Cohere Embed v3 y modelos multilingües.",'En las implantaciones empresariales, la vinculación de la gestión de autoridades (ACL), el enmascaramiento de PII, los registros de auditoría y el diseño multiusuario son requisitos esenciales, y es necesario pasar de una "PoC operativa" a un "sistema de producción listo para funcionar".'],icons:[{label:`Híbrido
293Buscar en`},{label:`Multimodal
294RAG`},{label:`Agentic
295RAG`},{label:`Empresa
296Seguridad`}]},features:{title:"Características de diseño y desarrollo",subtitle:"Diseño y desarrollo",items:[{title:"Recogida y preprocesamiento de datos",description:"Extracción y limpieza de datos de PDF, Word, Excel, HTML, Markdown, Slack, Confluence, SharePoint, etc. Soporte multimodal que incluye OCR, análisis de estructura de tablas y generación de pies de imagen. Mantenga la frescura con actualizaciones diferenciales y ejecución programada."},{title:"Diseño de sistemas de búsqueda de alta precisión",description:"Construye búsquedas de alta precisión combinando Búsqueda Vectorial + Búsqueda Híbrida BM25, Reranking por Cohere Rerank / Cross-Encoder, y Expansión y Descomposición de Consultas. También se implementan el filtrado de metadatos y el control de acceso basado en permisos."}

296,{title:"Generación de respuestas y control de calidad",description:"Mejora de la calidad de las respuestas gracias a la optimización del contexto, las preguntas de respuesta rápida y la inferencia de la cadena de pensamiento. La clarificación de la fuente, la visualización de la puntuación de confianza y la detección de ambigüedades garantizan la fiabilidad para uso empresarial."},{title:"Ciclo de funcionamiento y mejora",description:"Proporcionamos asistencia operativa, que incluye análisis de registros de uso, control de la tasa de aciertos en las búsquedas y de la satisfacción de los usuarios, mejora continua a través de circuitos de retroalimentación, infraestructura de pruebas A/B, actualizaciones del modelo de incrustación y revisiones de la estrategia de fragmentación."}]},techStack:{title:"Pila tecnológica: Vector / RAG",subtitle:"Plataforma y tecnología",categories:{embeddingModels:{title:"Modelos de incrustación",items:["OpenAI text-embedding-3-large","Cohere Embed v3","Viaje AI","Incrustaciones Jina v3","BGE-M3 (multilingüe)","multilingüe-e5-grande","Modelos específicos para Japón"]},vectorDatabases:{title:"Bases de datos vectoriales",items:["Pinecone (Gestionado)","Weaviate (Búsqueda híbrida)","Qdrant (alto rendimiento)","Milvus (a gran escala)","Chroma (ligero)","PostgreSQL pgvector","FAISS (local)"]},chunking:{title:"Agrupación y procesamiento",items:["fragmentación jerárquica","Agrupación semántica","Superposición de trozos","Ventana de frases","Recuperación contextual","Tratamiento de tablas y gráficos","Apoyo multimodal"]},search:{title:"Búsqueda y recuperación",items:["Búsqueda híbrida (Vector + BM25)","Cohere Rerank","Recodificación cruzada","Ampliación y descomposición de consultas","HyDE (Generación hipotética de documentos)","Autoconsulta","Recuperación de consultas múltiples"]}},updateNote:{title:"Principales puntos de actualización para 2026",description:"En el modelo Embedding, text-Embedding-3-large y Cohere Embed v3 se convirtieron en la norma de la empresa, y el rendimiento en japonés mejoró notablemente; la recuperación contextual demostró su eficacia, y añadir contexto a los trozos se convirtió en el método estándar. La Búsqueda Híbrida + Reordenación se ha convert

296ido en una configuración obligatoria para la búsqueda, reduciendo el número de configuraciones de búsqueda vectorial simple, y se está poniendo en práctica la RAG Agentic (los agentes seleccionan dinámicamente las estrategias de búsqueda). Desde el punto de vista operativo, se está haciendo hincapié en el establecimiento de ciclos de medición y mejora de la calidad utilizando marcos de evaluación RAG (RAGAS, LangSmith)."}},cta:{title:"Contacte con nosotros",description:`Podemos consultarle desde la disposición de los requisitos.
297También se pueden considerar configuraciones PoC y LLM locales.`,primaryButton:"Contacte con nosotros",secondaryButton:"Consulta técnica"}},KL={hero:{badges:["Pila completa","Integración LLM","Diseño API"],title:"Desarrollo de aplicaciones de IA",description:"Diseñar y crear aplicaciones prácticas que aprovechen el LLM"},mission:{title:"Descripción general del servicio de desarrollo de aplicaciones de IA",paragraphs:["Proporcionamos diseño y desarrollo de aplicaciones de IA que crean valor empresarial analizando datos adquiridos de API externas con LLM, vinculando API con diversas fuentes de datos como CRM, ERP, SaaS, sensores IoT y redes sociales, y alimentando los datos adquiridos a LLM en tiempo real. Permitimos el análisis avanzado de datos que no se puede lograr con las herramientas de BI convencionales, como la generación de informes analíticos utilizando el lenguaje natural, la detección de anomalías, la predicción de tendencias y la clasificación automática.","No sólo la simple integración de API, sino también la integración de múltiples fuentes de datos, la limpieza de datos y la conversión a un formato fácil de interpretar para LLM mediante un procesamiento estructurado. La ingeniería rápida maximiza la precisión del análisis y genera resultados especializados para los requisitos empresariales. Construimos aplicaciones prácticas que combinan datos e IA, como datos de ventas para predecir negociaciones comerciales, registros de atención al cliente para realizar análisis de VoC y datos de sensores para proporcionar recomendaciones de mantenimiento de equipos.","Damos soporte a una variedad de interfaces como aplicaciones web, aplicaciones móviles, integración Slack/Teams y procesamiento periódico por lotes. Proporcionamos un soporte coherente desde la definición de requisitos hasta el diseño de la API, la implementación de la integración LLM y el funcionamiento en producción."]},summary:{title:"Tendencias en el desarrollo de aplicaciones de IA",paragraphs:['De 2025 a 202

2976, el desarrollo de aplicaciones de IA está entrando en una fase práctica basada en "integración API x análisis LLM". El análisis de datos no estructurados y la generación de conocimientos en lenguaje natural, que no podían gestionar las herramientas de BI y los cuadros de mando tradicionales, están empezando a incorporarse a los sistemas empresariales.',"Cabe destacar la maduración de la funcionalidad de salida estructurada de LLM, que permite un control estricto de la salida conforme al esquema JSON, lo que hace realidad una canalización automatizada de extremo a extremo, en la que los datos adquiridos a través de las API son analizados por LLM y los resultados se introducen directamente en los sistemas posteriores. La ejecución en varias fases de la llamada a funciones permite gestionar en un único flujo de trabajo la recopilación, integración y análisis de datos a través de varias API.","En la infraestructura de procesamiento de datos, Polars ha surgido como un estándar para el procesamiento de alta velocidad y, en combinación con el procesamiento en flujo, facilita el análisis en tiempo real.","En las implantaciones corporativas, las necesidades han pasado del desarrollo PoC puntual al diseño escalable para el funcionamiento en producción, lo que requiere un diseño de arquitectura integral que incluya la optimización de costes, la gestión de errores y el soporte de registros de auditoría."],icons:[{label:`Agentic
298IA`},{label:`MCP
299Protocolo`},{label:`Multi
300Agente`},{label:`Humano-en
301-the-Loop`}]},features:{title:"Características de diseño y desarrollo",subtitle:"Diseño y desarrollo",items:[{title:"Integración de API y diseño de recopilación de datos",description:"Construimos una infraestructura de recopilación de datos compatible con varios protocolos como REST API, GraphQL, WebSocket y Webhook. Diseñamos un canal de adquisición de datos estable mediante la implementación de procesos de autenticación (OAuth, clave API, JWT), limitación de velocidad y mecanismos de reintento. También soportamos la integración de datos, la programación y la adquisición diferencial desde múltiples API."},{title:"Preprocesamiento y estructuración de datos",description:"Convierte los datos sin procesar en un formato que pueda ser analizado eficazmente por LLM, incluido el análisis sintáctico de JSON, la limpieza de datos, la normalización y el procesamiento de valores omitidos, y realiza una segmentación y resumen óptimos de los datos teniendo en cuenta las restricciones de la ventana contextual. La asignación de metadatos garantiza la trazabilidad de los resultados del análisis."},{title:"Análisis LLM y diseño rápido",description:"Maximice la precisión de los análisis de datos diseñando plantillas rápidas específicas para sus requisitos empresariales. Construimos pipelines de análisis para clasificación, resumen, análisis de sentimiento, detección de anomalías, generación de predicciones, etc. Utilizamos Function Calling y Structured Output para generar resultados en un formato que pueda integrarse fácilmente con sistemas posteriores."},{title:"Aplicación y funcionamiento",description:"Proporcionamos los resultados de los análisis a través de diversas interfaces, como FastAPI, aplicaciones web con Next.js, etc., Slack/Teams Bot y ejecución programada por lotes. Los cuadros de mando, las alertas de notificación y la generación automática de informes se implementan y entregan de forma que los usuarios empresariales puedan utilizarlos inmediatamente. También proporcionamos supervisión operativa, optimización de costes y mejora continua."}]},techStack:{title:"Pila tecnológica: Desarrollo de aplicaciones de IA",subtitle:"Plataforma y tecnología",categories:{apiIntegration:{title:"Integración API",items:["REST / GraphQL / WebSocket","OAuth 2.1 / API key / JWT","Webhook / Sondeo","Apache Airflow / Prefect","Apio / Temporal","requests / httpx / aiohttp","Limitación de velocidad / Reintento","Validación de datos (Pydantic)"]},dataProcessing:{title:"Tratamiento de datos",items:["Pandas / Polares","Analizador JSON / JSONL","Proceso de limpieza de datos","Apache Spark (procesamiento a gran escala)","dbt (conversión de datos)","Grandes esperanzas","Lago Delta / Iceberg","Streaming (Kafka / Flink)"]}

301,llmCloud:{title:"LLM en la nube",items:["OpenAI GPT-4o / GPT-4.1","Claude 3.5 / Claude 4","Géminis 2.0 / Géminis 2.5","Comando R+ (Cohere)","Mistral Large"]},llmLocal:{title:"LLM local",items:["Llama 3.3 / Llama 4","Mistral / Mixtral","Qwen 2.5 / Qwen 3","DeepSeek V3 / R1","Gemma 2 / Gemma 3","Phi-4","ELYZA / Golondrina (especializada en japonés)","Mando R (Cohere)"]},inference:{title:"Inferencia / Marco",items:["vLLM / SGLang","Ollama / llama.cpp","Proxy LiteLLM","Llamada a función","Salida estructurada","Motor de plantillas Prompt","LangChain / LlamaIndex","Canal de análisis propio"]},application:{title:"Capa de aplicación",items:["FastAPI / Litestar","Next.js / React","Slack SDK / Teams SDK","Streamlit / Gradio","Trabajos programados (Cron / Cloud Scheduler)","PostgreSQL / MongoDB","Redis (Caché / Cola)","Docker / Kubernetes"]}},updateNote:{title:"Principales puntos de actualización para 2026",description:"En el área de análisis LLM, la funcionalidad de salida estructurada de GPT-4.1 y Claude 4 ha madurado para estandarizar un estricto control de salida conforme con JSON Schema, y la ejecución en varias etapas de Function Calling permite la recopilación y el análisis de datos complejos a través de varias API en una única canalización. La ejecución en varias etapas de Function Calling permite la recopilación y el análisis de datos complejos a través de múltiples API en una única canalización. En el procesamiento de datos, Polars se ha convert

301ido en una alternativa consolidada a Pandas para el procesamiento de alta velocidad y, en combinación con el procesamiento de flujos, facilita la construcción de una infraestructura de análisis en tiempo real. En la capa de aplicación, la integración de herramientas externas mediante MCP se ha convertido en algo habitual, lo que acelera el desarrollo de aplicaciones agénticas en las que los LLM llaman a las API de forma autónoma para adquirir y analizar datos."}},cta:{title:"Contacte con nosotros",description:`Podemos consultarle desde la disposición de los requisitos.
302También se pueden considerar configuraciones PoC y LLM locales.`,primaryButton:"Contacte con nosotros",secondaryButton:"Consulta técnica"}},VL={seo:fL,header:kL,hero:bL,mission:yL,businessServices:vL,features:AL,businessProcess:xL,story:LL,contact:PL,footer:wL,cookiePolicy:SL,about:IL,portfolio:ML,common:jL,privacyPolicy:CL,termsOfService:DL,weatherMap:zL,agentDevelopment:TL,faq:EL,pricing:RL,aiTranscription:NL,keywordTracking:GL,onPremiseLLM:OL,vectorRAG:BL,aiAppDevelopment:KL},FL={home:{title:"Plataforma de soluções com tecnologia de IA - telet.ai",description:"Transforme as conversas com os seus clientes com análises de chamadas baseadas em IA. Obtenha insights em tempo real, análises de sentimentos e acompanhamento de desempenho para melhorar os resultados da sua equipa."},about:{title:"Informações da organização - telet.ai",description:"Learn about telet.ai's mission to transform customer conversations with AI. Meet our team and explore our journey."},privacy:{title:"Política de Privacidade - telet.ai",description:"Review telet.ai's privacy policy to understand how we handle your data and protect your privacy."},terms:{title:"Termos de Serviço - telet.ai",description:"Read telet.ai's terms of service for using this website and contacting us for consultations."},cookies:{title:"Política de Cookies - telet.ai",description:"Learn how telet.ai uses cookies and similar technologies (Cloudflare and Google Analytics)."},portfolio:{title:"Portfólio - telet.ai",description:"Explore o nosso portfólio de soluções de IA projetadas para melhorar as suas operações comerciais e a experiência dos clientes."},agentDevelopment:{title:"Concepção e desenvolvimento de agentes de IA empresarial - telet.ai",description:"Projete e crie agentes de IA de nível de produção adaptados aos seus fluxos de trabalho, ferramentas e necessidades de conformidade."},onPremiseLLM:{title:"Operações LLM no local - telet.ai",description:"Opere LLMs modernos dentro do seu ambiente para obter um forte controlo de dados, custos previsíveis e inferência de baixa latência."},vectorRAG:{title:"Vector RAG & Design de Índice - telet.ai",description:"Crie pipelines de recuperação de design e índices personalizados para cada fluxo de trabalho, a fim de melhorar as respostas fundamentadas."},aiAppDevelopment:{title:"Desenvolvimento de aplicações de IA - telet.ai",description:"Crie aplicações práticas com tecnologia LLM, integradas às suas APIs, dados e fluxos de trabalho comerciais."}},WL={brand:"telet.ai",nav:{services:"Serviços",portfolio:"Portfólio",about:"Informações da organização",contact:"Contato"}},qL={brandName:"telet.ai",tagline:"Comunicações com várias IAs",badge:"Inteligência de chamadas com tecnologia de IA",title:{line1:"Local e defensivo,",line2:"Expandir na nuvem,",line3:"Automatizado pelo agente."},description:"A inferência de borda no SLM garante a soberania dos dados e a integração da nuvem no LLM dimensiona a capacidade de processamento ao infinito; a adoção da arquitetura Agentic AI está começando a mostrar resultados nas principais empresas."},UL={badge:"Nossa missão",title:"Tornando o LLM mais acessível",paragraphs:['Você pode realmente ir tão longe?". -Essa surpresa é o que estamos buscando.','O potencial da modelagem de linguagem em larga escala (LLM) está se expandindo a cada dia. No entanto, queremos derrubar barreiras como "Estou preocupado em enviar dados confidenciais para APIs externas", "Aplicativos avançados de IA são apenas para grandes empresas" e "O custo de implementação não vale a pena".',"Somos especializados na criação de LLMs locais que são concluídos em seu ambiente. As informações altamente confidenciais dos clientes podem ser utilizadas com segurança sem serem expostas ao mundo exterior. Podemos maximizar os benefícios da IA e, ao mesmo tempo, garantir a segurança dos dados.","Ele cuida de tudo, desde a criação de documentos de rotina e a organização de dados até a análise especializada e o suporte a decisões. Nossa tecnologia proprietária de agentes oferece precisão comparável aos modelos de API.",'O LLM não substitui as pessoas. É uma ferramenta poderosa que permite que as pessoas exerçam mais criatividade, julgamento e conhecimento. Levamos a tecnologia de especial para comum - uma verdadeira "consumerização" da tecnologia.','Fique à vontade para nos consultar. Trabalharemos com você para descobrir "O que podemos fazer?" junto com você.'],animation:{label:"LLM",sublabel:"Tecnologia"}},_L={title:"Serviços",subtitle:"Serviços comerciais",services:[{title:"Projeto e desenvolvimento de agentes de IA para empresas",titleEn:"Agente de IA empresarial",description:"Projetar e desenvolver agentes de IA otimizados para requisitos de negócios, regras internas e escopo de dados. Suporte de LLM do tipo API externo para LLM local.",badges:["Design individual","Compatível com LLM","especialização em negócios"],link:"Detalhes."},{title:"Servidor LLM em promessa",titleEn:"Servidor LLM no local",description:"Executa o LLM em escala de 7B a 120B no servidor dedicado da empresa. Oferece um ambiente de uso seguro e flexível do LLM, evitando o risco de transmissão externa de dados.",badges:["7B-120B suportado","Chave de API emitida","Projeto de segurança"],link:"Detalhes."},{title:"Vetor RAG e design de índice",titleEn:"Arquitetura Vector RAG",description:"Separe e otimize os dados RAG por conteúdo comercial e lógica de análise. Projeto de índice para pesquisas rápidas e precisas.",badges:["

302Separação de índices","Pesquisa de alta velocidade","Suporte multilíngue"],link:"Detalhes."},{title:"Desenvolvimento de aplicativos de IA",titleEn:"Desenvolvimento de aplicativos de IA",description:"Desenvolver aplicativos práticos de IA com análise e geração pelo LLM em seu núcleo, utilizando APIs existentes e dados externos.",badges:["Vinculação de API","Análise do LLM","Design integrado"],link:"Detalhes."}]},HL={title:"Projetos de realização usando tecnologia de IA",subtitle:"Experiência em desenvolvimento de IA em uma ampla variedade de campos, incluindo assistentes multilíngues, previsão do tempo, LLM local e monitoramento de segurança.",viewDetails:"Ver detalhes →.",items:[{title:"Assistente multilíngue de IA",description:"Assistente de bate-papo multilíngue da RAG + LLM. Respostas altamente precisas a consultas em vários idiomas, incluindo japonês, inglês e chinês."},{title:"Sistema de previsão do tempo",description:"Aprende dados meteorológicos e gera previsões em tempo real. Previsões altamente precisas são geradas usando o reconhecimento de padrões de séries temporais e o RAG para pesquisar padrões semelhantes no passado."},{title:"Servidor interno / On-Prem Local LLM",description:"Fornecemos IA adaptada às suas operações comerciais, pronta para uso em sua empresa, com Open WebUI como ponto de entrada comum, pistas de inferência rápidas com Ollama/MLX e domínios de dados completamente separados para cada UID. Os documentos internos são capturados em um RAG com incorporação em inglês e tradução automática de consultas em japonês, e as habilidades de negócios, como resumo de notícias, organização de documentos e tarefas de rotina, são executadas com segurança lado a lado. O sistema de execução de habilidades é isolado como um contêiner OpenClaw e protegido por três camadas de borda Cloudflare e Nginx local. Fornecemos suporte abrangente para seleção de modelos, design de habilidades e políticas operacionais, e iniciamos a IA dedicada da sua empresa em um formato que pode ser usado desde o dia em que é implantado."},{title:"Previsão de chegada orbital interplanetária (AI)",description:"Obter dados planetários da API da NASA e interpretar cálculos de órbita no LLM. Gera relatórios sobre a janela de lançamento, o tempo necessário e a eficiência de combustível."},{title:"Análise de IA de dados imobiliários",description:"Colete informações imobiliárias por meio de raspagem da Web e analise-as com LLM + Python. Geração automática de tendências de preços, previsões de mercado e retorno sobre o investimento."},{title:"IA de adivinhação (adivinhação de quatro pilares)",description:"Reproduza a lógica dos Quatro Pilares do Destino em Python e interprete-a com o LLM. Adquira coordenadas e tempo da API Astronomy para avaliação personalizada da sorte e da compatibilidade."},{title:"Google Workspace AI integration",description:"Integração do LLM com Gmail, Drive, Calendar, etc. A IA auxilia no resumo de e-mails, na pesquisa de arquivos e no agendamento."},{title:"World NEWS Multilingualização",description:"Coleção em tempo real de notícias mundiais, traduzidas e resumidas em vários idiomas pelo LLM. Tradução natural e geração de resumos com base na compreensão contextual."},{title:"Monitoramento de registros de segurança AI Manager",description:"Monitoramento em tempo real dos registros de segurança, detecção e análise de ameaças pelo LLM e alertas imediatos por meio da correspondência com padrões de ataques anteriores pelo RAG."}]},$L={title:"Como prosseguir com o projeto",headline:"Tudo o que você precisa é de uma visão.",paragraphs:['Já se foi o tempo em que as diferenças de idioma eram uma barreira para os negócios e, embora usemos tradução por IA, podemos lidar com negociações e desenvolvimento de negócios em 14 idiomas por meio de nosso fluxo de trabalho exclusivo que faz uso total do compartilhamento de imagens. Até mesmo especificações complexas podem ser verificadas com imagens de amostra para evitar erros de comunicação. A única coisa que importa é "o que você deseja criar".',"O projeto começa com uma audiência inicial no aplicativo de negócios. Depois de um compartilhamento geral da visão do cliente e da noção dos problemas, é assinado um NDA (Non-Disclosure Agreement, acordo de confidencialidade). Depois de criar um ambiente no qual as informações confidenciais possam ser discutidas em sigilo, realizamos entrevistas detalhadas e c

302onfirmamos os requisitos. Com base no conteúdo, preparamos uma proposta e confirmamos a direção do projeto usando imagens de amostra. Com o entendimento estabelecido, preparamos as especificações e iniciamos o desenvolvimento.","Este fluxo é apenas uma introdução geral. Nós nos adaptaremos de forma flexível à sua situação e ao seu cronograma. Onde quer que você esteja no mundo, mesmo que não fale o mesmo idioma, as imagens e a IA darão suporte à sua comunicação. Por que você não se junta a nós para entrar no mercado de fronteira que ninguém ainda explorou? Deixe que o telet.ai seja seu parceiro para dar forma às suas ideias."],cards:[{title:"Audiência / NDA",description:"Após a audiência inicial por e-mail, é assinado um contrato de confidencialidade. Você pode nos fornecer detalhes com tranquilidade."},{title:"Planejamento e CA",description:"São fornecidas propostas e imagens de amostra. Há suporte para 14 idiomas, para que você possa prosseguir sem barreiras linguísticas."},{title:"Amostragem e início",description:"Prepararemos as especificações e iniciaremos os negócios mediante acordo."},{title:"Desenvolvimento, teste e operação",description:"Implementação rápida por meio de desenvolvimento ágil. Fornecemos suporte consistente, desde os testes até o suporte operacional."}]},QL={badge:"Estudos de caso",title:"História de introdução à IA",items:[{title:"Soluções de eficiência empresarial e redução de custos",subtitle:"Reduza a carga de trabalho diária e crie um ambiente no qual você possa se concentrar em seu negócio principal.",description:'Muitas pequenas e médias empresas (PMEs) enfrentam escassez de mão de obra e dificuldades de contratação. Mesmo quando novos funcionários são contratados, os custos de treinamento são altos e há problemas com as taxas de retenção. Nossa solução de IA oferece suporte a tarefas diárias, como automação do gerenciamento de orçamento, entrada e organização de dados, recuperação de documentos internos e geração automática de relatórios e documentos. Os assistentes de IA lidam com tarefas rotineiras que costumavam tomar muito tempo, 24 horas por dia, 365 dias por ano, sem erros. As empresas que introduziram a IA perceberam reduções significativas nas horas de trabalho. Desde "aumentar os recursos humanos" até "colaborar com a IA". Faremos o possível para apoiar a reforma do estilo de trabalho de sua empresa.'},{title:"Automação do suporte ao cliente",subtitle:"O serviço 24 horas melhora a satisfação do cliente e reduz os custos de mão de obra.",description:"Os chatbots com IA fornecem respostas automatizadas a perguntas frequentes, resposta primária e classificação de consultas, além de resposta em tempo real em vários idiomas. Resposta em tempo real em vários idiomas. Os operadores humanos podem se concentrar em casos complexos e melhorar a qualidade do atendimento ao cliente. Eleve a qualidade do seu atendimento a um novo patamar com o suporte ao cliente com IA."},{title:"IA de conhecimento e utilização de documentos",subtitle:"Ativos de conhecimento interno em um formato que pode ser utilizado imediatamente por qualquer pessoa",description:'Onde está esse manual?". Há informações que somente o antecessor conhece" - o conhecimento impessoalizado impede o crescimento de uma empresa. Nossa tecnologia RAG (geração de extensão de pesquisa) permite que você recupere as informações necessárias de manuais internos, perguntas frequentes e documentos anteriores simplesmente fazendo perguntas em linguagem natural. Ela agiliza o treinamento de novos funcionários, digitaliza o conhecimento tácito dos veteranos e permite o compartilhamento de conhecimento entre os departamentos. Os documentos existentes podem ser usados como estão, portanto, não há necessidade de criar novos documentos.'},{title:"IA de análise de imagens para cuidados médicos e de enfermagem",subtitle:"Olhos atentos, suporte 24 horas por dia por IA",description:"Nas áreas de assistência médica e de enfermagem, é necessário cuidar de um grande número de pacientes e usuários com uma equipe limitada. Nossa IA de análise de imagem/vídeo detecta anormalidades em tempo real a partir de imagens de câmeras. Ela detecta automaticamente doenças infecciosas por meio da detecção de febre, alerta imediatamente se um paciente cair ou se afastar e detecta alterações n

302a condição física por meio da análise de expressões faciais. Reduzindo a carga de patrulhas noturnas, respondendo rapidamente a emergências e automatizando tarefas de registro, a equipe pode se concentrar em suas tarefas de atendimento primário. A instalação no local também está disponível para garantir a privacidade."}]},JL={title:"Entre em contato conosco",description:"Podemos consultá-lo desde a fase de planejamento dos requisitos e também podemos considerar configurações de PoC e LLM locais.",openForm:"Formulário aberto",closeForm:"Fechar o formulário",form:{name:"Seu nome",namePlaceholder:"Taro Yamada",email:"Endereço de e-mail",emailPlaceholder:"[email protected]",subject:"assunto",subjectPlaceholder:"Assunto de sua consulta",message:"Mensagem",messagePlaceholder:"Preencha sua consulta",required:"*",submit:"Enviar",submitting:"Enviando...",turnstileMessage:'A autorização "Não sou um robô" é necessária para o envio.',turnstileError:"Falha ao carregar a autorização de segurança. Favor recarregar.",errorRetry:"Tente novamente."}},ZL={brand:"telet.ai",tagline:"Comunicações com várias IAs",description:"Inove seus negócios com agentes de IA. Crie valor a partir do diálogo.",sections:{product:{title:"Produto",features:"Recurso",benefits:"introdução",testimonials:"Depoimentos de clientes"},services:{title:"Serviço",agentDev:"Projeto e desenvolvimento de agentes de IA para empresas",onPremise:"Servidor LLM em promessa",vectorRag:"Vetor RAG e design de índice",aiApp:"Desenvolvimento de aplicativos de IA"},company:{title:"Informações da organização",about:"Sobre o telet.ai",profile:"Perfil do representante",contact:"Entre em contato conosco"},process:{title:"Como fazemos negócios",hearing:"Audiência / NDA",planning:"Planejamento e CA",sampling:"Amostragem e início",development:"Desenvolvimento, teste e operação"}},copyright:"© 2026 telet.ai. Todos os direitos reservados.",legal:{privacy:"Política de privacidade",terms:"Termos de uso",cookies:"Política de cookies"}},XL={title:"Política de cookies",subtitle:"Esta seção descreve os cookies e outras tecnologias (Cloudflare / Google Analytics) usados neste site.",lastUpdated:"Última atualização: 22 de janeiro de 2026",intro:'telet.ai ("nós", "nos", "nosso") ("Nós") pode usar cookies e tecnologias semelhantes para fornecer e melhorar este Site e garantir sua segurança. Não definimos nossos próprios cookies de rastreamento especiais para fins de publicidade (embora a Cloudflare e o Google Analytics possam definir cookies ou tecnologias semelhantes de acordo com suas respectivas especificações de serviço).',sections:{what:{title:"1. o que é um cookie?",description:"Cookies são pequenos arquivos de texto que são armazenados no navegador quando você visita um site."},howWeUse:{title:"Objetivo de nosso uso de cookies, etc.",description:"Podemos usar cookies, etc. principalmente para os seguintes fins",items:["Para estabilizar a exibição e a operação do site","Para detectar e impedir o acesso não autorizado e o tráfego malicioso","Para entender o status do acesso e ajudar a melhorar o site"]},googleAnalytics:{title:"3. uso do Google Analytics",description:"Usamos o Google Analytics 4 (GA4) fornecido pelo Google LLC para análise de acesso.",items:["As informações coletadas podem incluir páginas visualizadas, tempo gasto, fonte de referência, informações sobre o dispositivo e o navegador, etc.","Não usamos diretamente informações de identificação pessoal (nome, endereço de e-mail, etc.) com a finalidade de enviá-las ao Google Analytics.","O tratamento de dados pelo Google está sujeito aos termos e políticas do Google"]},cloudflare:{title:"4. uso do Cloudflare",description:"Usamos os serviços da Cloudflare, Inc. para medidas de entrega e segurança deste Site. A Cloudflare pode definir e fazer referência a cookies, etc. para fins como proteção contra DDoS e prevenção de bots.",items:["A finalidade principal é a otimização da segurança e da comunicação, e não se destina a ser rastreada para a entrega de anúncios","O tratamento de dados pela Cloudflare está sujeito aos termos e políticas da Cloudflare"]},noSpecialCookies:{title:"5. nossos próprios cookies especiais",de

302scription:"Não configuramos nossos próprios cookies especiais para fins de publicidade de direcionamento comportamental, etc. O uso de cookies, etc., limita-se principalmente ao escopo da operação, segurança e análise de acesso do site."},choices:{title:"6. gerenciamento de cookies (opt-out)",description:"Se os cookies estiverem desativados, algumas funções poderão não estar disponíveis.",items:["Os cookies podem ser excluídos ou desativados nas configurações do navegador","Se você quiser desativar a medição pelo Google Analytics, use o método de desativação fornecido pelo Google","O uso do modo privado de um navegador, etc., pode limitar as informações armazenadas."]},changes:{title:"7. alterações a esta política",description:"Podemos revisar esta política em resposta a alterações em leis e regulamentos ou mudanças nos serviços utilizados. A política revisada entrará em vigor quando for publicada nesta página."},contact:{title:"8. entre em contato conosco",description:"Se tiver alguma dúvida sobre esta política, entre em contato conosco pelo e-mail",company:"telet.ai",contactLink:"Ir para o formulário de consulta"}}},YL={hero:{title:"Sobre o telet.ai",subtitle:"Suporte cuidadoso ao desenvolvimento de IA que só pode ser fornecido por uma equipe pequena e personalizada."},story:{title:"Informações da organização",content:"A telet.ai começou com uma pergunta. Por que tantas empresas não estão conseguindo aproveitar os insights que estão nas inúmeras interações entre as empresas e seus clientes? A equipe, liderada por um representante com experiência em pesquisa de IA e desenvolvimento de software empresarial, foi criada para enfrentar esse problema de frente. Não somos uma organização de grande porte. Por isso, somos capazes de enfrentar seus desafios em profundidade e propor apenas as soluções realmente necessárias. Nosso ponto forte é a capacidade de projetar arquiteturas híbridas que integram LLM local e LLM na nuvem. Podemos proteger os dados confidenciais da sua empresa em um ambiente de nuvem dedicado e, ao mesmo tempo, realizar inferência avançada de IA de maneira escalonável. Fornecemos uma CLI para desenvolvedores e uma interface de usuário de bate-papo para a equipe de linha de frente, criando uma base para o uso de IA em toda a sua organização. E nos concentramos em ser a solução ideal, em vez de ser a mais avançada. O descarte de funções de acordo com o orçamento e a revisão de apenas uma configuração podem fazer uma grande diferença no custo. Entregamos o que você precisa, na quantidade que você precisa. Nosso suporte atencioso ao desenvolvimento de IA só é possível porque somos pequenos e pessoais. Para resolver a escassez de mão de obra e apoiar o crescimento de sua empresa. Essa é a promessa da telet.ai."},representative:{title:"Representante do telet.ai labo",name:"Keita Okuizumi",tagline:"Para o céu, para o mar e para o código - uma paixão pelo desenvolvimento de IA nascida de uma carreira incomum",paragraphs:['Como ex-piloto privado (PPL) e portador de licença de navio, há muitos anos lido com o clima no ar e no mar. No cockpit e na casa do leme, observei as mudanças no barômetro, decifrei as linhas de pressão isobáricas e as comparei com o movimento do vento e das nuvens. O senso de "ler dados, julgar e agir" que desenvolvi por meio desse processo repetitivo é o ponto de partida para mim hoje.','Comecei a aprender programação em Python aos 57 anos de idade. O ímpeto foi uma simples curiosidade, mas assim que comecei a escrever código, fiquei fascinado com as possibilidades dos agentes de IA. Criei aplicativos com minhas próprias mãos, executei-os e os aprimorei. Durante o processo, percebi algo. Em projetos anteriores, eu estava envolvido como não engenheiro, e o desenvolvimento era sempre feito por outra pessoa. No entanto, quando comecei a escrever o código eu mesmo, comecei a ver a essência do problema. Este é o gargalo aqui" ou "Este projeto não pode ser expandido" - ao ter o ponto de vista de um engenheiro, a velocidade de realização do que eu queria fazer aumentou drasticamente.',"Desenvolvimento de serviços meteorológicos combinando experiência de campo com ciência de dados, criação de agentes de IA para melhorar a eficiência operacional e vários projetos experimentais. À medida que continuei a trabalhar com minhas mãos, convenci-me não apenas de que a tecnologia era interessante, mas também de que deve haver pessoas que precisam dela.","Nosso objetivo não é perseguir a vanguarda. Enfrentamos os problemas e o orçamento do cliente e fornecemos apenas o que é realmente necessário a um custo razoável. Como não somos uma organização de grande porte, podemos oferecer suporte ao desenvolvimento personalizado de acordo com as necessidades de cada cliente. Queremos usar essa tecnologia para o benefício da sociedade - por isso lançamos o telet.ai."]},contact:{title:"Sobre nós",company:"telet.ai labo",ceo:"Representante: OKUIZUMI Keita",button:"Entre em contato conosco"},studios:{tokyo:{title:"Estúdio de Tóquio",address:"#301 First Hills A Musashikoganei, 3-3-21 Nukuiminamicho, Koganei-shi, Tóquio 184-0014, Japão"}}},eP={title:"Portfólio",subtitle:"Veja nosso histórico de utilização da tecnologia de IA",techStackLabel:"Pilhas de tecnologia",viewProject:"Exibir projeto",privateProject:"Projetos privados",visualizationLabel:"visualização de dados",cta:{title:"Entre em contato conosco",description:"Fique à vontade para entrar em contato conosco para discutir seu projeto ou para consultas técnicas.",button:"Entre em contato conosco"},projects:{"traffic-route-ai":{title:"Sistema de Seleção de Rotas de Transporte com IA",subtitle:"Planejamento de rotas de transporte público na Malásia",description:"Sistema que busca conexões de ônibus e trem entre origem e destino e compara opções por horário de chegada, duração e número de baldeações. Filtros de apenas ônibus ou trem, busca de estações e paradas próximas e uma interface multilíngue ajudam a escolher rotas para o dia a dia e viagens.",techStack:["Rotas de Transporte Público","Integração de Ônibus e Trem","Comparação de Rotas","Interface Multilíngue"]},"multilingual-assistant":{title:"Assistente multilíngue de IA",description:"Assistente de bate-papo inteligente para consultas em vários idiomas, criando 1.573 índices de vetores e implementando um controle preciso das respostas por meio de ramificação condicional, filtragem de palavras NG e acionamento de palavras-chave. Respostas consistentes e altamente precisas às perguntas dos usuários em vários idiomas, incluindo japonês, inglês e chinês, são geradas por meio de pesquisa semântica entre idiomas.",techStack:["RAG","Pesquisa de vetores","Incorporação multilíngue","LLM","lógica de ramificação condicional"]},"weather-forecast":{title:"Previsão meteorológica de IA com a API de clima",description:"Desenvolveu um sistema de IA que aprende com dados meteorológicos anteriores e faz previsões em tempo real das condições meteorológicas recentes. O LLM analisa dados anteriores obtidos da API da JMA, do OpenWeatherMap etc. e gera previsões de curto prazo para temperatura, probabilidade de precipitação, velocidade do vento etc. Combinando o reconhecimento de padrões de dados de séries temporais com a pesquisa de padrões de similaridade do passado pelo RAG, o sistema obtém previsões altamente precisas que complementam os modelos estatísticos convencionais.",techStack:["Vinculação da API de clima","LLM","RAG","Análise de dados de séries temporais","processamento em tempo real"]},"local-llm":{title:"Servidor interno / On-Prem Local LLM",description:"Fornecemos IA adaptada às suas operações comerciais, pronta para uso em sua empresa, com Open WebUI como ponto de entrada comum, pistas de inferência rápidas com Ollama/MLX e domínios de dados completamente separados para cada UID. Os documentos internos são capturados em um RAG com incorporação em inglês e tradução automática de consultas em japonês, e as habilidades de negócios, como resumo de notícias, organização de documentos e tarefas de rotina, são executadas com segurança lado a lado. O sistema de execução de habilidades é isolado como um contêiner OpenClaw e protegido por três camadas de borda Cloudflare e Nginx local. Fornecemos suporte abrangente para seleção de modelos, design de habilidades e políticas operacionais, e iniciamos a IA dedicada da sua empresa em um formato que pode ser usado desde o dia em que é implantado.",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"Previsão de chegada orbital interplanetária (AI)",description:"Desenvolveu um sistema para analisar pontos de partida ideais para transferência interplanetária, obtendo dados de posição planetária, elementos orbitais e parâmetros de gravidade da API da NASA. Calcula cálculos de órbita considerando a órbita de transferência de Hohmann e a assistência gravitacional usando API e Python, e exibe a janela de lançamento, o tempo necessário e a eficiência de combustível. O sistema é usado como uma ferramenta de simulação para instituições educacionais e empresas relacionadas ao espaço. *A função AI foi removida no momento, o cálculo está em andamento com a API e o Python.",techStack:["NASA API (Horizons, SPICE)","Cálculo da dinâmica orbital","Python","visualização de dados"]},"real-estate":{title:"Análise de IA de dados imobiliários",description:"Desenvolveu um sistema que coleta informações imobiliárias de uma região específica por meio de raspagem da Web e realiza análises avançadas de dados usando LLM e Python. Variáveis como preço da propriedade, preço unitário tsubo, idade do edifício e distância da estação são extraídas, e a análise de tendências de preços, previsões de mercado e simulações de retorno de investimento são geradas automaticamente. Os recursos também foram extraídos de descrições de propriedades não estruturadas usando LLM para fornecer uma análise de mercado abrangente combinada com dados quantitativos. *Oferta agora disponível como uma segunda versão (a versão COMPLETA é fornecida separadamente)",techStack:["Raspagem da Web (Scrapy, Playwright)","Python","Pandas","Análise do LLM","visualização de dados"]},"fortune-ai":{title:"Sistema de avaliação de IA dos quatro pilares",subtitle:"Reproduzindo a sabedoria tradicional com a tecnologia.",paragraphs:["O Four Pillars of Divination é a culminação de milhares de anos de adivinhação oriental. Esse sistema reproduz sua lógica tradicional de adivinhação totalmente em Python, combinada com a interpretação e a geração de conselhos usando LLM (Large Language Models). A IA reproduz fielmente o processo de avaliação realizado por um adivinho especialista.","Depois de inserir a data, a hora e o local de nascimento, o sistema recupera os horários do nascer e do pôr do sol e as informações de coordenadas naquele momento a partir da API de Astronomia. Com base nesses dados, o sistema calcula com precisão o signo do zodíaco, os dez deuses e o equilíbrio dos cinco elementos. A lógica baseada n

302a ciência de adivinhação dos Quatro Pilares do Destino é indexada em Python e combinada com dados estatísticos e informações de RAG (Retrieval Augmentation Generation) para melhorar a precisão da análise da fórmula de vida.","Os dados calculados sobre o estilo de vida são explicados em linguagem natural pelo LLM de maneira fácil de entender. São gerados resultados de avaliação personalizados, incluindo o fluxo da sorte, a compatibilidade dos relacionamentos interpessoais e a ocupação adequada para você. Você pode obter respostas com a mesma profundidade como se tivesse consultado uma cartomante profissional.","Atualmente, ele está disponível em uma segunda versão. Você pode experimentar a análise básica do kumishiki e a análise da sorte. A versão completa, que inclui análises mais detalhadas e conselhos multifacetados, está disponível separadamente."],techStack:["Python","API de astronomia (Sunrise-Sunset, TimeZoneDB)","Lógica de quatro pilares","LLM","Cálculo de coordenadas e tempo"]},"google-workspace":{title:"Google Workspace AI integration",description:"Desenvolveu um sistema que vincula o Google Workspace (Gmail, Drive, Agenda, Docs, Sheets) ao LLM para ler, editar e processar automaticamente dados comerciais. A IA auxilia nas tarefas diárias, como resumir o conteúdo de e-mails, gerar rascunhos para respostas automáticas, pesquisar e analisar arquivos do Drive, extrair programações do Calendário e propor ajustes. A operação do espaço de trabalho a partir do agente também é realizada por meio de conexão API segura com autenticação OAuth e design de ferramenta compatível com MCP. A segunda versão já está disponível (a versão COMPLETA será fornecida separadamente).",techStack:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"]},"world-news":{title:"World NEWS Multilingualização",description:"Desenvolvimento de um sistema que coleta artigos em tempo real de fontes de notícias de todo o mundo e realiza tradução, resumo e categorização multilíngue usando LLM. Traduz artigos originais em inglês, chinês, francês, árabe e outros idiomas para o japonês e os organiza por tópico. O monitoramento eficiente de notícias internacionais não é obtido por meio de uma simples tradução automática, mas por uma tradução natural baseada na compreensão contextual usando LLM e na geração de resumos que extraem os pontos principais do artigo. *Oferta agora disponível em uma segunda versão (a versão COMPLETA é fornecida separadamente).",techStack:["Notícias API","Coleção de RSS","Tradução e Resumo do LLM","processamento multilíngue","Auto-categorização","processamento em lote programado"]},"security-monitoring":{title:"Monitoramento de registros de segurança AI Manager",description:"Desenvolveu um sistema para monitoramento em tempo real de registros de segurança corporativa e detecção e análise avançadas de ameaças usando LLM. Várias fontes de registro, como firewalls, IDS/IPS, registros de autenticação e registros de aplicativos, são coletadas e analisadas por um módulo Python e comparadas com padrões de ataques anteriores, informações de vulnerabilidade e casos de incidentes acumulados no RAG. Quando um padrão anormal é detectado, o LLM explica o nível da ameaça, o escopo do impacto e a resposta recomendada em linguagem natural e alerta imediatamente a pessoa responsável. Se o mesmo padrão se repetir, um alerta de alta prioridade é emitido automaticamente para acelerar a resposta ao incidente.",techStack:["Módulo de monitoramento Python","Coleta de registros (Fluentd, Logstash)","RAG","Banco de dados de padrões de ameaças","Análise do LLM","Notificações de alerta (Slack, PagerDuty)"]}}},aP={learnMore:"Leia mais",getStarted:"começar a ...",contactUs:"Entre em contato conosco",backToHome:"Voltar para a página inicial"},nP={title:"Política de privacidade",subtitle:"Política de privacidade",lastUpdated:"Última atualização: 23 de janeiro de 2026",intro:'O telet.ai (o "Serviço") considera a proteção de suas informações pessoais uma responsabilidade importante e estabeleceu a seguinte política de privacidade.',sections:{collection:{title:"1. informações a serem coletadas",description:"O Serviço poderá coletar as seguintes informações quando você entrar em contato conosco ou usar o Serviço.",items:["Endereço de e-mail","Seu nome (opcional)","Detalhes da consulta","Outras informações fornecidas voluntariamente por você"]},purpose:{title:"2. finalidade do uso das informações",description:"As informações pessoais coletadas serão usadas somente para as seguintes finalidades",items:["Atender e responder a consultas","Contato e informações sobre nossos serviços","Comunicação necessária para o andamento do projeto","Análise para melhoria do serviço (em um formato que não identifique indivíduos)"]}

302,thirdParty:{title:"3. fornecimento a terceiros",description:"O Serviço não fornecerá suas informações pessoais a terceiros, exceto nos seguintes casos",items:["Se o cliente consentir","Quando houver uma solicitação de divulgação com base em leis e regulamentos","Quando necessário para a proteção da vida, do corpo ou da propriedade de uma pessoa"]},security:{title:"4. gerenciamento de informações",description:"O Serviço gerenciará adequadamente as informações pessoais coletadas e tomará medidas de segurança razoáveis para impedir o acesso não autorizado, a perda, o vazamento e a falsificação."},email:{title:"5. tratamento de e-mails",description:"O Serviço tratará os e-mails de acordo com a política a seguir.",items:["Usaremos o conteúdo de seu e-mail somente para responder à sua consulta e fornecer nossos serviços.","Não venderemos, alugaremos ou compartilharemos seu endereço de e-mail com terceiros","Podemos lhe enviar e-mails informativos sobre nossos serviços. Se desejar cancelar a assinatura, use o link de cancelamento no e-mail ou entre em contato conosco usando as informações de contato abaixo.","A criptografia SSL/TLS é usada nas comunicações por e-mail para garantir a segurança das comunicações","Excluiremos seu e-mail com segurança após armazená-lo por um período de tempo apropriado."]},cookies:{title:"6. uso de cookies",description:"Os cookies podem ser desativados nas configurações de seu navegador."},external:{title:"7. uso de serviços externos",description:"Este serviço pode usar os seguintes serviços externos. Consulte os respectivos links para conhecer a política de privacidade de cada serviço.",items:["Google Analytics (access analysis)","Outros serviços em nuvem necessários para as operações comerciais"]},changes:{title:"8. alterações na política de privacidade",description:"Esta política está sujeita a revisão sem aviso prévio, de acordo com alterações nas leis e regulamentos ou alterações no conteúdo do serviço. A política revisada entrará em vigor quando for publicada nesta página."},contact:{title:"9. entre em contato conosco",description:"Se tiver alguma dúvida sobre esta política, entre em contato conosco pelo e-mail",company:"telet.ai",contactLink:"Ir para o formulário de consulta"}}},tP={title:"Termos de uso",subtitle:"Estes são os termos e condições para o uso deste site e para agendar uma consulta, fazer consultas e solicitar informações ao telet.ai.",lastUpdated:"Última atualização: 22 de janeiro de 2026",intro:'Estes Termos de Uso (doravante denominados "Termos") Estes Termos de Uso (doravante denominados "Termos") aplicam-se ao website operado pela telet.ai (doravante denominada "nós", "nos", "nosso" ou "nossa"). (doravante denominada "nós", "nos" ou "nosso"). (doravante denominada "Site") operado pela telet.ai (doravante denominada "Nós"). O Site é fornecido principalmente como uma página de destino para agendamentos de consultas, consultas e solicitações de documentos, e não prevê a criação de contas ou funções de cobrança paga no Site.',sections:{scope:{title:"1. escopo de aplicação",description:'Estes Termos e Condições se aplicam a todos os visitantes e usuários do Site ("Usuários"). Os Termos de Uso se aplicam a todos os visitantes e usuários do Site (doravante denominados "Usuários"). Caso um NDA separado, acordo de terceirização, contrato individual etc. seja celebrado entre nós e um Usuário, esse documento terá precedência sobre os Termos e Condições.'},prohibited:{title:"2. itens proibidos",description:"Ao usar o Site, os Usuários não devem se envolver em nenhum dos seguintes atos",items:["Atos que violam leis e regulamentos ou a ordem e a moral públicas","Qualquer ato que infrinja os direitos de propriedade intelectual, a privacidade ou outros direitos ou interesses nossos ou de terceiros","Atos que interfiram na operação deste site ou dos sistemas relacionados por meio de acesso não autorizado, carga excessiva, sabotagem, etc.","Enviar informações falsas ou fazer-se passar por um terceiro em formulários de consulta, etc.","Raspagem, engenharia reversa ou acesso automatizado excessivo sem nossa permissão prévia"]},disclaimer:{title:"3. isenção de responsabilidade",description:'fornecemos este site "no estado em que se encontra" e "conforme disponível". Na medida máxima permitida por lei, nos isentamos de todas as garantias com relação a este site.',items:["As informações contidas neste site estão sujeitas a alterações ou exclusões sem aviso prévio.","Não garantimos a precisão, a integridade ou a adequação das informações contidas neste site a uma finalidade específica.","Não seremos responsáveis por quaisquer danos resultantes de seu uso deste site ou da confiança nas informações aqui contidas.","As respostas a consultas e questionamentos são apenas para informações gerais e não constituem aconselhamento jurídico, tributário, médico ou de outro tipo."]},ip:{title:"4. direitos de propriedade intelectual",description:"Todos os direitos sobre o texto, as imagens, os designs, as marcas registradas, os logotipos etc. deste site pertencem a nós ou aos detentores dos direitos. Você não pode usá-los sem a permissão dos detentores dos direitos."},links:{title:"5. links para sites externos",description:"Este site pode conter links para sites de terceiros. Não somos responsáveis pelo conteúdo ou pela operação dos sites vinculados."},changes:{title:"6. modificação destes Termos e Condições",description:"Reservamo-nos o direito de alterar estes Termos e Condições de tempos em tempos, conforme necessário. Os Termos de Uso alterados entrarão em vigor quando publicados no Site."},governingLaw:{title:"7. lei aplicável e jurisdição",description:"Estes Termos e Condições serão regidos e interpretados de acordo com as leis do Japão. Em caso de qualquer disputa relacionada a este Site, o Tribunal Distrital de Tóquio terá jurisdição exclusiva como tribunal de primeira instância."},contact:{title:"8. entre em contato conosco",description:"Se tiver alguma dúvida sobre estes Termos e Condições, entre em contato com",company:"telet.ai",contactLink:"Ir para o formulário de consulta"}}}

302,iP={title:"movimento regular",subtitle:"A IA prevê o",description1:"As informações meteorológicas são fornecidas em tempo real por cada país. Seu país é exibido no centro do mapa. O clima de ontem e o de hoje são registros reais – as nuvens de chuva deveriam estar no seu céu naquela data e hora.",description2:"No entanto, essa animação também mostra os movimentos climáticos futuros.",description3:"Vetores de dados passados e busca de padrões em alta velocidade. Os padrões climáticos também podem ser previstos com base no ambiente ao redor. Da mesma forma, é possível prever a colheita, a produção e as vendas calculando elementos (i) do ambiente e valores estatísticos avançados do desempenho anterior e fazendo com que a IA os analise.",refresh:"atualização (por exemplo, memória)",loading:"Carregando o mapa meteorológico..."},sP={hero:{badges:["Empresa","LLM local","Agente personalizado"],title:"Projeto e desenvolvimento de agentes de IA para empresas",description:"Projetar e criar agentes de IA dedicados e adaptados aos requisitos da empresa"},mission:{title:"Serviços de desenvolvimento de agentes de IA personalizados",description:"Desenvolvemos consistentemente agentes de IA personalizados que abordam os problemas comerciais exclusivos de sua empresa, desde o projeto até a implementação e a operação. Nosso projeto arquitetônico enfatiza a compatibilidade com sistemas de missão crítica existentes e ambientes de SaaS, permitindo uma integração suave com APIs, bancos de dados e ferramentas internas. Nossos engenheiros estão familiarizados com LangChain, AutoGen, CrewAI e outras estruturas importantes, Nossos engenheiros estão familiarizados com LangChain, AutoGen, CrewAI e outras estruturas importantes e proporão a melhor solução para a pilha de tecnologia e a estrutura operacional da sua empresa. Não desenvolvemos apenas chatbots únicos, mas também criamos sistemas multiagentes nos quais vários agentes cooperam entre si para realizar tarefas e fluxos de trabalho agênticos que funcionam em conjunto com ferramentas externas. Apoiamos a maximização do valor comercial trazido pelos agentes de IA por meio de uma parceria de longo prazo que inclui melhoria contínua após a implementação, seguindo atualizações de modelos e manutenção de sistemas de monitoramento operacional.",imageAlt:"Serviços de desenvolvimento de agentes de IA personalizados"},summary:{title:"Tendências de desenvolvimento de agentes",paragraphs:['De 2025 a 202

3026, o desenvolvimento do Agente de IA está passando da fase de demonstração para a fase de implementação corporativa em grande escala. O conceito de "Agentic AI" criou raízes, e sistemas autônomos de execução de negócios que vão além de meros chatbots começaram a operar em vários setores.','Agentes que usam computador" que controlam diretamente navegadores e desktops, como o Operator da OpenAI, o Computer Use da Anthropic e o Project Mariner do Google, tornaram-se práticos. O MCP (Model Context Protocol) é um ecossistema em expansão, que permite a integração plug-and-play com milhares de ferramentas.',"As arquiteturas de vários agentes atingiram a maturidade, com agentes especializados trabalhando juntos para realizar tarefas complexas, como gerenciamento de projetos, suporte ao cliente e análise de dados. A padronização do protocolo A2A (Agent-to-Agent) também avançou na colaboração de agentes entre diferentes fornecedores.",'O modelo de governança evoluiu de "Human-in-the-Loop" para "Human-on-the-Loop" para implementações corporativas, com um mecanismo de auditoria de decisões pós-agente e escalonamento automático com base no nível de risco como recursos padrão, acelerando a adoção em setores regulamentados, como finanças, saúde e manufatura. A adoção desse sistema está se acelerando em setores regulamentados, como o financeiro, o médico e o de manufatura.']},features:{title:"Design e desenvolvimento",subtitle:"Design e desenvolvimento",items:[{title:"Design de agentes com base em requisitos comerciais",description:"Extrair casos de uso da análise do fluxo de negócios, decompor tarefas e esclarecer as responsabilidades do agente. Determinamos se o processamento pode ser concluído por um único agente ou se requer coordenação de vários agentes e selecionamos os padrões de inferência ideais, como ReAct, Plan-and-Execute e Reflexion."},{title:"Integração de RAGs/ferramentas/fluxos de trabalho",description:"Pipelines RAG altamente precisos que combinam Embedding Generation, Chunk Strategy e Hybrid Search, conexão padronizada a APIs e bancos de dados externos com definições de ferramentas compatíveis com MCP e orquestração de fluxo de trabalho em várias etapas usando LangGraph e CrewAI. Orquestre fluxos de trabalho de várias etapas com LangGraph e CrewAI."},{title:"Projeto de segurança e autorização",description:"O controle de acesso das funções do agente por RBAC/ABAC, as contramedidas de injeção de prompt e a detecção e o mascaramento de PII são implementados. Os registros de auditoria de todas as entradas e saídas são mantidos, e há suporte para a operação em ambiente compatível com SOC2 e ISO27001."},{title:"Implementação, incluindo operação e monitoramento",description:"Implemente a visualização de traços com LangSmith, Langfuse, etc., e o monitoramento em tempo real da latência, do consumo de tokens e da taxa de erros. Crie um ciclo de aprimoramento contínuo, incluindo alertas de detecção de anomalias, infraestrutura de teste A/B e teste de regressão ao atualizar modelos."}]},techStack:{title:"Pilhas de tecnologia",subtitle:"Plataforma e tecnologia",categories:{agentFramework:{title:"Estrutura do agente",items:["AutoGen 0.4 / AG2","CrewAI","LangGraph","SDK de agentes da OpenAI","MCP antrópico","Google ADK (Agent Development Kit)","Núcleo semântico","Plataforma proprietária do agente"]},apiLayer:{title:"Camada de API",items:["FastAPI / Litestar","gRPC / Conectar","Federação GraphQL","Eventos enviados pelo servidor (SSE)","WebSocket / NATS","OAuth 2.1 / OIDC","Gateway de API (Kong / Envoy)","Limite de taxa adaptável"]},vectorDb:{title:"Vetor DB / RAG",items:["Pinha","Weaviate","Qdrant","Milvus","ChromaDB","pgvetor","FAISS","Turbopuffer"]}

302,infrastructure:{title:"Infraestrutura",items:["Kubernetes (EKS / GKE / AKS)","Docker / Podman","Cluster de GPU no local","Nuvem privada","Sem servidor (Lambda / Cloud Run)","OpenTelemetry / Langfuse","Terraform / Pulumi","ArgoCD / Flux"]}},updateNote:{title:"Principais pontos de atualização para 2026",description:"Na estrutura do agente, estão surgindo SDKs/protocolos oficiais dos principais fornecedores, como o OpenAI Agents SDK, o Anthropic MCP e o Google ADK; na camada de API, o gRPC e o SSE se tornaram o padrão para a comunicação entre agentes, e a transição para o OAuth 2.1 está em andamento. No Vector DB, o Qdrant e o Turbopuffer estão ganhando atenção por sua pesquisa de alta velocidade."}},cta:{title:"Entre em contato conosco",description:`Podemos consultá-lo desde a definição dos requisitos.
303As configurações de PoC e LLM local também podem ser consideradas.`,primaryButton:"Entre em contato conosco",secondaryButton:"Consulta técnica"}},rP={title:"Perguntas frequentes",subtitle:"PERGUNTAS FREQUENTES",items:[{question:"Posso usar meus manuais e FAQs existentes sem modificações?",answer:"Sim, você pode usá-lo; ele é compatível com a maioria dos formatos, incluindo PDF, Word, Excel, HTML e arquivos de texto. Basta fazer o upload de seus documentos internos existentes como estão, e a IA aprenderá o conteúdo e o usará para responder às suas perguntas. Não é necessária nenhuma conversão de formato ou processamento especial."},{question:"É seguro usá-lo com dados que contêm informações confidenciais?",answer:"Sim, você pode usar nosso serviço com segurança. No local (servidores internos) ou em um ambiente de nuvem privada, os dados nunca são transmitidos para fora da empresa. Podemos propor configurações para atender a seus requisitos de segurança."},{question:"Quanto tempo leva para ser implementado?",answer:"Um bot de perguntas frequentes padrão pode ser implementado em apenas 2 a 4 semanas. Se for necessária a integração com sistemas existentes ou personalização avançada, estimamos de 1 a 3 meses. Também podemos começar com uma PoC (prova de conceito) em pequena escala."},{question:"Você usa APIs externas, como ChatGPT ou Claude?",answer:"A escolha está disponível mediante solicitação. Oferecemos suporte a configurações que usam APIs externas (OpenAI, Anthropic, Google, etc.) e configurações que usam LLMs de código aberto (Llama, Mistral, Qwen, etc.) que são executadas totalmente localmente. Podemos propor a melhor configuração para seus requisitos de custo, segurança e desempenho."},{question:"Qual é a precisão da resposta da IA?",answer:"Dependendo do aplicativo e da qualidade dos dados, um sistema RAG adequadamente projetado pode retornar respostas precisas a perguntas para as quais existem documentos relevantes com 90% ou mais de precisão. Mecanismos para controlar a halcinação (geração de informações errôneas) também são incorporados como padrão."},{question:"Vocês oferecem suporte a outros idiomas além do japonês?",answer:"Sim, o suporte multilíngue está disponível. Oferecemos suporte a todos os principais idiomas, inclusive japonês, inglês, chinês e coreano. Em vez de ter modelos separados para cada idioma, um único sistema pode lidar com consultas em vários idiomas."},{question:"Você pode se integrar aos sistemas existentes (CRM, ferramentas de bate-papo, etc.)?",answer:"Sim, ele pode ser integrado; ele suporta a integração da API com as principais ferramentas de negócios, como Salesforce, HubSpot, Slack, Microsoft Teams e LINE WORKS. Você pode incorporar um assistente de IA sem alterar seu fluxo de trabalho existente."},{question:"Qual é sua estrutura de taxas?",answer:"Isso depende do tamanho e dos requisitos do projeto. Embora as taxas de construção inicial e de operação mensal sejam geralmente combinadas, também aceitamos solicitações de desenvolvimento pontual ou apenas de serviços de consultoria. Informe-nos primeiro sobre seus requisitos. Nós lhe forneceremos uma estimativa aproximada."}]},oP={title:"Planos de tarifas",subtitle:"Planos de preços",description:"Planos flexíveis para se adequar ao tamanho de sua empresa",plans:[{name:"inicial",price:"29.800 ienes",period:"Mensal/usuário",description:"O melhor plano para equipes pequenas. Funções básicas de IA a um preço acessível.",features:["Até 100 chamadas por mês","Transcrição básica","Detecção de palavras-chave","Suporte por e-mail","Retenção de dados por 7 dias"],cta:"Comece a usar gratuitamente",popular:!1},{name:"profissional",price:"¥79,800",period:"Mensal/usuário",description:"Ideal para equipes em crescimento. Recursos analíticos avançados.",features:["Até 500 chamadas por mês","Transcrição de IA de alta precisão","Análise emocional","Vinculação de CRM","Suporte prioritário","Retenção de dados por 30 dias","Relatórios personalizados"],cta:"Teste gratuito de 14 dias",popular:!0},{name:"empresa",price:"Entre em contato conosco",period:"personalizado",description:"Planos com recursos completos para grandes organizações",features:["Chamadas ilimitadas","Modelo de IA dedicado","Suporte no local","Suporte dedicado","Colaboração personalizada","Garantia de SLA","Auditorias de segurança","Retenção ilimitada de dados"],cta:"Consultar a equipe de vendas",popular:!1}],faq:{title:"Perguntas frequentes",items:[{question:"Existe um teste gratuito?",answer:"Sim, uma avaliação gratuita de 14 dias está disponível para os planos Professional. Não é necessário cartão de crédito para começar."},{question:"Posso mudar meu plano a qualquer momento?",answer:"Sim, você pode fazer upgrade ou downgrade do seu plano a qualquer momento. As alterações serão aplicadas a partir do próximo ciclo de faturamento."},{question:"Existe um termo de contrato vinculado?",answer:"Os planos mensais não têm restrições de prazo de contrato. Se você optar por um contrato anual, receberá um desconto de 20%."}]}},lP={hero:{badge:"Tecnologia com tecnologia de IA",title:"Transcrição de IA",description:"Modelos avançados de aprendizado de máquina proporcionam 99,5% de precisão. A IA treinada em milhões de dados de interação com o cliente converte voz em texto.",features:["99,5% de precisão","processamento em tempo real","Suporte para mais de 40 idiomas"]},features:{title:"Funções principais",subtitle:"Mecanismo de transcrição avançado que vai além do simples reconhecimento de fala",items:[{title:"transcrição em tempo real",description:"Obtenha transcrições em tempo real durante as conversas. Obtenha insights instantâneos e nunca perca uma oportunidade de treinamento em tempo real."},{title:"Suporte multilíngue",description:"Suporta mais de 40 idiomas e abrange equipes globais com detecção automática de idiomas."},{title:"compreensão contextual",description:"Os modelos de IA treinados em terminologia específica do setor reconhecem com precisão termos técnicos e comerciais."},{title:"separação de alto-falantes",description:"Identifica e separa automaticamente diferentes locutores em uma conversa e atribui atributos claros ao locutor."},{title:"léxico personalizado",description:"Aprender os nomes exclusivos de produtos, nomes de serviços e termos técnicos de sua empresa proporciona uma precisão ainda maior."},{title:"Garantia de qualidade",description:"Verificações automáticas de qualidade e pontuação de confiabilidade garantem a transcrição da mais alta qualidade."}]},useCases:{title:"Cenas de uso",subtitle:"Setores e equipes que exigem transcrição precisa de chamadas",items:[{title:"Equipe de vendas",description:"Refletir sobre as chamadas de vendas e identificar padrões de sucesso. Melhorar a eficácia das conversas e treinar os membros da equipe com exemplos específicos."}

303,{title:"Suporte ao cliente",description:"Registrar com precisão os problemas dos clientes. Manter um histórico de suporte abrangente para garantir a qualidade consistente do serviço."},{title:"controle de qualidade",description:"Monitorar a conformidade e garantir o cumprimento do script. Manter registros detalhados para auditorias e treinamentos."}]},stats:{accuracy:{value:"99.5%",label:"precisão da transcrição"},languages:{value:"40+",label:"Idiomas suportados"},latency:{value:"<2 segundos.",label:"atraso no processamento"},availability:{value:"24/7",label:"processamento contínuo"}}},dP={hero:{badge:"Detecção inteligente",title:"Rastreamento de palavras-chave",description:"Monitore automaticamente as menções, as refutações e os tópicos importantes dos concorrentes. Nunca perca sinais importantes das conversas com os clientes.",badges:["Rastreamento da concorrência","Detecção de resposta","Análise de tópicos","palavra-chave personalizada"]},features:{title:"Rastrear informações críticas",subtitle:"Detecção e análise automáticas de palavras-chave de todas as conversas",items:[{title:"Monitoramento da concorrência",description:"Acompanhe as menções dos concorrentes para entender como os clientes em potencial comparam sua solução com as alternativas do mercado."},{title:"Detecção de resposta",description:"Identifique automaticamente as objeções e preocupações comuns e desenvolva respostas e treinamentos eficazes."},{title:"palavra-chave personalizada",description:"Defina e monitore palavras-chave e frases personalizadas específicas para sua empresa, produto ou setor."},{title:"Análise de tendências",description:"Descubra as tendências emergentes das conversas com os clientes e adapte sua estratég

303ia com base nos sinais reais do mercado."},{title:"extração de contexto",description:"Obtenha o contexto antes e depois da palavra-chave, não apenas a menção dela. Entenda não apenas o que foi dito, mas por que e como foi dito."},{title:"Pesquisa inteligente",description:"Pesquise instantaneamente todas as conversas para descobrir exatamente onde e quando um determinado tópico foi discutido."}]},applications:{title:"Cenas de uso",items:{competitive:{title:"Inteligência competitiva",description:"Conhecimento profundo do cenário competitivo:",points:["Acompanhe seus concorrentes mencionados com mais frequência","Entenda como os clientes em potencial comparam recursos e preços","Identificar suas vantagens e fraquezas competitivas","Criação de materiais anticompetitivos eficazes"]},objections:{title:"resposta a uma refutação",description:"Transforme uma refutação em uma oportunidade:",points:["Identificar as preocupações mais comuns dos clientes","Analisar as respostas de refutação dos melhores desempenhos","Manual com resposta comprovada.","Treine sua equipe com uma recuperação eficaz"]},product:{title:"Insights sobre o produto",description:"Promover o desenvolvimento de produtos por meio do feedback do cliente:",points:["Identificar os recursos mais solicitados","Compreender os desafios da funcionalidade atual","Descubra casos de uso inesperados","Priorizar o roteiro com base nas necessidades do cliente"]},compliance:{title:"Conformidade e qualidade",description:"Garantir a conformidade e a qualidade regulatórias:",points:["Monitorar as divulgações exigidas e a linguagem jurídica","Sinalizar conversas que possam violar a política","Acompanhe a conformidade do script e as práticas recomendadas","Gerar relatórios de conformidade para auditoria"]}}},advancedFeatures:{title:"Recursos avançados",items:[{title:"recuperação ambígua",description:"Detecta automaticamente variações de palavras-chave, sinônimos e termos relacionados sem configuração manual."},{title:"análise histórica",description:"Analise as tendências de palavras-chave ao longo do tempo para identificar padrões sazonais e tópicos emergentes."},{title:"Alertas em tempo real",description:"Receba notificações imediatas quando palavras-chave importantes forem mencionadas em conversas ao vivo."}]}},uP={hero:{badges:["Empresa","LLM local","Operação interna do servidor"],title:"Construção de LLM no local",description:"Opere o LLM em seu próprio ambiente, mantendo os dados fora da empresa"},mission:{title:"Visão geral do serviço de construção de LLM no local",paragraphs:["Para empresas que não podem liberar dados confidenciais fora da empresa, criamos um ambiente LLM que pode ser concluído em seus próprios servidores ou em uma nuvem privada. Essa solução é ideal para organizações que exigem gerenciamento rigoroso de informações, como instituições financeiras, instituições médicas, agências governamentais e institutos de pesquisa, porque não usa nenhuma API pública e todo o processamento é concluído na infraestrutura gerenciada pelo cliente.","Fornecemos suporte abrangente desde a seleção do modelo, construção do servidor, otimização do mecanismo de inferência e monitoramento da operação, usando modelos de peso aberto disponíveis comercialmente, como Llama 3.3, Qwen 2.5, Mistral e DeepSeek, com mecanismos de inferência de alto desempenho, como vLLM, Ollama e SGLang. Esses modelos são executados em mecanismos de inferência de alto desempenho, como vLLM, Ollama e SGLang, para fornecer um ambiente de inferência de baixa latência e alta produtividade.","Oferecemos suporte a uma ampla variedade de casos de uso, como integração com sistemas existentes, chatbots, RAGs internos, resumo de documentos e geração de código. Podemos projetar para atender aos requisitos da empresa, incluindo especialização de negócios por meio de ajuste fino do modelo, otimização de GPU NVIDIA/Apple Silicon, design multilocatário, construção de gateway de API e muito mais."]},summary:{title:"Tendências do LLM no local",paragraphs:["Em 2025-2026, a utilidade dos LLMs no local está melhorando rapidamente. O desempenho dos modelos ponderados abertos superou o dos modelos fechados, e o número de modelos oferecidos com licenças disponíveis comercialmente reduziu significativamente o obstáculo para as operações internas.","Os modelos com parâmetros de 8B a 70B atingiram um nível prático para muitas tarefas comerciais;

303 a quantização GGUF permite a inferência em alta velocidade até mesmo em GPUs de consumo.","Para os mecanismos de inferência, o Continuous Batching e o PagedAttention do vLLM aumentam drasticamente o rendimento, e o RadixAttention do SGLang acelera interações complexas de várias voltas. O Ollama se tornou o padrão de fato para ambientes de desenvolvimento local, e o llama.cpp é otimizado pela Apple Silicon para inferência rápida em Macs da série M.","Na adoção corporativa, além da soberania e da conformidade dos dados, a otimização de custos a longo prazo (afastando-se do faturamento de API pago conforme o uso) está se tornando cada vez mais uma motivação para a adoção. As configurações híbridas (local para processamento sensível e API em nuvem para processamento de uso geral) tornaram-se uma opção comum."],icons:[{label:`Privado
304Nuvem`},{label:`Dados
305Soberania`},{label:`Custo
306Otimização`},{label:`Empresa
307Segurança`}]},features:{title:"Recursos de design e desenvolvimento",subtitle:"Design e desenvolvimento",items:[{title:"Projeto de infraestrutura e otimização de GPU",description:"Projete a configuração de hardware ideal para sua carga de trabalho e seu orçamento, incluindo NVIDIA A100/H100, RTX 4090/5090 e Apple Silicon (M2 Ultra/M3 Max/M4 Max), e crie um ambiente de inferência que aproveite ao máximo o desempenho da GPU por meio da otimização CUDA, MPS e Metal. Otimização de CUDA, MPS e Metal para maximizar o desempenho da GPU."},{title:"Seleção e otimização de modelos",description:"Selecione o modelo ideal entre Llama, Qwen, Mistral, DeepSeek, etc., considerando as características da tarefa, os requisitos de linguagem e as restrições de recursos. A quantização (GGUF/AWQ/GPTQ), a otimização do cache KV e o ajuste do processamento em lote são usados para obter precisão e taxa de transferência."},{title:"Construção do mecanismo de inferência",description:"Selecione e crie mecanismos de inferência, como vLLM, SGLang, Ollama, TGI, etc., de acordo com seu aplicativo, e aplique as tecnologias mais recentes, como Continuous Batching, Speculative Decoding e Prefix Caching, para fornecer APIs de inferência de baixa latência e alta produtividade. Fornecemos APIs de inferência de baixa latência e alta taxa de transferência."},{title:"Operação, monitoramento e segurança",description:"Implemente o monitoramento de métricas inferenciais com o Prometheus/Grafana, controle de acesso com gateway de API, logs de auditoria, dimensionamento automático e recuperação de falhas. Crie um ambiente de operação de produção que atenda aos requisitos de segurança da empresa."}]},techStack:{title:"Pilha de tecnologia: LLM no local",subtitle:"Plataforma e tecnologia",categories:{openWeightModels:{title:"Modelos de peso aberto",items:["Llama 3.3 70B / 8B","Qwen 2.5 72B / 32B / 7B","Mistral Large / Nemo","DeepSeek V3 / R1","Gemma 2 27B / 9B","Phi-4","Comando R (Cohere)"]},japaneseModels:{title:"Modelo específico do Japão",items:["ELYZA-japonês-Llama-2","Andorinha 70B / 13B","Japonês-Llama-3","Qwen-2.5-Japanese","Karakuri-LM"]},inferenceEngine:{title:"Mecanismo de inferência",items:["vLLM (Loteamento Contínuo)","SGLang (RadixAttention)","Ollama (desenvolvimento local)","llama.cpp (Apple Silicon)","TGI (Hugging Face)","TensorRT-LLM (NVIDIA)","ExLlamaV2 (especialização em quantização)"]},quantization:{title:"Quantização",items:["GGUF (padrão llama.cpp)","AWQ (4 bits de alta precisão)","GPTQ (otimização de GPU)","bits e bytes (8 bits/4 bits)","EETQ (Quantização de Eficiência Eficiente)"]},hardware:{title:"Hardware",items:["NVIDIA H100 / A100","NVIDIA L40S / A10G","RTX 4090 / 5090","Apple Silicon M2 Ultra","Apple Silicon M3/M4 Max","AMD MI300X"]}

307,infrastructure:{title:"Infraestrutura",items:["Docker / Kubernetes","Servidor NVIDIA Triton","Gateway de API (Kong / Envoy)","Prometheus / Grafana","Nuvem privada (VMware / OpenStack)","Suporte ambiental com isolamento acústico"]}},updateNote:{title:"Principais pontos de atualização para 2026",description:"Nos modelos de peso aberto, o Llama 3.3 atinge o desempenho de uso geral da classe GPT-4 e melhora muito o desempenho do idioma japonês; a série Qwen 2.5 expande a escolha com uma variedade de tamanhos e desempenho bem equilibrado; o DeepSeek V3/R1 atrai a atenção por seu desempenho de custo de inferência; e os mecanismos de inferência vLLM 2.0 Chunked Prefill e SGLang's RadixAttention aceleram o diálogo de várias voltas. No mecanismo de inferência, o Chunked Prefill do vLLM 2.0 e o RadixAttention da SGLang aceleram as interações de várias voltas; a otimização do llama.cpp da Apple Silicon torna prática a inferência do modelo 70B no M4 Max; e a tecnologia Quantization oferece precisão de 4 bits e alto desempenho. As técnicas de quantização alcançaram precisão de 4 bits e velocidade de inferência, e o GGUF tornou-se o formato padrão de fato."}},cta:{title:"Entre em contato conosco",description:`Podemos consultá-lo desde a definição dos requisitos.
308As configurações de PoC e LLM local também podem ser consideradas.`,primaryButton:"Entre em contato conosco",secondaryButton:"Consulta técnica"}},cP={hero:{badges:["Empresa","RAG","geração de extensões de pesquisa"],title:"Desenvolvimento Vector / RAG",description:"Criar um sistema RAG para vincular o conhecimento interno ao LLM"},mission:{title:"Visão geral dos serviços de desenvolvimento da Vector / RAG",paragraphs:["Criaremos um sistema RAG (geração de extensão de pesquisa) que permite que os funcionários pesquisem e façam perguntas em linguagem natural, vinculando os ativos de conhecimento acumulados na empresa com o LLM. Integramos várias fontes de dados, como manuais, atas, documentos técnicos, perguntas frequentes, histórico do Slack, Confluence, SharePoint etc. para criar um ambiente em que o conhecimento organizacional possa ser utilizado imediatamente.","Maximizamos a precisão da pesquisa e a qualidade da resposta aplicando as tecnologias mais recentes, como a pesquisa híbrida (pesquisa vetorial + pesquisa de palavras-chave), reanálise, chunking hierárquico, filtragem de metadados etc., além da pesquisa simples de documentos. Indicamos claramente a fonte da resposta para suprimir a halcinação e garantir a confiabilidade.","Oferecemos suporte a vários ambientes, incluindo AWS, GCP, Azure e no local, e nos integramos à infraestrutura de autenticação e ao gerenciamento de autoridade existentes. Fornecemos suporte consistente para manter a atualização dos dados (atualizações diferenciais e reindexação automática), analisando os registros de uso e melhorando continuamente a precisão."]},summary:{title:"Tendências tecnológicas do RAG",paragraphs:['Entre 2025 e 2026, a tecnologia RAG está entrando na fase de "implementação básica para qualidade de produção". Além da pesquisa vetorial simples, a Pesquisa Híbrida, que combina vários métodos de pesquisa, o Reranking, que reordena os resultados da pesquisa, e a Decomposição de Consultas, que decompõe e processa perguntas complexas, tornaram-se configurações padrão.',"As estratégias de fragmentação também evoluíram, com a fragmentação hierárquica que leva em conta a estrutura do documento, a sobreposição de blocos e a fragmentação multimodal que processa tabelas e figuras separadamente, tudo isso contribuindo para melhorar a precisão. O Contextual Retrieval (adição de informações contextuais aos blocos) também foi amplamente reconhecido como eficaz.","Para a seleção do banco de dados vetorial, além dos serviços gerenciados, como Pinecone, Weaviate, Qdrant e Milvus, o PostgreSQL pgvector e o FAISS local oferecem uma ampla gama de opções para atender às suas necessidades. Os modelos de incorporação também estão disponíveis, incluindo text-Embedding-3-large, Cohere Embed v3 e modelos multilíngues.",'Em implementações empresariais, a vinculação de gerenciamento de autoridade (ACL), o mascaramento de PII, os registros de auditoria e o design multilocatário são requisitos essenciais, e é necessário passar de um "PoC funcional" para um "sistema de produção pronto para produção".'],icons:[{label:`Híbrido
309Pesquisa`},{label:`Multimodal
310RAG`},{label:`Agêntico
311RAG`},{label:`Empresa
312Segurança`}]},features:{title:"Recursos de design e desenvolvimento",subtitle:"Design e desenvolvimento",items:[{title:"Pipeline de coleta e pré-processamento de dados",description:"Extração e limpeza de dados de PDF, Word, Excel, HTML, Markdown, Slack, Confluence, SharePoint, etc. Suporte multimodal, incluindo OCR, análise de estrutura de tabela e geração de legenda de imagem. Mantenha o frescor com atualizações diferenciais e execução programada."},{title:"Projeto de sistema de busca de alta precisão",description:"Construa uma pesquisa de alta precisão combinando Vector Search + BM25 Hybrid Search, Reranking by Cohere Rerank / Cross-Encoder, e Query Expansion and Decomposition. A filtragem de metadados e o controle de acesso baseado em permissão também são implementados."},{title:"Geração de respostas e controle de qualidade",description:"Qualidade de resposta aprimorada com otimização de contexto, prompts de poucos disparos e inferência de cadeia de pensamento. O esclarecimento da fonte, a exibição da pontuação de confiança e a detecção de halcinação são implementados para garantir a confiabilidade do uso comercial."},{title:"Ciclo de operação e aprimoramento",description:"Fornecemos suporte operacional, incluindo análise de registros de uso, monitoramento da taxa de acertos de pesquisa e da satisfação com as respostas, melhoria contínua por meio de ciclos de feedback, infraestrutura de testes A/B, atualizações de modelos de incorporação e revisões de estratég

312ias de fragmentação."}]},techStack:{title:"Pilha de tecnologia: Vector / RAG",subtitle:"Plataforma e tecnologia",categories:{embeddingModels:{title:"Modelos de incorporação",items:["OpenAI text-embedding-3-large","Cohere Embed v3","IA do Voyage","Jina Embeddings v3","BGE-M3 (multilíngue)","multilingual-e5-large","Modelos específicos do Japão"]},vectorDatabases:{title:"Bancos de dados vetoriais",items:["Pinecone (gerenciado)","Weaviate (Pesquisa híbrida)","Qdrant (alto desempenho)","Milvus (grande escala)","Chroma (leve)","PostgreSQL pgvector","FAISS (local)"]},chunking:{title:"Chunking e processamento",items:["fragmentação hierárquica","Agrupamento semântico","Sobreposição de blocos","Janela de frases","Recuperação contextual","Processamento de tabelas e gráficos","Suporte multimodal"]},search:{title:"Busca e recuperação",items:["Pesquisa híbrida (Vector + BM25)","Cohere Rerank","Reranking entre codificadores","Expansão e decomposição de consultas","HyDE (Hypothetical Document Generation)","Recuperação de consulta automática","Recuperação de várias consultas"]}},updateNote:{title:"Principais pontos de atualização para 2026",description:"No modelo Embedding, o text-Embedding-3-large e o Cohere Embed v3 tornaram-se o padrão da empresa, e o desempenho do idioma japonês melhorou significativamente; o Contextual Retrieval provou ser eficaz, e a adição de contexto aos blocos tornou-se o método padrão. O Hybrid Search + Reranking tornou-se uma configuração obrigatória para a pesquisa, reduzindo o número de configurações simples de pesquisa vetorial, e o Agentic RAG (agentes selecionam dinamicamente as estratég

312ias de pesquisa) está sendo usado na prática. No aspecto operacional, a ênfase está sendo colocada no estabelecimento de ciclos de medição e aprimoramento da qualidade usando estruturas de avaliação RAG (RAGAS, LangSmith)."}},cta:{title:"Entre em contato conosco",description:`Podemos consultá-lo desde a definição dos requisitos.
313As configurações de PoC e LLM local também podem ser consideradas.`,primaryButton:"Entre em contato conosco",secondaryButton:"Consulta técnica"}},mP={hero:{badges:["Pilha completa","Integração do LLM","Projeto da API"],title:"Desenvolvimento de aplicativos de IA",description:"Projetar e criar aplicativos práticos que aproveitem o LLM"},mission:{title:"Visão geral do serviço de desenvolvimento de aplicativos de IA",paragraphs:["Fornecemos projeto e desenvolvimento de aplicativos de IA que criam valor comercial analisando dados adquiridos de APIs externas com o LLM, vinculando APIs a diversas fontes de dados, como CRM, ERP, SaaS, sensores de IoT e mídias sociais, e alimentando o LLM com os dados adquiridos em tempo real. Possibilitamos a análise avançada de dados que não pode ser obtida com ferramentas de BI convencionais, como a geração de relatórios analíticos usando linguagem natural, detecção de anomalias, previsão de tendências e classificação automática.","Não apenas a simples integração de API, mas também a integração de várias fontes de dados, a limpeza de dados e a conversão para um formato que seja fácil de ser interpretado pelo LLM por meio de processamento estruturado. A engenharia imediata maximiza a precisão da análise e gera resultados especializados para os requisitos de negócios. Criamos aplicativos práticos que combinam dados e IA, como dados de vendas para prever negociações comerciais, registros de suporte ao cliente para realizar análises de VoC e dados de sensores para fornecer recomendações de manutenção de equipamentos.","Oferecemos suporte a uma variedade de interfaces, como aplicativos da Web, aplicativos móveis, integração Slack/Teams e processamento periódico em lote. Fornecemos suporte consistente desde a definição de requisitos até o design da API, a implementação da integração do LLM e a operação de produção."]},summary:{title:"Tendências no desenvolvimento de aplicativos de IA",paragraphs:['De 2025 a 202

3136, o desenvolvimento de aplicativos de IA está entrando em uma fase prática baseada em "integração de API x análise de LLM". A análise de dados não estruturados e a geração de insights em linguagem natural, que não podiam ser tratados por ferramentas e painéis de BI tradicionais, estão começando a ser incorporados aos sistemas corporativos.',"Destaca-se o amadurecimento da funcionalidade de saída estruturada do LLM, que permite o controle rigoroso da saída em conformidade com o esquema JSON, tornando realidade um pipeline automatizado de ponta a ponta, em que os dados adquiridos por meio de APIs são analisados pelo LLM e os resultados são alimentados diretamente nos sistemas subsequentes. A execução em vários estágios da Chamada de Função permite que a coleta, a integração e a análise de dados em várias APIs sejam tratadas em um único fluxo de trabalho.","Na infraestrutura de processamento de dados, o Polars surgiu como um padrão para processamento de alta velocidade e, em combinação com o processamento de streaming, facilita a análise em tempo real.","Nas implementações corporativas, as necessidades mudaram do desenvolvimento pontual de PoC para um projeto dimensionável para a operação de produção, exigindo um projeto de arquitetura abrangente que inclua otimização de custos, tratamento de erros e suporte a registros de auditoria."],icons:[{label:`Agêntico
314IA`},{label:`MCP
315Protocolo`},{label:`Múltiplos
316Agente`},{label:`Humano em
317-no circuito`}]},features:{title:"Recursos de design e desenvolvimento",subtitle:"Design e desenvolvimento",items:[{title:"Integração de API e projeto de coleta de dados",description:"Criamos uma infraestrutura de coleta de dados compatível com vários protocolos, como API REST, GraphQL, WebSocket e Webhook. Projetamos um pipeline de aquisição de dados estável implementando processos de autenticação (OAuth, chave de API, JWT), limitação de taxa e mecanismos de repetição. Também oferecemos suporte à integração de dados, agendamento e aquisição diferencial de várias APIs."},{title:"Pré-processamento e estruturação de dados",description:"Converte dados brutos em um formato que pode ser analisado com eficiência pelo LLM, incluindo análise de JSON, limpeza de dados, normalização e processamento de valores ausentes, e realiza a segmentação e o resumo ideais dos dados, considerando as restrições da janela de contexto. A atribuição de metadados garante a rastreabilidade dos resultados da análise."},{title:"Análise do LLM e projeto imediato",description:"Maximize a precisão da análise de dados projetando modelos de prompt específicos para suas necessidades comerciais. Criamos pipelines de análise para classificação, sumarização, análise de sentimentos, detecção de anomalias, geração de previsões, etc. Utilizamos Function Calling e Structured Output para gerar resultados em um formato que pode ser facilmente integrado a sistemas subsequentes."},{title:"Implementação e operação de aplicativos",description:"Fornecemos resultados de análise por meio de várias interfaces, incluindo FastAPI, aplicativos da Web usando Next.js, etc., Slack/Teams Bot e execução em lote programada. Painéis, alertas de notificação e geração automática de relatórios são implementados e entregues em um formato que os usuários corporativos podem utilizar imediatamente. Também fornecemos monitoramento operacional, otimização de custos e melhoria contínua."}]},techStack:{title:"Pilha de tecnologia: Desenvolvimento de aplicativos de IA",subtitle:"Plataforma e tecnologia",categories:{apiIntegration:{title:"Integração de API",items:["REST / GraphQL / WebSocket","OAuth 2.1 / API key / JWT","Webhook / Sondagem","Fluxo de ar Apache / Prefect","Aipo / Temporal","solicitações / httpx / aiohttp","Limite de taxa / Repetição","Validação de dados (Pydantic)"]},dataProcessing:{title:"Processamento de dados",items:["Pandas / Polares","Analisador JSON / JSONL","Pipeline de limpeza de dados","Apache Spark (processamento em grande escala)","dbt (conversão de dados)","Grandes expectativas","Lago Delta / Iceberg","Streaming (Kafka/Flink)"]}

317,llmCloud:{title:"LLM em nuvem",items:["OpenAI GPT-4o / GPT-4.1","Claude 3.5 / Claude 4","Gemini 2.0 / Gemini 2.5","Comando R+ (Cohere)","Mistral Large"]},llmLocal:{title:"LLM local",items:["Llama 3.3 / Llama 4","Mistral / Mixtral","Qwen 2.5 / Qwen 3","DeepSeek V3 / R1","Gemma 2 / Gemma 3","Phi-4","ELYZA / Swallow (especializada em japonês)","Comando R (Cohere)"]},inference:{title:"Inferência / Estrutura",items:["vLLM / SGLang","Ollama / llama.cpp","Proxy LiteLLM","Chamada de função","Saída estruturada","Mecanismo de modelo de prompt","LangChain / LlamaIndex","Pipeline de análise proprietária"]},application:{title:"Camada de aplicativos",items:["FastAPI / Litestar","Next.js / React","Slack SDK / Teams SDK","Streamlit / Gradio","Trabalhos agendados (Cron / Cloud Scheduler)","PostgreSQL / MongoDB","Redis (Cache / Fila)","Docker / Kubernetes"]}},updateNote:{title:"Principais pontos de atualização para 2026",description:"Na área de análise do LLM, a funcionalidade de Saída Estruturada do GPT-4.1 e do Claude 4 amadureceu para padronizar o controle de saída estrito em conformidade com o esquema JSON, e a execução em vários estágios da Chamada de Função permite a coleta e a análise de dados complexos em várias APIs em um único pipeline. A execução em vários estágios da Chamada de Função permite a coleta e a análise de dados complexos em várias APIs em um único pipeline. No processamento de dados, o Polars tornou-se uma alternativa estabelecida ao Pandas para o processamento em alta velocidade e, em combinação com o processamento de streaming, facilita a construção de uma infraestrutura de análise em tempo real. Na camada de aplicativos, a integração de ferramentas externas usando o MCP tornou-se comum, acelerando o desenvolvimento de aplicativos agênticos nos quais os LLMs chamam APIs de forma autônoma para adquirir e analisar dados."}},cta:{title:"Entre em contato conosco",description:`Podemos consultá-lo desde a definição dos requisitos.
318As configurações de PoC e LLM local também podem ser consideradas.`,primaryButton:"Entre em contato conosco",secondaryButton:"Consulta técnica"}},pP={seo:FL,header:WL,hero:qL,mission:UL,businessServices:_L,features:HL,businessProcess:$L,story:QL,contact:JL,footer:ZL,cookiePolicy:XL,about:YL,portfolio:eP,common:aP,privacyPolicy:nP,termsOfService:tP,weatherMap:iP,agentDevelopment:sP,faq:rP,pricing:oP,aiTranscription:lP,keywordTracking:dP,onPremiseLLM:uP,vectorRAG:cP,aiAppDevelopment:mP},gP={home:{title:"KI-gestützte Lösungsplattform - telet.ai",description:"Verwandeln Sie Ihre Kundengespräche mit KI-gestützter Anrufanalyse. Erhalten Sie Echtzeit-Einblicke, Stimmungsanalysen und Leistungsüberwachung, um die Ergebnisse Ihres Teams zu verbessern."},about:{title:"Organisationsinformationen - telet.ai",description:"Learn about telet.ai's mission to transform customer conversations with AI. Meet our team and explore our journey."},privacy:{title:"Datenschutzerklärung - telet.ai",description:"Review telet.ai's privacy policy to understand how we handle your data and protect your privacy."},terms:{title:"Nutzungsbedingungen - telet.ai",description:"Read telet.ai's terms of service for using this website and contacting us for consultations."},cookies:{title:"Cookie-Richtlinie - telet.ai",description:"Learn how telet.ai uses cookies and similar technologies (Cloudflare and Google Analytics)."},portfolio:{title:"Portfolio - telet.ai",description:"Entdecken Sie unser Portfolio an KI-Lösungen, die darauf ausgelegt sind, Ihre Geschäftsabläufe und Kundenerfahrungen zu verbessern."},agentDevelopment:{title:"Entwurf und Entwicklung von KI-Agenten für Unternehmen - telet.ai",description:"Entwerfen und erstellen Sie produktionsreife KI-Agenten, die auf Ihre Arbeitsabläufe, Tools und Compliance-Anforderungen zugeschnitten sind."},onPremiseLLM:{title:"Lokale LLM-Operationen - telet.ai",description:"Nutzen Sie moderne LLMs in Ihrer Umgebung für eine starke Datenkontrolle, vorhersehbare Kosten und Inferenz mit geringer Latenz."},vectorRAG:{title:"Vektor-RAG & Index-Design - telet.ai",description:"Entwickeln Sie auf jeden Arbeitsablauf zugeschnittene Pipelines und Indizes für die Designabfrage, um fundierte Antworten zu verbessern."},aiAppDevelopment:{title:"Entwicklung von KI-Anwendungen - telet.ai",description:"Entwickeln Sie praktische Anwendungen auf Basis von LLMs – integriert in Ihre APIs, Daten und Geschäftsabläufe."}},hP={brand:"telet.ai",nav:{services:"Dienstleistungen",portfolio:"Portfolio",about:"Organisationsinformationen",contact:"Kontakt"}},fP={brandName:"telet.ai",tagline:"Multi AI-Kommunikationen",badge:"KI-gestützte Anrufintelligenz",title:{line1:"Lokal und defensiv,",line2:"Expandieren Sie in der Cloud,",line3:"Automatisiert durch Agent."},description:"Die Edge-Inferenz in SLM gewährleistet die Datenhoheit, und die Cloud-Integration in LLM skaliert die Verarbeitungsleistung ins Unendliche; die Einführung der Agentic AI-Architektur zeigt erste Ergebnisse bei führenden Unternehmen."},kP={badge:"Unser Auftrag",title:"LLM besser zugänglich machen",paragraphs:['Können Sie wirklich so weit gehen?" --Diese Überraschung ist unser Ziel.','Das Potenzial der groß angelegten Sprachmodellierung (LLM) wird täglich größer. Wir wollen jedoch Barrieren wie "Ich habe Angst, vertrauliche Daten an externe APIs zu senden", "Fortgeschrittene KI-Anwendungen sind nur für große Unternehmen" und "Die Kosten für die Implementierung lohnen sich nicht" abbauen.',"Wir sind darauf spezialisiert, lokale LLMs zu erstellen, die innerhalb Ihrer Umgebung abgeschlossen werden. Hochsensible Kundendaten können sicher genutzt werden, ohne der Außenwelt ausgesetzt zu sein. Wir können die Vorteile der K

318I maximieren und gleichzeitig die Datensicherheit gewährleisten.","Sie reicht von der routinemäßigen Dokumentenerstellung und Datenorganisation bis hin zu speziellen Analysen und Entscheidungshilfen. Unsere firmeneigene Agententechnologie bietet eine Genauigkeit, die mit API-Modellen vergleichbar ist.",'Das LLM ersetzt den Menschen nicht. Es ist ein leistungsfähiges Instrument, das es den Menschen ermöglicht, mehr Kreativität, Urteilsvermögen und Fachwissen einzusetzen. Wir machen die Technologie vom Besonderen zum Alltäglichen - eine echte "Verbraucherisierung" der Technologie.',"Sie können sich gerne mit uns beraten. Wir werden gemeinsam mit Ihnen herausfinden, was wir tun können."],animation:{label:"LLM",sublabel:"Technologie"}},bP={title:"Dienstleistungen",subtitle:"Dienstleistungen für Unternehmen",services:[{title:"Design und Entwicklung von KI-Agenten für Unternehmen",titleEn:"KI-Agent für Unternehmen",description:"Entwurf und Entwicklung von KI-Agenten, die für Geschäftsanforderungen, interne Regeln und Datenumfang optimiert sind. Unterstützung vom externen API-Typ LLM bis zum lokalen LLM.",badges:["Individuelles Design","LLM-kompatibel","wirtschaftliche Spezialisierung"],link:"Einzelheiten."},{title:"LLM-Server auf Abruf",titleEn:"LLM-Server vor Ort",description:"Führt LLM im Umfang von 7B bis 120B innerhalb des dedizierten Servers des Unternehmens aus. Bietet eine sichere und flexible LLM-Nutzungsumgebung und vermeidet das Risiko einer externen Datenübertragung.",badges:["7B-120B unterstützt","API KEY ausgestellt","Sicherheit Design"],link:"Einzelheiten."},{title:"Vektor-RAG und Indexgestaltung",titleEn:"Vector RAG Architektur",description:"Trennen und optimieren Sie RAG-Daten nach Geschäftsinhalten und Analyselogik. Indexdesign für eine schnelle und genaue Suche.",badges:["Index-Trennung","Hochgeschwindigkeitssuche","mehrsprachige Unterstützung"],link:"Einzelheiten."},{title:"AI-Anwendungsentwicklung",titleEn:"AI-Anwendungsentwicklung",description:"Entwicklung praktischer KI-Anwendungen, bei denen die Analyse und Generierung durch LLM im Mittelpunkt steht, unter Nutzung vorhandener APIs und externer Daten.",badges:["API-Verknüpfung","LLM-Analyse","Integriertes Design"],link:"Einzelheiten."}]},yP={title:"Leistungsprojekte mit KI-Technologie",subtitle:"KI-Entwicklungserfahrung in einer Vielzahl von Bereichen, darunter mehrsprachige Assistenten, Wettervorhersage, lokale LLM und Sicherheitsüberwachung.",viewDetails:"Details ansehen →.",items:[{title:"AI Mehrsprachiger Assistent",description:"Mehrsprachiger Chat-Assistent von RAG + LLM. Hochpräzise Antworten auf Anfragen in mehreren Sprachen, darunter Japanisch, Englisch und Chinesisch."},{title:"Wettervorhersagesystem",description:"Lernt Wetterdaten und erstellt Echtzeitvorhersagen. Hochpräzise Vorhersagen werden durch den Einsatz von Zeitreihen-Mustererkennung und RAG erzeugt, um nach ähnlichen Mustern in der Vergangenheit zu suchen."},{title:"Interner Server / On-Prem Local LLM",description:"Wir liefern auf Ihre Geschäftsabläufe zugeschnittene KI, die in Ihrem Unternehmen sofort einsatzbereit ist, mit Open WebUI als gemeinsamen Einstiegspunkt, schnellen Inferenzspuren mit Ollama/MLX und vollständig getrennten Datendomänen für jede UID. Interne Dokumente werden in einem RAG mit englischer Einbettung und automatischer japanischer Abfrageübersetzung erfasst, und Business Skills wie Nachrichtenzusammenfassung, Dokumentenorganisation und Routineaufgaben werden sicher nebeneinander ausgeführt. Das Skill-Ausführungssystem ist als OpenClaw-Container isoliert und durch drei Schichten von Cloudflare Edge und lokalem Nginx geschützt. Wir bieten umfassende Unterstützung bei der Modellauswahl, dem Skill-Design und den Betriebsrichtlinien und machen die dedizierte KI Ihres Unternehmens vom ersten Tag an einsatzfähig."},{title:"Interplanetare Orbital-Ankunftsvorhersage (AI)",description:"Beziehen Sie Planetendaten von der NASA-API und interpretieren Sie Bahnberechnungen im LLM. Erzeugt Berichte über Startfenster, benötigte Zeit und Treibstoffeffizienz."}

318,{title:"AI-Analyse von Immobiliendaten",description:"Sammeln Sie Immobilieninformationen durch Web Scraping und analysieren Sie diese mit LLM + Python. Automatische Generierung von Preistrends, Marktprognosen und Investitionsrenditen."},{title:"Wahrsagerei AI (Vier Säulen Wahrsagerei)",description:"Reproduzieren Sie die Logik der Vier Säulen des Schicksals in Python und interpretieren Sie sie mit LLM. Erfassen Sie Koordinaten und Zeit von der Astronomie-API für eine personalisierte Einschätzung des Glücks und der Kompatibilität."},{title:"Google Workspace AI integration",description:"LLM-Integration mit Gmail, Drive, Kalender, etc. KI hilft bei der E-Mail-Zusammenfassung, Dateisuche und Terminplanung."},{title:"World NEWS Mehrsprachigkeit",description:"Sammlung von Weltnachrichten in Echtzeit, übersetzt und zusammengefasst in mehreren Sprachen von LLM. Natürliche Übersetzung und Generierung von Zusammenfassungen auf der Grundlage eines kontextuellen Verständnisses."},{title:"Sicherheitsprotokollüberwachung AI Manager",description:"Echtzeitüberwachung von Sicherheitsprotokollen, Erkennung und Analyse von Bedrohungen durch LLM und sofortige Warnmeldungen durch Abgleich mit früheren Angriffsmustern durch RAG."}]},vP={title:"Wie man mit dem Projekt fortfährt",headline:"Alles, was Sie brauchen, ist eine Vision.",paragraphs:['Vorbei sind die Zeiten, in denen Sprachunterschiede ein Geschäftshindernis darstellten. Obwohl wir KI-Übersetzung verwenden, können wir Geschäftsverhandlungen und Entwicklungen in 14 Sprachen durch unseren einzigartigen Arbeitsablauf abwickeln, der die gemeinsame Nutzung von Bildern in vollem Umfang nutzt. Selbst komplexe Spezifikationen können anhand von Beispielbildern überprüft werden, um Missverständnisse zu vermeiden. Das Einzige, was zählt, ist das, was Sie schaffen wollen".',"Das Projekt beginnt mit einer ersten Anhörung in der Geschäftsanwendung. Nach einem allgemeinen Austausch über die Visionen des Kunden und sein Gespür für die Probleme wird ein NDA (Non-Disclosure Agreement) unterzeichnet. Nachdem wir eine Umgebung geschaffen haben, in der vertrauliche Informationen vertraulich besprochen werden können, führen wir ausführliche Interviews und bestätigen die Anforderungen. Auf der Grundlage des Inhalts erstellen wir einen Vorschlag und bestätigen die Richtung des Projekts anhand von Beispielbildern. Wenn wir uns einig sind, erstellen wir die Spezifikationen und beginnen mit der Entwicklung.","Dieser Ablauf ist nur eine allgemeine Einführung. Wir werden uns flexibel an Ihre Situation und Ihren Zeitplan anpassen. Wo auch immer Sie sich auf der Welt befinden, selbst wenn Sie nicht dieselbe Sprache sprechen, werden Bilder und KI Ihre Kommunikation unterstützen. Warum schließen Sie sich nicht uns an, um den Markt zu betreten, den noch niemand erschlossen hat? Lassen Sie telet.ai Ihr Partner sein, um Ihren Ideen Gestalt zu geben."],cards:[{title:"Anhörung / NDA",description:"Nach der ersten Anhörung per E-Mail wird eine Geheimhaltungsvereinbarung unterzeichnet. Sie können uns unbesorgt Einzelheiten mitteilen."},{title:"Planung & CA",description:"Es werden Vorschläge und Beispielbilder bereitgestellt. 14 Sprachen werden unterstützt, so dass Sie ohne Sprachbarrieren arbeiten können."},{title:"Probenahme & Start",description:"Wir werden ein Lastenheft erstellen und nach Vereinbarung die Arbeit aufnehmen."},{title:"Entwicklung, Prüfung und Betrieb",description:"Zügige Umsetzung durch agile Entwicklung. Wir bieten durchgängige Unterstützung vom Testen bis zur Betriebsunterstützung."}]},AP={badge:"Fallstudien",title:"AI-Einführungsgeschichte",items:[{title:"Lösungen für Unternehmenseffizienz und Kostenreduzierung",subtitle:"Reduzieren Sie die tägliche Arbeitsbelastung und schaffen Sie ein Umfeld, in dem Sie sich auf Ihr Kerngeschäft konzentrieren können.",description:'Viele kleine und mittlere Unternehmen (KMU) haben mit Arbeitskräftemangel und Einstellungsschwierigkeiten zu kämpfen. Selbst wenn neues Personal eingestellt wird, sind die Schulungskosten hoch und es gibt Probleme mit der Mitarbeiterbindung. Unsere KI-Lösung unterstützt alltägliche Aufgaben wie die Automatisierung der Budgetverwaltung, die Dateneingabe und -organisation, die interne Suche nac

318h Dokumenten und die automatische Erstellung von Berichten und Dokumenten. KI-Assistenten erledigen Routineaufgaben, die früher viel Zeit in Anspruch nahmen, 24 Stunden am Tag, 365 Tage im Jahr, ohne Fehler. Unternehmen, die KI eingeführt haben, konnten ihre Arbeitszeiten deutlich reduzieren. Von der "Aufstockung der Humanressourcen" bis zur "Zusammenarbeit mit KI". Wir werden unser Bestes tun, um die Reform des Arbeitsstils in Ihrem Unternehmen zu unterstützen.'},{title:"Automatisierung der Kundenbetreuung",subtitle:"Der 24-Stunden-Service verbessert die Kundenzufriedenheit und senkt die Arbeitskosten.",description:"KI-Chatbots bieten automatische Antworten auf häufig gestellte Fragen, primäre Beantwortung und Sortierung von Anfragen sowie Echtzeit-Antworten in mehreren Sprachen. Echtzeit-Antworten in mehreren Sprachen. Menschliche Mitarbeiter können sich auf komplexe Fälle konzentrieren und die Qualität des Kundenservices verbessern. Heben Sie Ihre Servicequalität mit KI-Kundensupport auf die nächste Stufe."},{title:"Wissen und Dokumentennutzung AI",subtitle:"Interne Wissensbestände in einer Form, die von jedem sofort genutzt werden kann",description:'Wo ist das Handbuch?" Es gibt Informationen, die nur der Vorgänger kennt" - unpersönliches Wissen hemmt das Wachstum eines Unternehmens. Unsere RAG-Technologie (Search Extension Generation) ermöglicht es Ihnen, notwendige Informationen aus internen Handbüchern, FAQs und früheren Dokumenten abzurufen, indem Sie einfach Fragen in natürlicher Sprache stellen. Sie rationalisiert die Schulung neuer Mitarbeiter, digitalisiert das implizite Wissen von Veteranen und ermöglicht den abteilungsübergreifenden Wissensaustausch. Vorhandene Dokumente können unverändert weiterverwendet werden, so dass keine neuen Dokumente erstellt werden müssen.'},{title:"Bildanalyse-KI für die medizinische und pflegerische Versorgung",subtitle:"Wachsame Augen, 24-Stunden-Unterstützung durch KI",description:"Im medizinischen und pflegerischen Bereich ist es notwendig, eine große Anzahl von Patienten und Nutzern mit wenig Personal zu überwachen. Unsere Bild-/Videoanalyse-KI erkennt Anomalien in Echtzeit aus Kamerabildern. Sie erkennt automatisch Infektionskrankheiten, indem sie Fieber feststellt, alarmiert sofort, wenn ein Patient stürzt oder umherirrt, und erkennt Veränderungen des körperlichen Zustands durch Analyse der Mimik. Durch die Verringerung der Belastung durch nächtliche Patrouillen, die schnelle Reaktion auf Notfälle und die Automatisierung von Aufzeichnungsaufgaben kann sich das Personal auf seine primären Pflegeaufgaben konzentrieren. Zur Wahrung der Privatsphäre ist auch eine Vor-Ort-Installation möglich."}]},xP={title:"Kontakt",description:"Wir können Sie bereits in der Phase der Bedarfsplanung beraten und auch PoC- und lokale LLM-Konfigurationen in Betracht ziehen.",openForm:"Offene Form",closeForm:"Schließen Sie das Formular",form:{name:"Ihr Name",namePlaceholder:"Taro Yamada",email:"E-Mail Adresse",emailPlaceholder:"[email protected]",subject:"Thema",subjectPlaceholder:"Gegenstand Ihrer Anfrage",message:"Nachricht",messagePlaceholder:"Bitte füllen Sie Ihre Anfrage aus",required:"*",submit:"Senden Sie",submitting:"Ich schicke...",turnstileMessage:'Die Genehmigung "Ich bin kein Roboter" ist für die Einreichung erforderlich.',turnstileError:"Die Sicherheitsautorisierung konnte nicht geladen werden. Bitte neu laden.",errorRetry:"Bitte versuchen Sie es erneut."}},LP={brand:"telet.ai",tagline:"Multi AI-Kommunikationen",description:"Innovieren Sie Ihr Unternehmen mit KI-Agenten. Schaffen Sie Mehrwert durch Dialog.",sections:{product:{title:"Produkt",features:"Merkmal",benefits:"Einführung",testimonials:"Erfahrungsberichte von Kunden"},services:{title:"Dienst",agentDev:"Design und Entwicklung von KI-Agenten für Unternehmen",onPremise:"LLM-Server auf Abruf",vectorRag:"Vektor-RAG und Indexgestaltung",aiApp:"AI-Anwendungsentwicklung"},company:{title:"Organisationsinformationen",about:"Über telet.ai",profile:"Repräsentatives Profil",contact:"Kontakt"},process:{title:"Wie wir Geschäfte machen",hearing:"Anhörung / NDA",planning:"Planung & CA",sampling:"Probenahme & Start",development:"Entwicklung, Prüfung und Betrieb"}},copyright:"© 2026 telet.ai. Alle Rechte vorbehalten.",legal:{privacy:"Datenschutzbestimmungen",terms:"Nutzungsbedingungen",cookies:"Cookie-Richtlinie"}},PP={title:"Cookie-Richtlinie",subtitle:"Dieser Abschnitt beschreibt die auf dieser Website verwendeten Cookies und anderen Technologien (Cloudflare / Google Analytics).",lastUpdated:"Letzte Aktualisierung: 22. Januar 2026",intro:'telet.ai ("wir", "uns", "unser") ("wir") kann Cookies und ähnliche Technologien verwenden, um diese Website bereitzustellen und zu verbessern und um ihre Sicherheit zu gewährleisten. Wir setzen keine eigenen speziellen Tracking-Cookie

318s für Werbezwecke (obwohl Cloudflare und Google Analytics Cookies oder ähnliche Technologien in Übereinstimmung mit ihren jeweiligen Service-Spezifikationen setzen können).',sections:{what:{title:"1. Was ist ein Keks?",description:"Cookies sind kleine Textdateien, die in Ihrem Browser gespeichert werden, wenn Sie eine Website besuchen."},howWeUse:{title:"Zweck unserer Verwendung von Cookies usw.",description:"Wir können Cookies usw. hauptsächlich für folgende Zwecke verwenden",items:["Zur Stabilisierung der Anzeige und des Betriebs der Website","Erkennen und Verhindern von unberechtigtem Zugriff und bösartigem Datenverkehr","Um den Status des Zugangs zu verstehen und die Website zu verbessern"]},googleAnalytics:{title:"3. Verwendung von Google Analytics",description:"Wir verwenden Google Analytics 4 (GA4) von Google LLC für die Zugriffsanalyse.",items:["Zu den gesammelten Informationen gehören die aufgerufenen Seiten, die Verweildauer, die Verweisquelle, Geräte- und Browserinformationen usw.","Wir verwenden keine direkt persönlich identifizierbaren Informationen (Name, E-Mail-Adresse usw.), um sie an Google Analytics zu senden.","Die Behandlung der Daten durch Google unterliegt den Bedingungen und Richtlinien von Google"]},cloudflare:{title:"4. Nutzung von Cloudflare",description:"Wir nutzen die Dienste von Cloudflare, Inc. für die Bereitstellung und Sicherheitsmaßnahmen dieser Website. Cloudflare kann Cookies usw. für Zwecke wie DDoS-Schutz und Bot-Prävention setzen und darauf verweisen.",items:["Sie dienen in erster Linie der Sicherheit und der Optimierung der Kommunikation und sind nicht für die Nachverfolgung der Anzeigenschaltung bestimmt.","Die Behandlung von Daten durch Cloudflare unterliegt den Bedingungen und Richtlinien von Cloudflare"]},noSpecialCookies:{title:"5. unsere eigenen Spezialkekse",description:"Wir setzen keine eigenen speziellen Cookies zum Zwecke der verhaltensorientierten Werbung usw. Die Verwendung von Cookies usw. ist hauptsächlich auf den Betrieb der Website, die Sicherheit und die Zugriffsanalyse beschränkt."},choices:{title:"6. die Verwaltung von Cookies (Opt-out)",description:"Wenn Cookies deaktiviert sind, können einige Funktionen nicht verfügbar sein.",items:["Cookies können in den Browsereinstellungen gelöscht oder deaktiviert werden","Wenn Sie die Messung durch Google Analytics deaktivieren möchten, verwenden Sie bitte die von Google bereitgestellte Opt-out-Methode","Die Verwendung des privaten Modus eines Browsers usw. kann die gespeicherten Informationen einschränken."]},changes:{title:"7. Änderungen an dieser Politik",description:"Wir können diese Richtlinie als Reaktion auf Änderungen von Gesetzen und Vorschriften oder auf Änderungen bei den genutzten Diensten überarbeiten. Die überarbeitete Richtlinie tritt in Kraft, wenn sie auf dieser Seite veröffentlicht wird."},contact:{title:"8. Kontakt",description:"Wenn Sie Fragen zu dieser Politik haben, kontaktieren Sie uns bitte unter",company:"telet.ai",contactLink:"Weiter zum Anfrageformular"}}},wP={hero:{title:"Über telet.ai",subtitle:"Sorgfältige Unterstützung bei der KI-Entwicklung, die nur von einem kleinen, persönlichen Team geleistet werden kann."},story:{title:"Über uns",content:"telet.ai begann mit einer Frage. Warum gelingt es so vielen Unternehmen nicht, die Erkenntnisse zu nutzen, die in den unzähligen Interaktionen zwischen Unternehmen und ihren Kunden liegen? Das Team, das von einem Vertreter mit Erfahrung in der KI-Forschung und der Entwicklung von Unternehmenssoftware geleitet wird, wurde gegründet, um dieses Problem frontal anzugehen. Wir sind keine große Organisation. Deshalb sind wir in der Lage, uns eingehend mit Ihren Herausforderungen zu befassen und nur die Lösungen vorzuschlagen, die wirklich notwendig sind. Unsere Stärke ist unsere Fähigkeit, hybride Architekturen zu entwerfen, die lokales LLM und Cloud LLM integrieren. Wir können die sensiblen Daten Ihres Unternehmens in einer dedizierten Cloud-Umgebung schützen und gleichzeitig fortschrittliche KI-Inferenz in einer skalierbaren Weise durchführen. Wir stellen eine CLI für Entwickler und eine Chat-UI für Mitarbeiter zur Verfügung und schaffen so eine Grundlage für den Einsatz von KI in Ihrem gesamten Unternehmen. Und wir konzentrieren uns darauf, die optimale Lösung zu sein und nicht die fortsc

318hrittlichste. Der Verzicht auf Funktionen je nach Budget und die Überprüfung einer einzigen Konfiguration können einen großen Kostenunterschied ausmachen. Wir liefern Ihnen das, was Sie brauchen, in der Menge, die Sie brauchen. Unsere aufmerksame KI-Entwicklungsunterstützung ist nur möglich, weil wir klein und persönlich sind. Wir lösen Personalengpässe und unterstützen das Wachstum Ihres Unternehmens. Das ist das Versprechen von telet.ai."},representative:{title:"Repräsentant von telet.ai labo",name:"Keita Okuizumi",tagline:"Zum Himmel, zum Meer und zum Code - eine Leidenschaft für KI-Entwicklung, geboren aus einer ungewöhnlichen Karriere",paragraphs:['Als ehemaliger Privatpilot (PPL) und Inhaber eines Schiffsführerscheins habe ich mich viele Jahre lang mit dem Wetter in der Luft und auf See beschäftigt. Im Cockpit und im Steuerhaus beobachtete ich die Veränderungen des Barometers, entzifferte die isobaren Drucklinien und verglich sie mit der Bewegung von Wind und Wolken. Der Sinn für "Daten lesen, beurteilen und handeln", den ich durch diesen sich wiederholenden Prozess entwickelt habe, ist der Ausgangspunkt für mich heute.','Ich habe im Alter von 57 Jahren begonnen, Python zu programmieren. Der Anstoß dazu war einfache Neugier, aber sobald ich anfing, Code zu schreiben, war ich von den Möglichkeiten der KI-Agenten fasziniert. Ich habe mit meinen eigenen Händen Anwendungen erstellt, sie laufen lassen und verbessert. Dabei wurde mir etwas klar. Bei früheren Projekten war ich als Nicht-Ingenieur beteiligt, und die Entwicklung wurde immer von jemand anderem übernommen. Als ich jedoch anfing, den Code selbst zu schreiben, erkannte ich den Kern des Problems. Das ist der Engpass hier" oder "Dieser Entwurf kann nicht erweitert werden" - durch die Sichtweise eines Ingenieurs hat sich die Geschwindigkeit, mit der ich das, was ich tun will, verwirklichen kann, dramatisch erhöht.',"Entwicklung von Wetterdiensten durch die Kombination von Felderfahrung und Datenwissenschaft, Aufbau von KI-Agenten zur Verbesserung der betrieblichen Effizienz und verschiedene experimentelle Projekte. Als ich weiter mit meinen Händen arbeitete, war ich nicht nur davon überzeugt, dass die Technologie interessant ist, sondern auch, dass es Menschen geben muss, die das brauchen.","Unser Ziel ist es nicht, dem neuesten Stand der Technik hinterherzulaufen. Wir stellen uns den Problemen und dem Budget des Kunden und liefern nur das, was wirklich notwendig ist, zu einem vernünftigen Preis. Wir sind kein großes Unternehmen, so dass wir in der Lage sind, Entwicklungsunterstützung zu leisten, die auf die Bedürfnisse jedes einzelnen Kunden zugeschnitten ist. Wir wollen diese Technologie zum Nutzen der Gesellschaft einsetzen - deshalb haben wir telet.ai gegründet."]},contact:{title:"Organisationsinformationen",company:"telet.ai labo",ceo:"Stellvertreter: OKUIZUMI Keita",button:"Kontakt"},studios:{tokyo:{title:"Studio Tokio",address:"#301 First Hills A Musashikoganei, 3-3-21 Nukuiminamicho, Koganei-shi, Tokio 184-0014, Japan"}}},SP={title:"Portfolio",subtitle:"Sehen Sie unsere Erfolgsbilanz beim Einsatz von KI-Technologie",techStackLabel:"Technologie-Stapel",viewProject:"Projekt anzeigen",privateProject:"Private Projekte",visualizationLabel:"Datenvisualisierung",cta:{title:"Kontakt",description:"Bitte nehmen Sie Kontakt mit uns auf, um Ihr Projekt zu besprechen oder technische Fragen zu stellen.",button:"Kontakt"},projects:{"traffic-route-ai":{title:"KI-System zur Auswahl von Verkehrsverbindungen",subtitle:"Routenplanung für den öffentlichen Verkehr in Malaysia",description:"Das System sucht Bus- und Bahnverbindungen zwischen Start und Ziel und vergleicht Optionen nach Ankunftszeit, Reisedauer und Umstiegen. Filter für reine Bus- oder Bahnverbindungen, die Suche nach nahegelegenen Stationen und Haltestellen sowie eine mehrsprachige Oberfläche unterstützen die Routenwahl im Alltag und auf Reisen.",techStack:["ÖPNV-Routenplanung","Bus- und Bahnanbindung","Routenvergleich","Mehrsprachige Oberfläche"]},"multilingual-assistant":{title:"AI Mehrsprachiger Assistent",description:"Intelligenter Chat-Assistent für mehrsprachige Anfragen, der 1.573 Vektorindizes erstellt und eine präzise Antwortsteuerung durch bedingte Verzweigungen, NG-Wort-Filterung und Stichwortauslösung implementiert. Konsistente und hochpräzise Antworten auf Benutzerfragen in mehreren Sprachen, einschließlich Japanisch, Englisch und Chinesisch, werden durch sprachübergreifende semantische Suche generiert.",techStack:["RAG","Vektorsuche","Mehrsprachige Einbettung","LLM","bedingte Verzweigungslogik"]},"weather-forecast":{title:"AI-Wettervorhersage mit Wetter-API",description:"Entwicklung eines KI-Systems, das aus vergangenen Wetterdaten lernt und Echtzeitprognosen für aktuelle Wetterbedingungen erstellt. LLM analysiert vergangene Daten, die von der JMA-API, OpenWeatherMap usw. stammen, und erstellt kurzfristige Prognosen für Temperatur, Niederschlagswahrscheinlichkeit, Windgeschwindigkeit usw. Durch die Kombination der Mustererkennung von Zeitreihendaten mit der Suche nac

318h Ähnlichkeitsmustern in der Vergangenheit durch RAG erzielt das System hochpräzise Vorhersagen, die herkömmliche statistische Modelle ergänzen.",techStack:["Wetter-API-Kopplung","LLM","RAG","Analyse von Zeitreihendaten","Echtzeitverarbeitung"]},"local-llm":{title:"Interner Server / On-Prem Local LLM",description:"Wir liefern auf Ihre Geschäftsabläufe zugeschnittene KI, die in Ihrem Unternehmen sofort einsatzbereit ist, mit Open WebUI als gemeinsamen Einstiegspunkt, schnellen Inferenzspuren mit Ollama/MLX und vollständig getrennten Datendomänen für jede UID. Interne Dokumente werden in einem RAG mit englischer Einbettung und automatischer japanischer Abfrageübersetzung erfasst, und Business Skills wie Nachrichtenzusammenfassung, Dokumentenorganisation und Routineaufgaben werden sicher nebeneinander ausgeführt. Das Skill-Ausführungssystem ist als OpenClaw-Container isoliert und durch drei Schichten von Cloudflare Edge und lokalem Nginx geschützt. Wir bieten umfassende Unterstützung bei der Modellauswahl, dem Skill-Design und den Betriebsrichtlinien und machen die dedizierte KI Ihres Unternehmens vom ersten Tag an einsatzfähig.",techStack:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},"orbital-prediction":{title:"Interplanetare Orbital-Ankunftsvorhersage (AI)",description:"Entwicklung eines Systems zur Analyse optimaler Abflugpunkte für den interplanetaren Transfer durch Beschaffung von Planetenpositionsdaten, Bahnelementen und Schwerkraftparametern aus der NASA-API. Berechnung der Umlaufbahn unter Berücksichtigung des Hohmann-Transfer-Orbits und der Schwerkraftunterstützung mit Hilfe von API und Python sowie Anzeige des Startfensters, der benötigten Zeit und der Treibstoffeffizienz. Das System wird als Simulationswerkzeug für Bildungseinrichtungen und raumfahrtbezogene Unternehmen verwendet. *Die AI-Funktion wird derzeit entfernt, die Berechnung mit API und Python ist in Arbeit.",techStack:["NASA API (Horizons, SPICE)","Orbitaldynamische Berechnung","Python","Datenvisualisierung"]},"real-estate":{title:"AI-Analyse von Immobiliendaten",description:"Entwicklung eines Systems, das über Web Scraping Immobilieninformationen für eine bestimmte Region sammelt und mit LLM und Python eine erweiterte Datenanalyse durchführt. Variablen wie Immobilienpreis, Tsubo-Einheitspreis, Gebäudealter und Bahnhofsentfernung werden extrahiert, und Preistrendanalysen, Marktprognosen und Simulationen der Investitionsrendite werden automatisch erstellt. Merkmale wurden auch aus unstrukturierten Immobilienbeschreibungen mit LLM extrahiert, um eine umfassende Marktanalyse in Kombination mit quantitativen Daten zu ermöglichen. *Angebot jetzt als zweite Version verfügbar (FULL-Version separat erhältlich)",techStack:["Web-Scraping (Scrapy, Playwright)","Python","Pandas","LLM-Analyse","Datenvisualisierung"]},"fortune-ai":{title:"Vier Säulen des AI-Beurteilungssystems",subtitle:"Reproduktion traditioneller Weisheiten mit Hilfe von Technologie.",paragraphs:["Four Pillars of Divination ist der Höhepunkt der jahrtausendealten östlichen Wahrsagerei. Dieses System reproduziert die traditionelle Wahrsagerlogik vollständig in Python, kombiniert mit Interpretation und Ratschlagserzeugung unter Verwendung von LLM (Large Language Models). Die KI reproduziert originalgetreu den Beurteilungsprozess, der von einem erfahrenen Wahrsager durchgeführt wird.","Nach Eingabe von Datum, Uhrzeit und Geburtsort ruft das System die Sonnenaufgangs- und Sonnenuntergangszeiten sowie die Koordinateninformationen zu diesem Zeitpunkt von der Astronomie-API ab. Auf der Grundlage dieser Daten berechnet das System genau das Tierkreiszeichen, die zehn Götter und das Gleichgewicht der fünf Elemente. Die auf der Divinationswissenschaft der Vier Säulen des Schicksals basierende Logik wird in Python indiziert und mit statistischen Daten und RAG-Informationen (Retrieval Augmentation Generation) kombiniert, um die Genauigkeit der Lebensformelanalyse zu verbessern.","Die berechneten Lebensstildaten werden von LLM in natürlicher Sprache auf leicht verständliche Weise erklärt. Es werden personalisierte Einschätzungsergebnisse generiert, einschließlich des Glücksflusses, der Kompatibilität zwischenmenschlicher Beziehungen und des für Sie geeigneten Berufs. Sie können Antworten mit der gleichen Tiefe erhalten, als ob Sie einen professionellen Wahrsager konsultiert hätten.","Sie ist derzeit in einer zweiten Version verfügbar. Sie können die grundlegende Kumishiki-Analyse und die Glücksanalyse ausprobieren. Die Vollversion, die detailliertere Analysen und vielseitige Ratschläge enthält, ist separat erhältlich."],techStack:["Python","Astronomie-API (Sonnenaufgang-Sonnenuntergang, TimeZoneDB)","Vier-Säulen-Logik","LLM","Koordinaten- und Zeitberechnung"]}

318,"google-workspace":{title:"Google Workspace AI integration",description:"Entwicklung eines Systems, das Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) mit LLM verbindet, um Geschäftsdaten zu lesen, zu bearbeiten und automatisch zu verarbeiten. KI unterstützt bei täglichen Aufgaben wie dem Zusammenfassen von E-Mail-Inhalten, dem Erstellen von Entwürfen für automatische Antworten, dem Durchsuchen und Analysieren von Drive-Dateien, dem Extrahieren von Zeitplänen aus dem Kalender und dem Vorschlagen von Anpassungen. Der Betrieb des Arbeitsbereichs vom Agenten aus wird auch durch eine sichere API-Verbindung mit OAuth-Authentifizierung und MCP-konformem Tool-Design realisiert. Die zweite Version ist jetzt verfügbar (die FULL-Version wird separat bereitgestellt).",techStack:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"]},"world-news":{title:"World NEWS Mehrsprachigkeit",description:"Entwicklung eines Systems, das Artikel in Echtzeit aus Nachrichtenquellen auf der ganzen Welt sammelt und eine mehrsprachige Übersetzung, Zusammenfassung und Kategorisierung mit LLM durchführt. Übersetzt Originalartikel in Englisch, Chinesisch, Französisch, Arabisch und anderen Sprachen ins Japanische und ordnet sie nach Themen. Effiziente internationale Nachrichtenüberwachung wird nicht durch einfache maschinelle Übersetzung erreicht, sondern durch natürliche Übersetzung, die auf kontextuellem Verständnis unter Verwendung von LLM und der Erstellung von Zusammenfassungen basiert, die die wichtigsten Punkte des Artikels extrahieren. *Angebot jetzt als zweite Version verfügbar (FULL-Version separat erhältlich).",techStack:["Nachrichten-API","RSS-Sammlung","LLM Übersetzung und Zusammenfassung","mehrsprachige Verarbeitung","Auto-Kategorisierung","geplante Stapelverarbeitung"]},"security-monitoring":{title:"Sicherheitsprotokollüberwachung AI Manager",description:"Entwicklung eines Systems für die Echtzeit-Überwachung von Sicherheitsprotokollen in Unternehmen und für die fortschrittliche Erkennung und Analyse von Bedrohungen mithilfe von LLM. Verschiedene Protokollquellen wie Firewalls, IDS/IPS, Authentifizierungsprotokolle und Anwendungsprotokolle werden von einem Python-Modul gesammelt und analysiert und mit früheren Angriffsmustern, Schwachstelleninformationen und Vorfällen, die in RAG gesammelt wurden, verglichen. Wenn ein anormales Muster erkannt wird, erklärt LLM den Bedrohungsgrad, das Ausmaß der Auswirkungen und die empfohlene Reaktion in natürlicher Sprache und alarmiert sofort die verantwortliche Person. Wenn das gleiche Muster wieder auftritt, wird automatisch eine Warnung mit hoher Priorität ausgegeben, um die Reaktion auf den Vorfall zu beschleunigen.",techStack:["Python-Überwachungsmodul","Log-Sammlung (Fluentd, Logstash)","RAG","Bedrohungsmuster-DB","LLM-Analyse","Warnmeldungen (Slack, PagerDuty)"]}}},IP={learnMore:"Mehr lesen",getStarted:"anfangen zu ...",contactUs:"Kontakt",backToHome:"Zurück zu Home"},MP={title:"Datenschutzbestimmungen",subtitle:"Datenschutzbestimmungen",lastUpdated:"Letzte Aktualisierung: 23. Januar 2026",intro:'telet.ai (der "Dienst") betrachtet den Schutz Ihrer persönlichen Daten als eine wichtige Verantwortung und hat die folgenden Datenschutzrichtlinien aufgestellt.',sections:{collection:{title:"1. zu erfassende Informationen",description:"Der Dienst kann die folgenden Informationen erfassen, wenn Sie uns kontaktieren oder den Dienst nutzen.",items:["E-Mail Adresse","Ihr Name (optional)","Anfrage Details","Andere von Ihnen freiwillig zur Verfügung gestellte Informationen"]},purpose:{title:"2. Zweck der Verwendung von Informationen",description:"Die gesammelten persönlichen Daten werden nur für folgende Zwecke verwendet",items:["Beantwortung von Anfragen und Beantwortung von Anträgen","Kontakt und Informationen über unsere Dienstleistungen","Für den Projektfortschritt erforderliche Kommunikation","Analyse zur Verbesserung der Dienstleistungen (in einem Format, das keine Rückschlüsse auf Einzelpersonen zulässt)"]},thirdParty:{title:"3. Bereitstellung für Dritte",description:"Der Dienst gibt Ihre persönlichen Daten nicht an Dritte weiter, außer in folgenden Fällen",items:["Wenn der Kunde zustimmt","Wenn ein Antrag auf Offenlegung aufgrund von Gesetzen und Vorschriften vorliegt","Wenn dies zum Schutz des Lebens, des Körpers oder des Eigentums einer Person erforderlich ist"]},security:{title:"4. Informationsmanagement",description:"Der Dienst verwaltet die gesammelten personenbezogenen Daten ordnungsgemäß und ergreift angemessene Sicherheitsmaßnahmen, um unbefugten Zugriff, Verlust, Weitergabe und Fälschung zu verhindern."},email:{title:"5. Bearbeitung von E-Mails",description:"Der Dienst behandelt E-Mails nach Maßgabe der folgenden Bestimmungen.",items:["Wir werden den Inhalt Ihrer E-Mail nur für die Beantwortung Ihrer Anfrage und die Erbringung unserer Dienstleistungen verwenden.","Wir werden Ihre E-Mail-Adresse nicht verkaufen, vermieten oder an Dritte weitergeben.","Wir 

318können Ihnen Informations-E-Mails über unsere Dienstleistungen schicken. Wenn Sie sich abmelden möchten, verwenden Sie bitte den Abmeldelink in der E-Mail oder kontaktieren Sie uns über die nachstehenden Kontaktinformationen.","Für die E-Mail-Kommunikation wird eine SSL/TLS-Verschlüsselung verwendet, um eine sichere Kommunikation zu gewährleisten.","Wir werden Ihre E-Mail nach einer angemessenen Aufbewahrungszeit sicher löschen."]},cookies:{title:"6. Verwendung von Cookies",description:"Cookies können in den Einstellungen Ihres Browsers deaktiviert werden."},external:{title:"7. Inanspruchnahme von externen Dienstleistungen",description:"Dieser Dienst kann die folgenden externen Dienste nutzen. Bitte beachten Sie die jeweiligen Links zu den Datenschutzbestimmungen der einzelnen Dienste.",items:["Google Analytics (access analysis)","Andere für den Geschäftsbetrieb erforderliche Cloud-Dienste"]},changes:{title:"8. Änderungen der Datenschutzpolitik",description:"Diese Richtlinie kann ohne Vorankündigung geändert werden, wenn Gesetze und Vorschriften geändert werden oder wenn sich der Inhalt der Dienste ändert. Die überarbeitete Richtlinie tritt in Kraft, sobald sie auf dieser Seite veröffentlicht wird."},contact:{title:"9. Kontakt",description:"Wenn Sie Fragen zu dieser Politik haben, kontaktieren Sie uns bitte unter",company:"telet.ai",contactLink:"Weiter zum Anfrageformular"}}},jP={title:"Nutzungsbedingungen",subtitle:"Dies sind die Bedingungen für die Nutzung dieser Website und für die Buchung einer Beratung, für Anfragen und für die Anforderung von Informationen von telet.ai.",lastUpdated:"Letzte Aktualisierung: 22. Januar 2026",intro:'Diese Nutzungsbedingungen (im Folgenden als "Bedingungen" bezeichnet) Diese Nutzungsbedingungen (im Folgenden als "Bedingungen" bezeichnet) gelten für die von telet.ai (im Folgenden als "wir", "uns", "unser" oder "unser" bezeichnet) betriebene Website. (im Folgenden als "wir", "uns" oder "unser" bezeichnet). (im Folgenden als "Website" bezeichnet), betrieben von telet.ai (im Folgenden als "wir" bezeichnet). Die Website wird in erster Linie als Landing Page für Beratungstermine, Anfragen und Dokumentenanfragen zur Verfügung gestellt und bietet keine Funktionen zur Erstellung von Konten oder zur bezahlten Abrechnung auf der Website.',sections:{scope:{title:"1. Anwendungsbereich",description:'Diese Nutzungsbedingungen gelten für alle Besucher und Nutzer der Website ("Nutzer"). Die Nutzungsbedingungen gelten für alle Besucher und Nutzer der Website (nachstehend "Nutzer" genannt). Falls zwischen uns und einem Nutzer ein separates NDA, eine Outsourcing-Vereinbarung, ein individueller Vertrag usw. geschlossen wird, hat dieses Dokument Vorrang vor den Nutzungsbedingungen.'},prohibited:{title:"2. verbotene Gegenstände",description:"Bei der Nutzung der Website dürfen die Nutzer keine der folgenden Handlungen vornehmen",items:["Handlungen, die gegen Gesetze und Vorschriften oder die öffentliche Ordnung und Moral verstoßen","Jede Handlung, die unsere Rechte am geistigen Eigentum, die Privatsphäre oder andere Rechte oder Interessen eines Dritten verletzt","Handlungen, die den Betrieb dieser Website oder damit verbundener Systeme durch unbefugten Zugriff, übermäßige Belastung, Sabotage usw. stören.","Angabe falscher Informationen oder Vortäuschung einer dritten Person in Anfrageformularen usw.","Scraping, Reverse Engineering oder übermäßiger automatisierter Zugriff ohne unsere vorherige Genehmigung"]},disclaimer:{title:"3. Haftungsausschluss",description:'Wir stellen diese Website ohne Mängelgewähr" und wie verfügbar" zur Verfügung. Im vollen gesetzlich zulässigen Umfang schließen wir alle Garantien in Bezug auf diese Website aus.',items:["Die Informationen auf dieser Website können ohne vorherige Ankündigung geändert oder gelöscht werden.","Wir übernehmen keine Gewähr für die Richtigkeit, Vollständigkeit oder Eignung der Informationen auf dieser Website für einen bestimmten Zweck.","Wir haften nicht für Schäden, die sich aus der Nutzung dieser Website oder dem Vertrauen auf die darin enthaltenen Informationen ergeben.","Die Antworten auf Konsultationen und Anfragen dienen nur der allgemeinen Information und stellen keine rechtliche, steuerliche, medizinische oder sonstige professionelle Beratung dar."]},ip:{title:"4. Rechte an geistigem Eigentum",description:"Alle Rechte an den Texten, Bildern, Designs, Marken, Logos usw. auf dieser Website gehören uns oder den rechtmäßigen Inhabern der Rechte. Sie dürfen sie nicht ohne die Erlaubnis der Rechteinhaber verwenden."},links:{title:"5. Links zu externen Websites",description:"Diese Website kann Links zu Websites Dritter enthalten. Wir sind nicht verantwortlich für den Inhalt oder den Betrieb der verlinkten Seiten."},changes:{title:"6. Änderung dieser Bedingungen und Konditionen",description:"Wir behalten uns das Recht vor, diese Nutzungsbedingungen von Zeit zu Zeit nach Bedarf zu ändern. Die geänderten Nutzungsbedingungen treten in Kraft, wenn sie auf der Website veröffentlicht werden."},governingLaw:{title:"7. Geltendes Recht und Gerichtsbarkeit",description:"Diese Allgemeinen Geschäftsbedingungen unterliegen den Gesetzen Japans und werden nach diesen ausgelegt. Für alle Streitigkeiten, die sich aus oder im Zusammenhang mit dieser Website ergeben, ist das Bezirksgericht Tokio als Gericht erster Instanz ausschließlich zuständig."},contact:{title:"8. Kontakt",description:"Wenn Sie Fragen zu diesen Geschäftsbedingungen haben, wenden Sie sich bitte an",company:"telet.ai",contactLink:"Weiter zum Anfrageformular"}}}

318,CP={title:"regelmäßiger Antrag",subtitle:"KI prognostiziert die",description1:"Wetterinformationen werden von jedem Land in Echtzeit bereitgestellt. Ihr Land wird in der Mitte der Karte angezeigt. Das gestrige und heutige Wetter sind tatsächliche Aufzeichnungen – die Regenwolken sollten zu diesem Zeitpunkt an Ihrem Himmel gewesen sein.",description2:"Diese Animation animiert aber auch zukünftige Wetterbewegungen.",description3:"Vektoren für vergangene Daten und sucht mit hoher Geschwindigkeit nach Mustern. Auch Wettermuster lassen sich aus der Umgebung vorhersagen. In ähnlicher Weise ist es möglich, Ernte, Produktion und Umsatz vorherzusagen, indem man Elemente (i) aus der Umgebung und fortgeschrittene statistische Werte aus vergangenen Leistungen berechnet und sie von der KI analysieren lässt.",refresh:"Auffrischung (z. B. Speicher)",loading:"Laden der Wetterkarte..."},DP={hero:{badges:["Unternehmen","Lokaler LLM","Zollagent"],title:"Design und Entwicklung von KI-Agenten für Unternehmen",description:"Entwurf und Erstellung von speziellen, auf die Geschäftsanforderungen zugeschnittenen KI-Agenten"},mission:{title:"Kundenspezifische AI-Agent-Entwicklungsdienste",description:"Wir entwickeln maßgeschneiderte KI-Agenten, die von der Konzeption über die Implementierung bis hin zum Betrieb auf die speziellen Geschäftsanforderungen Ihres Unternehmens zugeschnitten sind. Unser architektonisches Design betont die Kompatibilität mit bestehenden unternehmenskritischen Systemen und SaaS-Umgebungen und ermöglicht eine reibungslose Integration mit APIs, Datenbanken und internen Tools. Unsere Ingenieure sind mit LangChain, AutoGen, CrewAI und anderen wichtigen Frameworks vertraut. Unsere Ingenieure sind mit LangChain, AutoGen, CrewAI und anderen wichtigen Frameworks vertraut und werden die beste Lösung für den Technologie-Stack und die Betriebsstruktur Ihres Unternehmens vorschlagen. Wir entwickeln nicht nur einzelne Chatbots, sondern bauen auch Multi-Agenten-Systeme, in denen mehrere Agenten miteinander kooperieren, um Aufgaben zu erfüllen, sowie Agentic Workflows, die in Verbindung mit externen Tools arbeiten. Wir unterstützen die Maximierung des geschäftlichen Nutzens von KI-Agenten durch eine langfristige Partnerschaft, die kontinuierliche Verbesserungen nach der Implementierung, nachfolgende Modell-Updates und die Wartung von Betriebsüberwachungssystemen umfasst.",imageAlt:"Kundenspezifische AI-Agent-Entwicklungsdienste"},summary:{title:"Trends in der Agentenentwicklung",paragraphs:['Von 2025 bis 2026 wird die Entwicklung von KI-Agenten von der Demonstrationsphase in die Phase der umfassenden Implementierung in Unternehmen übergehen. 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Die Standardisierung des A2A-Protokolls (Agent-to-Agent) hat ebenfalls Fortschritte bei der Zusammenarbeit von Agenten zwischen verschiedenen Anbietern gebracht.",'Das Governance-Modell hat sich von "Human-in-the-Loop" zu "Human-on-the-Loop" für Unternehmensimplementierungen entwickelt, wobei ein Mechanismus für die Prüfung von Entscheidungen nach dem Agenten und eine automatische Eskalation auf der Grundlage der Risikostufe zu den Standardfunktionen gehören, was die Einführung in regulierten Branchen wie dem Finanzwesen, dem Gesundheitswesen und der Fertigung beschleunigt. Die Einführung dieses Systems beschleunigt sich in regulierten Branchen wie dem Finanzwesen, dem Gesundheitswesen und der Fertigung.']},features:{title:"Design und Entwicklung",subtitle:"Design und Entwicklung",items:[{title:"Agentendesign auf der Grundlage von Geschäftsanforderungen",description:"Extrahieren von Anwendungsfällen aus der Geschäftsflussanalyse, Zerlegen von Aufgaben und Klären der Zuständigkeiten von Agenten. Wir bestimmen, ob die Verarbeitung von einem einzelnen Agenten abgeschlossen werden kann oder eine Koordination mehrerer Agenten erfordert, und wählen die optimalen Inferenzmuster wie ReAct, Plan-and-Execute und Reflexion."},{title:"RAG / Werkzeug / Workflow-Integration",description:"Hochpräzise RAG-Pipelines mit Einbettungsgenerierung, Chunk-Strategie und hybrider Suche, standardisierte Verbindung zu externen APIs und Datenbanken mit MCP-konformen Tool-Definitionen und mehrstufige Workflow-Orchestrierung mit LangGraph und CrewAI. 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318-Maskierung sind implementiert. Audit-Protokolle aller Eingaben und Ausgaben werden aufbewahrt, und der Betrieb einer SOC2- und ISO27001-konformen Umgebung wird unterstützt."},{title:"Umsetzung einschließlich Betrieb und Überwachung",description:"Implementierung der Trace-Visualisierung mit LangSmith, Langfuse usw. und Echtzeitüberwachung von Latenz, Token-Verbrauch und Fehlerrate. Aufbau eines kontinuierlichen Verbesserungszyklus mit Warnmeldungen zur Erkennung von Anomalien, Infrastruktur für A/B-Tests und Regressionstests bei der Aktualisierung von Modellen."}]},techStack:{title:"Technologie-Stapel",subtitle:"Plattform und Technologie",categories:{agentFramework:{title:"Agent Framework",items:["AutoGen 0.4 / AG2","CrewAI","LangGraph","OpenAI Agenten SDK","Anthropischer MCP","Google ADK (Agent Development Kit)","Semantischer Kern","Proprietäre Agentenplattform"]},apiLayer:{title:"API-Schicht",items:["FastAPI / Litestar","gRPC / Verbinden","GraphQL-Föderation","Server-gesendete Ereignisse (SSE)","WebSocket / NATS","OAuth 2.1 / OIDC","API-Gateway (Kong / Envoy)","Adaptive Ratenbegrenzung"]},vectorDb:{title:"Vektor DB / RAG",items:["Kiefernzapfen","Weaviate","Qdrant","Milvus","ChromaDB","pgvector","FAISS","Turbopuffer"]}

318,infrastructure:{title:"Infrastruktur",items:["Kubernetes (EKS / GKE / AKS)","Docker/Podman","Vor-Ort-GPU-Cluster","Private Wolke","Serverlos (Lambda / Cloud Run)","OpenTelemetry / Langfuse","Terraform / Pulumi","ArgoCD / Fluss"]}},updateNote:{title:"Wichtigste Aktualisierungspunkte für 2026",description:"Im Agenten-Framework sind offizielle SDKs/Protokolle von großen Anbietern wie OpenAI Agents SDK, Anthropic MCP und Google ADK im Kommen; im API-Layer sind gRPC und SSE zum Standard für die Kommunikation zwischen Agenten geworden, und der Übergang zu OAuth 2.1 ist im Gange. Im Bereich Vector DB machen Qdrant und Turbopuffer durch ihre Hochgeschwindigkeitssuche auf sich aufmerksam."}},cta:{title:"Kontakt",description:`Wir können Sie bei der Zusammenstellung der Anforderungen beraten.
319PoC und lokale LLM-Konfigurationen können ebenfalls berücksichtigt werden.`,primaryButton:"Kontakt",secondaryButton:"Technische Konsultation"}},zP={title:"Häufig gestellte Fragen",subtitle:"FAQ",items:[{question:"Kann ich meine bestehenden Handbücher und FAQs ohne Änderungen verwenden?",answer:"Ja, Sie können es verwenden; es unterstützt die meisten Formate, einschließlich PDF, Word, Excel, HTML und Textdateien. Laden Sie einfach Ihre vorhandenen internen Dokumente so hoch, wie sie sind, und KI lernt den Inhalt und verwendet ihn, um Ihre Fragen zu beantworten. Es ist keine Formatkonvertierung oder spezielle Bearbeitung erforderlich."},{question:"Ist die Verwendung mit Daten, die sensible Informationen enthalten, sicher?",answer:"Ja, Sie können unseren Dienst sicher nutzen. Ob vor Ort (interne Server) oder in einer privaten Cloud-Umgebung, die Daten werden niemals außerhalb des Unternehmens übertragen. Wir können Ihnen Konfigurationen vorschlagen, die Ihre Sicherheitsanforderungen erfüllen."},{question:"Wie lange dauert die Umsetzung?",answer:"Ein Standard-FAQ-Bot kann in nur 2-4 Wochen implementiert werden. Wenn eine Integration in bestehende Systeme oder eine erweiterte Anpassung erforderlich ist, veranschlagen wir 1-3 Monate. Wir können auch mit einem PoC (Proof of Concept) 

319im kleinen Rahmen beginnen."},{question:"Verwenden Sie externe APIs wie ChatGPT oder Claude?",answer:"Eine Auswahl ist auf Anfrage möglich. Wir unterstützen sowohl Konfigurationen, die externe APIs (OpenAI, Anthropic, Google usw.) verwenden, als auch Konfigurationen, die Open-Source-LLMs (Llama, Mistral, Qwen usw.) verwenden, die vollständig lokal laufen. Wir können Ihnen die beste Konfiguration für Ihre Kosten-, Sicherheits- und Leistungsanforderungen vorschlagen."},{question:"Wie genau ist die Antwort der KI?",answer:"Je nach Anwendung und Datenqualität kann ein richtig konzipiertes RAG-System Fragen, für die relevante Dokumente existieren, mit einer Genauigkeit von 90 % oder mehr beantworten. Mechanismen zur Kontrolle der Halcination (Generierung von fehlerhaften Informationen) sind ebenfalls standardmäßig eingebaut."},{question:"Unterstützen Sie auch andere Sprachen als Japanisch?",answer:"Ja, mehrsprachige Unterstützung ist verfügbar. Wir unterstützen alle wichtigen Sprachen, einschließlich Japanisch, Englisch, Chinesisch und Koreanisch. Anstatt separate Modelle für jede Sprache zu haben, kann ein einziges System mehrsprachige Abfragen bearbeiten."},{question:"Können Sie sich in bestehende Systeme (CRM, Chat-Tools usw.) integrieren?",answer:"Ja, er kann integriert werden. Er unterstützt die API-Integration mit wichtigen Business-Tools wie Salesforce, HubSpot, Slack, Microsoft Teams und LINE WORKS. Sie können einen KI-Assistenten einbinden, ohne Ihren bestehenden Arbeitsablauf zu ändern."},{question:"Wie ist Ihre Gebührenstruktur?",answer:"Das hängt von der Größe und den Anforderungen des Projekts ab. In der Regel werden die Gebühren für den Erstaufbau und den monatlichen Betrieb kombiniert, aber wir akzeptieren auch Anfragen für die Entwicklung von Spotlights oder reine Beratungsleistungen. Bitte teilen Sie uns zunächst Ihre Anforderungen mit. Wir werden Ihnen dann einen groben Kostenvoranschlag unterbreiten."}]},TP={title:"Tarif-Pläne",subtitle:"Preispläne",description:"Flexible Pläne zur Anpassung an die Größe Ihres Unternehmens",plans:[{name:"Starthilfe",price:"29.800 Yen",period:"Monatlich/Nutzer",description:"Der beste Plan für kleine Teams. KI-Basisfunktionen zu einem erschwinglichen Preis.",features:["Bis zu 100 Anrufe pro Monat","Grundlegende Transkription","Schlüsselwort-Erkennung","E-Mail-Unterstützung","7 Tage Datenspeicherung"],cta:"Jetzt kostenlos loslegen",popular:!1},{name:"professionell",price:"¥79,800",period:"Monatlich/Nutzer",description:"Ideal für wachsende Teams. 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319},RP={hero:{badge:"Intelligente Erkennung",title:"Verfolgung von Schlüsselwörtern",description:"Überwachen Sie automatisch Erwähnungen von Mitbewerbern, Gegenargumente und wichtige Themen. Verpassen Sie keine wichtigen Signale aus Kundengesprächen.",badges:["Verfolgung von Wettbewerbern","Antwort-Erkennung","Themenanalyse","benutzerdefiniertes Schlüsselwort"]},features:{title:"Wichtige Informationen verfolgen",subtitle:"Automatische Erkennung und Analyse von Schlüsselwörtern aus allen Gesprächen",items:[{title:"Überwachung des Wettbewerbs",description:"Verfolgen Sie Erwähnungen von Mitbewerbern, um zu verstehen, wie potenzielle Kunden Ihre Lösung mit Alternativen auf dem Markt vergleichen."},{title:"Antwort-Erkennung",description:"Erkennen Sie automatisch häufige Einwände und Bedenken und entwickeln Sie effektive Antworten und Schulungen."},{title:"benutzerdefiniertes Schlüsselwort",description:"Definieren und verfolgen Sie benutzerdefinierte Schlüsselwörter und Phrasen, die für Ihr Unternehmen, Ihr Produkt oder Ihre Branche spezifisch sind."},{title:"Trendanalyse",description:"Entdecken Sie neue Trends aus Kundengesprächen und passen Sie Ihre Strategie auf der Grundlage aktueller Marktsignale an."},{title:"Kontextextextraktion",description:"Holen Sie sich den Kontext vor und nach dem Schlüsselwort, nicht nur die Erwähnung des Schlüsselworts. Verstehen Sie nicht nur, was gesagt wurde, sondern auch warum und wie es gesagt wurde."},{title:"Intelligente Suche",description:"Durchsuchen Sie sofort alle Unterhaltungen, um genau herauszufinden, wo und wann ein bestimmtes Thema diskutiert wurde."}]},applications:{title:"Verwendungsszenen",items:{competitive:{title:"Competitive Intelligence",description:"Tiefes Verständnis der Wettbewerbslandschaft:",points:["Verfolgen Sie Ihre am häufigsten erwähnten Konkurrenten","Verstehen, wie potenzielle Kunden Merkmale und Preise vergleichen","Identifizieren Sie Ihre Wettbewerbsvorteile und Schwächen","Schaffung wirksamer wettbewerbswidriger Materialien"]},objections:{title:"Erwiderung einer Gegendarstellung",description:"Verwandeln Sie eine Widerlegung in eine Chance:",points:["Identifizieren Sie die häufigsten Kundenanliegen","Analysieren Sie die Antworten der Leistungsträger","Playbook mit bewährter Reaktion.","Trainieren Sie Ihr Team mit effektiven Turnarounds"]},product:{title:"Produkt-Einblicke",description:"Förderung der Produktentwicklung durch Kundenfeedback:",points:["Identifizierung der am meisten gewünschten Funktionen","Verstehen der Herausforderungen für die aktuelle Funktionalität","Entdecken Sie unerwartete Anwendungsfälle","Priorisierung der Roadmap auf der Grundlage der Kundenbedürfnisse"]},compliance:{title:"Einhaltung der Vorschriften und Qualität",description:"Sicherstellung der Einhaltung von Vorschriften und der Qualität:",points:["Überwachung der erforderlichen Angaben und der Rechtssprache","Kennzeichnung von Gesprächen, die gegen die Richtlinien verstoßen könnten","Verfolgung der Einhaltung von Skripten und bewährten Verfahren","Erstellung von Compliance-Berichten für Audits"]}}},advancedFeatures:{title:"Erweiterte Funktionen",items:[{title:"mehrdeutiger Abruf",description:"Automatische Erkennung von Schlüsselwortvarianten, Synonymen und verwandten Begriffen ohne manuelle Konfiguration."},{title:"historische Analyse",description:"Analysieren Sie Keyword-Trends im Zeitverlauf, um saisonale Muster und neue Themen zu erkennen."},{title:"Warnmeldungen in Echtzeit",description:"Erhalten Sie sofortige Benachrichtigungen, wenn wichtige Schlüsselwörter in Live-Unterhaltungen erwähnt werden."}]}},NP={hero:{badges:["Unternehmen","Lokaler LLM","Interner Serverbetrieb"],title:"Vor-Ort-LLM-Konstruktion",description:"Betreiben Sie LLM in Ihrer eigenen Umgebung und halten Sie die Daten außerhalb des Unternehmens."},mission:{title:"Überblick über den LLM-Bauservice vor Ort",paragraphs:["Für Unternehmen, die vertrauliche Daten nicht nach außen geben dürfen, erstellen wir eine LLM-Umgebung, die auf ihren eigenen Servern oder in einer privaten Cloud ausgeführt werden kann. Diese Lösung ist ideal für Organisationen, die ein strenges Informationsmanagement benötigen, wie z. B. Finanzinstitute, medizinische Einrichtungen, Regierungsbehörden und Forschungsinstitute, da sie keine öffentliche API verwendet und die gesamte Verarbeitung innerhalb der vom Kunden verwalteten Infrastruktur erfolgt.","Wir bieten umfassende Unterstützung von der Modellauswahl über die Serverkonstruktion und die Optimierung der Inferenzmaschine bis hin zur Betriebsüberwachung. Dabei verwenden wir handelsübliche Modelle mit offenem Gewicht wie Llama 3.3, Qwen 2.5, Mistral und DeepSeek mit Hochleistungs-Inferenzmaschinen wie vLLM, Ollama und SGLang. Diese Modelle werden auf Hochleistungs-Inferenzmaschinen wie vLLM, Ollama und SGLang ausgeführt, um eine Inferenzumgebung mit niedriger Latenz und hohem Durchsatz zu schaffen.","Wir unterstützen eine breite Palette von Anwendungsfällen wie die Integration in bestehende Systeme, Chatbots, interne RAGs, Dokumentenzusammenfassung und Codegenerierung. Wir können die Anforderungen von Unternehmen erfüllen, einschließlich Geschäftsspezialisierung durch Modellfeinabstimmung, NVIDIA GPU / Apple Silicon Optimierung, mandantenfähiges Design, API-Gateway-Konstruktion und mehr."]},summary:{title:"Vor-Ort-LLM-Trends",paragraphs:["In den Jahren 2025-2026 verbessert sich der Nutzen von LLMs vor Ort rapide. Die Leistung offener gewichteter Modelle hat die geschlossener Modelle überholt, und die Zahl der Modelle, die mit kommerziell verfügbaren Lizenzen angeboten werden, hat die Hürde für den internen Betrieb deutlich gesenkt.","Die Modelle mit 8B bis 70B Parametern haben ein praktisches Niveau für viele Geschäftsaufgaben erreicht, die GGUF-Quantisierung ermöglicht Hochgeschwindigkeitsinferenz sogar auf Consumer-GPUs.","Bei den Inferenz-Engines erhöhen Continuous Batching und PagedAttention von vLLM den Durchsatz dramatisch, und RadixAttention von SGLang beschleunigt komplexe Interaktionen mit mehreren Umdrehungen. Ollama hat sich zum De-facto-Standard für lokale Entwicklungsumgebungen entwickelt, und llama.cpp ist Apple Silicon, das für schnelle Inferenzen auf Macs der M-Serie optimiert ist.","Bei der Einführung in Unternehmen wird neben der Datensouveränität und der Einhaltung von Vorschriften auch die langfristige Kostenoptimierung (Abkehr von der Abrechnung nach Aufwand) immer mehr zu einem Motiv für die Einführung. Hybride Konfigurationen (lokal für sensible Verarbeitungen und Cloud-API für allgemeine Verarbeitungen) sind eine gängige Option geworden."],icons:[{label:`Privat
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323arakuri-LM"]},inferenceEngine:{title:"Inferenzmaschine",items:["vLLM (Kontinuierliches Batching)","SGLang (RadixAttention)","Ollama (lokale Entwicklung)","llama.cpp (Apple Silicon)","TGI (Umarmendes Gesicht)","TensorRT-LLM (NVIDIA)","ExLlamaV2 (Quantisierungsspezialisierung)"]},quantization:{title:"Quantisierung",items:["GGUF (Lama.cpp Standard)","AWQ (hohe Präzision 4bit)","GPTQ (GPU-Optimierung)","BitsundBytes(8bit/4bit)","EETQ (Effiziente Wirkungsgradquantisierung)"]},hardware:{title:"Hardware",items:["NVIDIA H100 / A100","NVIDIA L40S / A10G","RTX 4090 / 5090","Apple Silizium M2 Ultra","Apple Silizium M3/M4 Max","AMD MI300X"]},infrastructure:{title:"Infrastruktur",items:["Docker / Kubernetes","NVIDIA Triton-Server","API-Gateway (Kong / Envoy)","Prometheus / Grafana","Private Cloud (VMware/OpenStack)","Unterstützung für eine luftdurchlässige Umgebung"]}},updateNote:{title:"Wichtigste Aktualisierungspunkte für 2026",description:"Bei den Modellen mit offenem Gewicht erreicht Llama 3.3 eine Allzweckleistung der Klasse GPT-4 und verbessert die Leistung der japanischen Sprache erheblich; die Qwen 2.5-Serie erweitert die Auswahl mit einer Vielzahl von Größen und ausgewogener Leistung; DeepSeek V3/R1 zieht die Aufmerksamkeit auf seine Inferenzkostenleistung; und die Inferenzmaschinen vLLM 2.0 Chunked Prefill und SGLang's RadixAttention beschleunigen Multiturn-Dialoge. In der Inferenz-Engine beschleunigen Chunked Prefill von vLLM 2.0 und RadixAttention von SGLang Multiturn-Interaktionen; die Apple Silicon Optimierung von llama.cpp macht die Inferenz von 70B-Modellen auf M4 Max praktikabel; und die Quantisierungs-Technologie bietet 4-Bit-Präzision und hohe Leistung. Durch die Quantisierungstechniken wurden sowohl eine 4-Bit-Präzision als auch eine hohe Inferenzgeschwindigkeit erreicht, und GGUF wurde zum De-facto-Standardformat."}},cta:{title:"Kontakt",description:`Wir können Sie bei der Zusammenstellung der Anforderungen beraten.
324PoC und lokale LLM-Konfigurationen können ebenfalls berücksichtigt werden.`,primaryButton:"Kontakt",secondaryButton:"Technische Konsultation"}},GP={hero:{badges:["Unternehmen","RAG","Generierung von Sucherweiterungen"],title:"Vector / RAG Entwicklung",description:"Aufbau eines RAG-Systems zur Verknüpfung von internem Wissen mit LLM"},mission:{title:"Vector / RAG Development Services Überblick",paragraphs:["Wir werden ein RAG-System (Search Extension Generation) aufbauen, das es den Mitarbeitern ermöglicht, in natürlicher Sprache zu suchen und Fragen zu stellen, indem die im Unternehmen angesammelten Wissensbestände mit LLM verknüpft werden. Wir integrieren verschiedene Datenquellen wie Handbücher, Protokolle, technische Dokumente, FAQs, Slack-Verlauf, Confluence, SharePoint usw., um eine Umgebung zu schaffen, in der organisatorisches Wissen sofort genutzt werden kann.","Wir maximieren die Suchgenauigkeit und Antwortqualität, indem wir neben der einfachen Dokumentensuche die neuesten Technologien wie Hybrid Search (Vektorsuche + Schlagwortsuche), Reranking, hierarchisches Chunking, Metadatenfilterung usw. einsetzen. Wir geben die Quelle der Antwort eindeutig an, um sowohl die Halbierung der Suchergebnisse zu verhindern als auch die Zuverlässigkeit zu gewährleisten.","Wir unterstützen eine Vielzahl von Umgebungen, darunter AWS, GCP, Azure und On-Premise, und integrieren uns in die bestehende Authentifizierungsinfrastruktur und das Autoritätsmanagement. Wir bieten konsistente Unterstützung für die Aufrechterhaltung der Datenaktualität (differenzielle Updates und automatische Neuindizierung), die Analyse von Nutzungsprotokollen und die kontinuierliche Verbesserung der Genauigkeit."]},summary:{title:"RAG-Technologie-Trends",paragraphs:['Zwischen 2025 und 2026 tritt die RAG-Technologie in die Phase "Basisimplementierung bis Produktionsqualität" ein. Neben der einfachen Vektorsuche sind die Hybridsuche, die mehrere Suchmethoden kombiniert, das Reranking, das die Suchergebnisse neu ordnet, und die Query Decomposition, die komplexe Fragen zerlegt und verarbeitet, zu Standardkonfigurationen geworden.',"Auch die Chunking-Strategien haben sich weiterentwickelt: Hierarchisches Chunking, das die Struktur des Dokuments berücksichtigt, überlappende Chunks und multimodales Chunking, bei dem Tabellen und Abbildungen getrennt verarbeitet werden, tragen alle zur Verbesserung der Genauigkeit bei. Contextual Retrieval (das Hinzufügen von Kontextinformationen zu Chunks) hat sich ebenfalls als sehr effektiv erwiesen.","Für die Auswahl der Vektor-DB bieten neben verwalteten Die

324nsten wie Pinecone, Weaviate, Qdrant und Milvus auch PostgreSQL pgvector und lokales FAISS eine breite Palette von Optionen, um Ihre Anforderungen zu erfüllen. Auch Einbettungsmodelle sind verfügbar, darunter Text-Embedding-3-large, Cohere Embed v3 und mehrsprachige Modelle.",'Bei der Bereitstellung in Unternehmen sind die Verknüpfung von Autoritätsmanagement (ACL), die Maskierung von PII, Audit-Protokolle und ein mandantenfähiges Design wesentliche Anforderungen, und es besteht die Notwendigkeit, von einem "funktionierenden PoC" zu einem "produktionsreifen Produktionssystem" überzugehen.'],icons:[{label:`Hybride
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328pezifische Modelle"]},vectorDatabases:{title:"Vektordatenbanken",items:["Tannenzapfen (verwaltet)","Weaviate (Hybride Suche)","Qdrant (hohe Leistung)","Milvus (großer Maßstab)","Chroma (Leichtgewicht)","PostgreSQL pgvector","FAISS (lokal)"]},chunking:{title:"Chunking & Verarbeitung",items:["hierarchisches Chunking","Semantisches Chunking","Überlappende Chunks","Satzfenster","Kontextbezogenes Abrufen","Tabellen- und Diagrammbearbeitung","Multimodale Unterstützung"]},search:{title:"Suche und Abrufe",items:["Hybride Suche (Vektor + BM25)","Cohere Rerank","Encoder-übergreifendes Reranking","Erweiterung und Zerlegung von Abfragen","HyDE (Hypothetische Dokumentengenerierung)","Selbstabfrage-Abruf","Abruf mehrerer Abfragen"]}},updateNote:{title:"Wichtigste Aktualisierungspunkte für 2026",description:"Beim Einbettungsmodell wurden Text-Embedding-3-large und Cohere Embed v3 zum Unternehmensstandard, und die Leistung in japanischer Sprache verbesserte sich erheblich; Contextual Retrieval erwies sich als effektiv, und das Hinzufügen von Kontext zu Chunks wurde zur Standardmethode. Hybrid Search + Reranking ist zu einer obligatorischen Konfiguration für die Suche geworden, wodurch die Zahl der Konfigurationen, die sich auf die einfache Vektorsuche beschränken, verringert wurde, und Agentic RAG (Agenten wählen dynamisch Suchstrategien aus) wird in der Praxis eingesetzt. Auf der operativen Seite wird der Schwerpunkt auf die Einführung von Qualitätsmessungs- und Verbesserungszyklen unter Verwendung von RAG-Bewertungsrahmen (RAGAS, LangSmith) gelegt."}},cta:{title:"Kontakt",description:`Wir können Sie bei der Zusammenstellung der Anforderungen beraten.
329PoC und lokale LLM-Konfigurationen können ebenfalls berücksichtigt werden.`,primaryButton:"Kontakt",secondaryButton:"Technische Konsultation"}},OP={hero:{badges:["Voller Stapel","LLM-Integration","API-Entwurf"],title:"AI-Anwendungsentwicklung",description:"Entwurf und Entwicklung praktischer Anwendungen, die das LLM nutzen"},mission:{title:"Überblick über den AI-Anwendungsentwicklungsservice",paragraphs:["Wir bieten Design und Entwicklung von KI-Anwendungen, die durch die Analyse von Daten, die von externen APIs mit LLM erfasst werden, einen geschäftlichen Mehrwert schaffen, indem wir APIs mit verschiedenen Datenquellen wie CRM, ERP, SaaS, IoT-Sensoren und sozialen Medien verknüpfen und erfasste Daten in Echtzeit in LLM einspeisen. Wir ermöglichen fortschrittliche Datenanalysen, die mit herkömmlichen BI-Tools nicht möglich sind, wie z. B. die Erstellung von Analyseberichten mit natürlicher Sprache, die Erkennung von Anomalien, die Trendvorhersage und die automatische Klassifizierung.","Es handelt sich nicht nur um eine einfache API-Integration, sondern auch um die Integration mehrerer Datenquellen, die Datenbereinigung und die Konvertierung in ein Format, das für LLM durch strukturierte Verarbeitung leicht zu interpretieren ist. Promptes Engineering maximiert die Genauigkeit der Analyse und erzeugt eine auf die Geschäftsanforderungen spezialisierte Ausgabe. Wir entwickeln praktische Anwendungen, die Daten und KI kombinieren, z. B. Verkaufsdaten zur Vorhersage von Geschäftsverhandlungen, Kundensupportprotokolle zur Durchführung von VoC-Analysen und Sensordaten zur Bereitstellung von Wartungsempfehlungen für Geräte.","Wir unterstützen eine Vielzahl von Schnittstellen wie Webanwendungen, mobile Anwendungen, Slack/Teams-Integration und periodische Batch-Verarbeitung. Wir bieten konsistente Unterstützung von der Anforderungsdefinition über das API-Design und die Implementierung der LLM-Integration bis hin zum Produktionsbetrieb."]},summary:{title:"Trends in der Entwicklung von KI-Anwendungen",paragraphs:['Von 2025 bis 2026 tritt die Entwicklung von KI-Anwendungen in eine praktische Phase ein, die auf "API-Integration x LLM-Analyse" basiert. Die Analyse unstrukturierter Daten und die Generierung von Erkenntnissen in natürlicher Sprache, die von herkömmlichen BI-Tools und Dashboards nicht bewältigt werden können, werden allmählich in die Unternehmenssysteme integriert.',"Besonders hervorzuheben ist die Weiterentwicklung der LLM-Funktionalität für strukturierte Ausgabe, die eine strikte JSON-Schema-konforme Ausgabesteuerung ermöglicht und eine durchgängig automatisierte Pipeline Realität werden lässt, bei der über APIs erfasste Daten von LLM analysiert und die Ergebnisse direkt in nachfolgende Systeme eingespeist werden. Die mehrstufige Ausführung von Fu

329nction Calling ermöglicht es, die Datenerfassung, -integration und -analyse über mehrere APIs hinweg in einem einzigen Arbeitsablauf zu verarbeiten.","In der Datenverarbeitungsinfrastruktur hat sich Polars als Standard für die Hochgeschwindigkeitsverarbeitung herauskristallisiert, und in Kombination mit der Streaming-Verarbeitung ermöglicht es Echtzeitanalysen.","Bei Unternehmensimplementierungen haben sich die Anforderungen von einer einmaligen PoC-Entwicklung zu einem skalierbaren Design für den Produktionsbetrieb verlagert, was ein umfassendes Architekturdesign erfordert, das Kostenoptimierung, Fehlerbehandlung und Audit-Protokollunterstützung umfasst."],icons:[{label:`Agentisch
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$0(n,t,s){const o=ia(n)?n:Ot(n);return o.start(qd("",o,t,s)),o.animation}function qi(n,t,s,o={passive:!0}){return n.addEventListener(t,s,o),()=>n.removeEventListener(t,s)}const Q0=(n,t)=>n.depth-t.depth;class J0{constructor(){this.children=[],this.isDirty=!1}add(t){Id(this.children,t),this.isDirty=!0}remove(t){Md(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(Q0),this.isDirty=!1,this.children.forEach(t)}}function Z0(n,t){const s=la.now(),o=({timestamp:d})=>{const p=d-s;p>=t&&(En(o),n(p-t))};return De.setup(o,!0),()=>En(o)}function gr(n){return ia(n)?n.get():n}class X0{constructor(){this.members=[]}add(t){Id(this.members,t),t.scheduleRender()}remove(t){if(Md(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const s=this.members[this.members.length-1];s&&this.promote(s)}}relegate(t){const s=this.members.findIndex(d=>t===d);if(s===0)return!1;let o;for(let d=s;d>=0;d--){const p=this.members[d];if(p.isPresent!==!1){o=p;break}}return o?(this.promote(o),!0):!1}promote(t,s){const o=this.lead;if(t!==o&&(this.prevLead=o,this.lead=t,t.show(),o)){o.instance&&o.scheduleRender(),t.scheduleRender(),t.resumeFrom=o,s&&(t.resumeFrom.preserveOpacity=!0),o.snapshot&&(t.snapshot=o.snapshot,t.snapshot.latestValues=o.animationValues||o.latestValues),t.root&&t.root.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:d}=t.options;d===!1&&o.hide()}}exitAnimationComplete(){this.members.forEach(t=>{const{options:s,resumingFrom:o}=t;s.onExitComplete&&s.onExitComplete(),o&&o.options.onExitComplete&&o.options.onExitComplete()})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}const hr={hasAnimatedSinceResize:!0,hasEverUpdated:!1},Bl=["","X","Y","Z"],Y0=1e3;let eM=0;function Kl(n,t,s,o){const{latestValues:d}=t;d[n]&&(s[n]=d[n],t.setStaticValue(n,0),o&&(o[n]=0))}function vf(n){if(n.hasCheckedOptimisedAppear=!0,n.root===n)return;const{visualElement:t}=n.options;if(!t)return;const s=Bh(t);if(window.MotionHasOptimisedAnimation(s,"transform")){const{layout:d,layoutId:p}=n.options;window.MotionCancelOptimisedAnimation(s,"transform",De,!(d||p))}const{parent:o}=n;o&&!o.hasCheckedOptimisedAppear&&vf(o)}function Af({attachResizeListener:n,defaultParent:t,measureScroll:s,checkIsScrollRoot:o,resetTransform:d}){return class{constructor(c={},g=t?.()){this.id=eM++,this.animationId=0,this.animationCommitId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.isSharedProjectionDirty=!1,this.isTransformDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.hasCheckedOptimisedAppear=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.hasTreeAnimated=!1,this.layoutVersion=0,this.updateScheduled=!1,this.scheduleUpdate=()=>this.update(),this.projectionUpdateScheduled=!1,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{this.projectionUpdateScheduled=!1,this.nodes.forEach(tM),this.nodes.forEach(oM),this.nodes.forEach(lM),this.nodes.forEach(iM)},this.resolvedRelativeTargetAt=0,this.linkedParentVersion=0,this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.latestValues=c,this.root=g?g.root||g:this,this.path=g?[...g.path,g]:[],this.parent=g,this.depth=g?g.depth+1:0;for(let h=0;h<this.path.length;h++)this.path[h].shouldResetTransform=!0;this.root===this&&(this.nodes=new J0)}addEventListener(c,g){return this.eventHandlers.has(c)||this.eventHandlers.set(c,new Dd),this.eventHandlers.get(c).add(g)}notifyListeners(c,...g){const h=this.eventHandlers.get(c);h&&h.notify(...g)}hasListeners(c){return this.eventHandlers.has(c)}mount(c){if(this.instance)return;this.isSVG=Jh(c)&&!n0(c),this.instance=c;const{layoutId:g,layout:h,visualElement:b}=this.options;if(b&&!b.current&&b.mount(c),this.root.nodes.add(this),this.parent&&this.parent.children.add(this),this.root.hasTreeAnimated&&(h||g)&&(this.isLayoutDirty=!0),n){let f,y=0;const A=()=>this.root.updateBlockedByResize=!1;De.read(()=>{y=window.innerWidth}),n(c,()=>{const M=window.innerWidth;M!==y&&(y=M,this.root.updateBlockedByResize=!0,f&&f(),f=Z0(A,250),hr.hasAnimatedSinceResize&&(hr.hasAnimatedSinceResize=!1,this.nodes.forEach(lg)))})}g&&this.root.registerSharedNode(g,this),this.options.animate!==!1&&b&&(g||h)&&this.addEventListener("didUpdate",({delta:f,hasLayoutChanged:y,hasRelativeLayoutChanged:A,layout:M})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const P=this.options.transition||b.getDefaultTransition()||pM,{onLayoutAnimationStart:z,onLayoutAnimationComplete:C}=b.getProps(),E=!this.targetLayout||!kf(this.targetLayout,M),F=!y&&A;if(this.options.layoutRoot||this.resumeFrom||F||y&&(E||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0);const K={...Wd(P,"layout"),onPlay:z,onComplete:C};(b.shouldReduceMotion||this.options.layoutRoot)&&(K.delay=0,K.type=!1),this.startAnimation(K),this.setAnimationOrigin(f,F)}else y||lg(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=M})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const c=this.getStack();c&&c.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,this.eventHandlers.clear(),En(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(dM),this.animationId++)}getTransformTemplate(){const{visualElement:c}=this.options;return c&&c.getProps().transformTemplate}willUpdate(c=!0){if(this.root.hasTreeAnimated=!0,this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(window.MotionCancelOptimisedAnimation&&!this.hasCheckedOptimisedAppear&&vf(this),!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let f=0;f<this.path.length;f++){const y=this.path[f];y.shouldResetTransform=!0,y.updateScroll("snapshot"),y.options.layoutRoot&&y.willUpdate(!1)}const{layoutId:g,layout:h}=this.options;if(g===void 0&&!h)return;const b=this.getTransformTemplate();this.prevTransformTemplateValue=b?b(this.latestValues,""):void 0,this.updateSnapshot(),c&&this.notifyListeners("willUpdate")}update(){if(this.updateScheduled=!1,this.isUpdateBlocked()){this.unblockUpdate(),this.clearAllSnapshots(),this.nodes.forEach(rg);return}if(this.animationId<=this.animationCommitId){this.nodes.forEach(og);return}this.animationCommitId=this.animationId,this.isUpdating?(this.isUpdating=!1,this.nodes.forEach(rM),this.nodes.forEach(aM),this.nodes.forEach(nM)):this.nodes.forEach(og),this.clearAllSnapshots();const g=la.now();Ye.delta=$a(0,1e3/60,g-Ye.timestamp),Ye.timestamp=g,Ye.isProcessing=!0,Dl.update.process(Ye),Dl.preRender.process(Ye),Dl.render.process(Ye),Ye.isProcessing=!1}didUpdate(){this.updateScheduled||(this.updateScheduled=!0,$d.read(this.scheduleUpdate))}clearAllSnapshots(){this.nodes.forEach(sM),this.sharedNodes.forEach(uM)}scheduleUpdateProjection(){this.projectionUpdateScheduled||(this.projectionUpdateScheduled=!0,De.preRender(this.updateProjection,!1,!0))}scheduleCheckAfterUnmount(){De.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure(),this.snapshot&&!da(this.snapshot.measuredBox.x)&&!da(this.snapshot.measuredBox.y)&&(this.snapshot=void 0))}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let h=0;h<this.path.length;h++)this.path[h].updateScroll();const c=this.layout;this.layout=this.measure(!1),this.layoutVersion++,this.layoutCorrected=_e(),this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox);const{visualElement:g}=this.options;g&&g.notify("LayoutMeasure",this.layout.layoutBox,c?c.layoutBox:void 0)}updateScroll(c="measure"){let g=!!(this.options.layoutScroll&&this.instance);if(this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===c&&(g=!1),g&&this.instance){const h=o(this.instance);this.scroll={animationId:this.root.animationId,phase:c,isRoot:h,offset:s(this.instance),wasRoot:this.scroll?this.scroll.isRoot:h}}}resetTransform(){if(!d)return;const c=this.isLayoutDirty||this.shouldResetTransform||this.options.alwaysMeasureLayout,g=this.projectionDelta&&!ff(this.projectionDelta),h=this.getTransformTemplate(),b=h?h(this.latestValues,""):void 0,f=b!==this.prevTransformTemplateValue;c&&this.instance&&(g||Yn(this.latestValues)||f)&&(d(this.instance,b),this.shouldResetTransform=!1,this.scheduleRender())}measure(c=!0){const g=this.measurePageBox();let h=this.removeElementScroll(g);return c&&(h=this.removeTransform(h)),gM(h),{animationId:this.root.animationId,measuredBox:g,layoutBox:h,latestValues:{},source:this.id}}measurePageBox(){const{visualElement:c}=this.options;if(!c)return _e();const g=c.measureViewportBox();if(!(this.scroll?.wasRoot||this.path.some(hM))){const{scroll:b}=this.root;b&&(Tt(g.x,b.offset.x),Tt(g.y,b.offset.y))}return g}removeElementScroll(c){const g=_e();if(Ga(g,c),this.scroll?.wasRoot)return g;for(let h=0;h<this.path.length;h++){const b=this.path[h],{scroll:f,options:y}=b;b!==this.root&&f&&y.layoutScroll&&(f.wasRoot&&Ga(g,c),Tt(g.x,f.offset.x),Tt(g.y,f.offset.y))}return g}applyTransform(c,g=!1){const h=_e();Ga(h,c);for(let b=0;b<this.path.length;b++){const f=this.path[b];!g&&f.options.layoutScroll&&f.scroll&&f!==f.root&&Et(h,{x:-f.scroll.offset.x,y:-f.scroll.offset.y}),Yn(f.latestValues)&&Et(h,f.latestValues)}return Yn(this.latestValues)&&Et(h,this.latestValues),h}removeTransform(c){const g=_e();Ga(g,c);for(let h=0;h<this.path.length;h++){const b=this.path[h];if(!b.instance||!Yn(b.latestValues))continue;fd(b.latestValues)&&b.updateSnapshot();const f=_e(),y=b.measurePageBox();Ga(f,y),Zp(g,b.latestValues,b.snapshot?b.snapshot.layoutBox:void 0,f)}return Yn(this.latestValues)&&Zp(g,this.latestValues),g}setTargetDelta(c){this.targetDelta=c,this.root.scheduleUpdateProjection(),this.isProjectionDirty=!0}setOptions(c){this.options={...this.options,...c,crossfade:c.crossfade!==void 0?c.crossfade:!0}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}forceRelativeParentToResolveTarget(){this.relativeParent&&this.relativeParent.resolvedRelativeTargetAt!==Ye.timestamp&&this.relativeParent.resolveTargetDelta(!0)}resolveTargetDelta(c=!1){const g=this.getLead();this.isProjectionDirty||(this.isProjectionDirty=g.isProjectionDirty),this.isTransformDirty||(this.isTransformDirty=g.isTransformDirty),this.isSharedProjectionDirty||(this.isSharedProjectionDirty=g.isSharedProjectionDirty);const h=!!this.resumingFrom||this!==g;if(!(c||h&&this.isSharedProjectionDirty||this.isProjectionDirty||this.parent?.isProjectionDirty||this.attemptToResolveRelativeTarget||this.root.updateBlockedByResize))return;const{layout:f,layoutId:y}=this.options;if(!this.layout||!(f||y))return;this.resolvedRelativeTargetAt=Ye.timestamp;const A=this.getClosestProjectingParent();A&&this.linkedParentVersion!==A.layoutVersion&&!A.options.layoutRoot&&this.removeRelativeTarget(),!this.targetDelta&&!this.relativeTarget&&(A&&A.layout?this.createRelativeTarget(A,this.layout.layoutBox,A.layout.layoutBox):this.removeRelativeTarget()),!(!this.relativeTarget&&!this.targetDelta)&&(this.target||(this.target=_e(),this.targetWithTransforms=_e()),this.relativeTarget&&this.relativeTargetOrigin&&this.relativeParent&&this.relativeParent.target?(this.forceRelativeParentToResolveTarget(),O0(this.target,this.relativeTarget,this.relativeParent.target)):this.targetDelta?(this.resumingFrom?this.target=this.applyTransform(this.layout.layoutBox):Ga(this.target,this.layout.layoutBox),tf(this.target,this.targetDelta)):Ga(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget&&(this.attemptToResolveRelativeTarget=!1,A&&!!A.resumingFrom==!!this.resumingFrom&&!A.options.layoutScroll&&A.target&&this.animationProgress!==1?this.createRelativeTarget(A,this.target,A.target):this.relativeParent=this.relativeTarget=void 0))}getClosestProjectingParent(){if(!(!this.parent||fd(this.parent.latestValues)||nf(this.parent.latestValues)))return this.parent.isProjecting()?this.parent:this.parent.getClosestProjectingParent()}isProjecting(){return!!((this.relativeTarget||this.targetDelta||this.options.layoutRoot)&&this.layout)}createRelativeTarget(c,g,h){this.relativeParent=c,this.linkedParentVersion=c.layoutVersion,this.forceRelativeParentToResolveTarget(),this.relativeTarget=_e(),this.relativeTargetOrigin=_e(),xr(this.relativeTargetOrigin,g,h),Ga(this.relativeTarget,this.relativeTargetOrigin)}removeRelativeTarget(){this.relativeParent=this.relativeTarget=void 0}calcProjection(){const c=this.getLead(),g=!!this.resumingFrom||this!==c;let h=!0;if((this.isProjectionDirty||this.parent?.isProjectionDirty)&&(h=!1),g&&(this.isSharedProjectionDirty||this.isTransformDirty)&&(h=!1),this.resolvedRelativeTargetAt===Ye.timestamp&&(h=!1),h)return;const{layout:b,layoutId:f}=this.options;if(this.isTreeAnimating=!!(this.parent&&this.parent.isTreeAnimating||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!(b||f))return;Ga(this.layoutCorrected,this.layout.layoutBox);const y=this.treeScale.x,A=this.treeScale.y;p0(this.layoutCorrected,this.treeScale,this.path,g),c.layout&&!c.target&&(this.treeScale.x!==1||this.treeScale.y!==1)&&(c.target=c.layout.layoutBox,c.targetWithTransforms=_e());const{target:M}=c;if(!M){this.prevProjectionDelta&&(this.createProjectionDeltas(),this.scheduleRender());return}!this.projectionDelta||!this.prevProjectionDelta?this.createProjectionDeltas():(Up(this.prevProjectionDelta.x,this.projectionDelta.x),Up(this.prevProjectionDelta.y,this.projectionDelta.y)),Ni(this.projectionDelta,this.layoutCorrected,M,this.latestValues),(this.treeScale.x!==y||this.treeScale.y!==A||!ng(this.projectionDelta.x,this.prevProjectionDelta.x)||!ng(this.projectionDelta.y,this.prevProjectionDelta.y))&&(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",M))}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(c=!0){if(this.options.visualElement?.scheduleRender(),c){const g=this.getStack();g&&g.scheduleRender()}this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}createProjectionDeltas(){this.prevProjectionDelta=zt(),this.projectionDelta=zt(),this.projectionDeltaWithTransform=zt()}setAnimationOrigin(c,g=!1){const h=this.snapshot,b=h?h.latestValues:{},f={...this.latestValues},y=zt();(!this.relativeParent||!this.relativeParent.options.layoutRoot)&&(this.relativeTarget=this.relativeTargetOrigin=void 0),this.attemptToResolveRelativeTarget=!g;const A=_e(),M=h?h.source:void 0,P=this.layout?this.layout.source:void 0,z=M!==P,C=this.getStack(),E=!C||C.members.length<=1,F=!!(z&&!E&&this.options.crossfade===!0&&!this.path.some(mM));this.animationProgress=0;let K;this.mixTargetDelta=U=>{const W=U/1e3;dg(y.x,c.x,W),dg(y.y,c.y,W),this.setTargetDelta(y),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(xr(A,this.layout.layoutBox,this.relativeParent.layout.layoutBox),cM(this.relativeTarget,this.relativeTargetOrigin,A,W),K&&F0(this.relativeTarget,K)&&(this.isProjectionDirty=!1),K||(K=_e()),Ga(K,this.relativeTarget)),z&&(this.animationValues=f,U0(f,b,this.latestValues,W,F,E)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=W},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(c){this.notifyListeners("animationStart"),this.currentAnimation?.stop(),this.resumingFrom?.currentAnimation?.stop(),this.pendingAnimation&&(En(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=De.update(()=>{hr.hasAnimatedSinceResize=!0,this.motionValue||(this.motionValue=Ot(0)),this.currentAnimation=$0(this.motionValue,[0,1e3],{...c,velocity:0,isSync:!0,onUpdate:g=>{this.mixTargetDelta(g),c.onUpdate&&c.onUpdate(g)},onStop:()=>{},onComplete:()=>{c.onComplete&&c.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const c=this.getStack();c&&c.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(Y0),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const c=this.getLead();let{targetWithTransforms:g,target:h,layout:b,latestValues:f}=c;if(!(!g||!h||!b)){if(this!==c&&this.layout&&b&&xf(this.options.animationType,this.layout.layoutBox,b.layoutBox)){h=this.target||_e();const y=da(this.layout.layoutBox.x);h.x.min=c.target.x.min,h.x.max=h.x.min+y;const A=da(this.layout.layoutBox.y);h.y.min=c.target.y.min,h.y.max=h.y.min+A}Ga(g,h),Et(g,f),Ni(this.projectionDeltaWithTransform,this.layoutCorrected,g,f)}}registerSharedNode(c,g){this.sharedNodes.has(c)||this.sharedNodes.set(c,new X0),this.sharedNodes.get(c).add(g);const b=g.options.initialPromotionConfig;g.promote({transition:b?b.transition:void 0,preserveFollowOpacity:b&&b.shouldPreserveFollowOpacity?b.shouldPreserveFollowOpacity(g):void 0})}isLead(){const c=this.getStack();return c?c.lead===this:!0}getLead(){const{layoutId:c}=this.options;return c?this.getStack()?.lead||this:this}getPrevLead(){const{layoutId:c}=this.options;return c?this.getStack()?.prevLead:void 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t=this.visualElement.getProps(),{drag:s=!1,dragDirectionLock:o=!1,dragPropagation:d=!1,dragConstraints:p=!1,dragElastic:c=yd,dragMomentum:g=!0}=t;return{...t,drag:s,dragDirectionLock:o,dragPropagation:d,dragConstraints:p,dragElastic:c,dragMomentum:g}}}function ur(n,t,s){return(t===!0||t===n)&&(s===null||s===n)}function rj(n,t=10){let s=null;return Math.abs(n.y)>t?s="y":Math.abs(n.x)>t&&(s="x"),s}class oj extends Nn{constructor(t){super(t),this.removeGroupControls=Ma,this.removeListeners=Ma,this.controls=new sj(t)}mount(){const{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||Ma}update(){const{dragControls:t}=this.node.getProps(),{dragControls:s}=this.node.prevProps||{};t!==s&&(this.removeGroupControls(),t&&(this.removeGroupControls=t.subscribe(this.controls)))}unmount(){this.removeGroupControls(),this.removeListeners(),this.controls.endPanSession()}}const 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Bus-only and train-only filters, nearby station and stop search, and a multilingual interface help users choose routes for everyday journeys and travel.",zh:"根据出发地和目的地搜索公交与铁路组合路线，并按到达时间、行程时长和换乘次数比较方案。支持仅公交、仅铁路筛选、附近车站搜索及多语言界面，帮助用户为日常出行和旅行选择合适的路线。",ko:"출발지와 목적지를 기준으로 버스와 철도를 조합한 경로를 검색하고 도착 시각, 소요 시간, 환승 횟수로 후보를 비교하는 시스템입니다. 버스 전용·철도 전용 필터, 주변 역과 정류장 검색, 다국어 화면을 제공하여 일상 이동과 여행에 맞는 경로 선택을 돕습니다.",es:"Sistema que busca conexiones de autobús y tren entre un origen y un destino y compara las opciones por hora de llegada, duración y transbordos. Los filtros de solo autobús o tren, la búsqueda de estaciones y paradas cercanas y una interfaz multilingüe facilitan la elección de rutas para desplazamientos diarios y viajes.",pt:"Sistema que busca conexões de ônibus e trem entre origem e destino e compara opções por horário de chegada, duração e número de baldeações. Filtros de apenas ônibus ou trem, busca de estações e paradas próximas e uma interface multilíngue ajudam a escolher rotas para o dia a dia e viagens.",de:"Das System sucht Bus- und Bahnverbindungen zwischen Start und Ziel und vergleicht Optionen nach Ankunftszeit, Reisedauer und Umstiegen. Filter für reine Bus- oder Bahnverbindungen, die Suche nac

334h nahegelegenen Stationen und Haltestellen sowie eine mehrsprachige Oberfläche unterstützen die Routenwahl im Alltag und auf Reisen.",ms:"Sistem yang mencari gabungan laluan bas dan kereta api antara tempat bertolak dan destinasi serta membandingkan pilihan mengikut waktu ketibaan, tempoh perjalanan dan bilangan pertukaran. Penapis bas sahaja atau kereta api sahaja, carian stesen dan hentian berdekatan serta antara muka berbilang bahasa membantu pemilihan laluan untuk perjalanan harian dan percutian.",id:"Sistem yang mencari kombinasi rute bus dan kereta antara titik keberangkatan dan tujuan serta membandingkan pilihan berdasarkan waktu tiba, durasi perjalanan, dan jumlah perpindahan. Filter khusus bus atau kereta, pencarian stasiun dan halte terdekat, serta antarmuka multibahasa membantu memilih rute untuk perjalanan sehari-hari maupun wisata.",th:"ระบบค้นหาเส้นทางที่ผสมผสานรถโดยสารและรถไฟจากจุดเริ่มต้นถึงปลายทาง พร้อมเปรียบเทียบเวลาเดินทาง เวลาถึง และจำนวนครั้งที่เปลี่ยนสาย รองรับตัวกรองเฉพาะรถโดยสารหรือรถไฟ การค้นหาสถานีและป้ายใกล้เคียง และหน้าจอหลายภาษา เพื่อช่วยเลือกเส้นทางสำหรับการเดินทางประจำวันและการท่องเที่ยว"},techStack:{ja:["公共交通経路検索","バス・鉄道連携","経路比較","多言語UI"],en:["Public Transit Routing","Bus & Rail Integration","Route Comparison","Multilingual UI"],zh:["公共交通路线搜索","公交与铁路整合","路线比较","多语言界面"],ko:["대중교통 경로 검색","버스·철도 연계","경로 비교","다국어 UI"],es:["Rutas de Transporte Público","Integración de Autobús y Tren","Comparación de Rutas","Interfaz Multilingüe"],pt:["Rotas de Transporte Público","Integração de Ônibus e Trem","Comparação de Rotas","Interface Multilíngue"],de:["ÖPNV-Routenplanung","Bus- und Bahnanbindung","Routenvergleich","Mehrsprachige Oberfläche"],ms:["Laluan Pengangkutan Awam","Integrasi Bas dan Kereta Api","Perbandingan Laluan","Antara Muka Berbilang Bahasa"],id:["Rute Transportasi Umum","Integrasi Bus dan Kereta","Perbandingan Rute","Antarmuka Multibahasa"],th:["ค้นหาเส้นทางขนส่งสาธารณะ","เชื่อมต่อรถโดยสารและรถไฟ","เปรียบเทียบเส้นทาง","หน้าจอหลายภาษา"]},projectUrl:"https://trf.mychats.info/",imageSrc:"/images/traffic-route-ai.webp",videoSrc:"/videos/traffic-route-ai.mp4"},{id:"multilingual-assistant",number:"",title:{ja:"AI多言語アシスタント",en:"AI Multilingual Assistant",zh:"AI多语言助手",ko:"AI 다국어 어시스턴트",es:"Asistente Multilingue con IA",pt:"Assistente Multilíngue com IA",th:"ผู้ช่วย AI หลายภาษา",ms:"Pembantu AI Pelbagai Bahasa",id:"Asisten AI Multibahasa",de:"KI-Mehrsprachiger Assistent"},description:{ja:"複数言語での問い合わせに対応するインテリジェントなチャットアシスタント。1,573件のベクトルインデックスを構築し、条件分岐、NGワードフィルタリング、キーワードトリガーによる精密な回答制御を実装しました。日本語・英語・中国語など多言語でのユーザー質問に対し、言語横断的なセマンティック検索により一貫した高精度な回答を生成します。",en:"An intelligent chat assistant supporting multilingual inquiries. Built a vector index of 1,573 items with conditional branching, NG word filtering, and keyword-triggered precision response control. Generates consistent, high-accuracy answers to user questions in Japanese, English, Chinese, and other languages through cross-lingual semantic search.",zh:"支持多语言查询的智能聊天助手。构建了1,573个条目的向量索引，实现了条件分支、敏感词过滤和关键词触发的精准回答控制。通过跨语言语义搜索，为日语、英语、中文等多语言用户问题ç”

334Ÿæˆä¸€è‡´çš„高精度回答。",ko:"다국어 문의를 지원하는 지능형 채팅 어시스턴트. ì¡°ê±´ 분기, 금지어 필터링, 키워드 트리거 정밀 응답 제어를 갖춘 1,573개 항목의 벡터 인덱스를 구축했습니다. 일본어, 영어, 중국어 등 다국어 사용자 질문에 대해 교차 언어 의미 검색을 통해 일관되고 높은 정확도의 답변을 생성합니다.",es:"Un asistente de chat inteligente que admite consultas multilingües. Se construyó un índice vectorial de 1,573 elementos con ramificación condicional, filtrado de palabras prohibidas y control de respuesta precisa activado por palabras clave. Genera respuestas consistentes y de alta precisión para preguntas de usuarios en japonés, inglés, chino y otros idiomas mediante búsqueda semántica multilingüe.",pt:"Um assistente de chat inteligente que suporta consultas multilíngues. Construiu um índice vetorial de 1.573 itens com ramificação condicional, filtragem de palavras proibidas e controle de resposta de precisão acionado por palavras-chave. Gera respostas consistentes e de alta precisão para perguntas de usuários em japonês, inglês, chinês e outros idiomas por meio de pesquisa semântica multilíngue.",th:"ผู้ช่วยแชทอัจฉริยะที่รองรับการสอบถามหลายภาษา สร้างดัชนีเวกเตอร์ 1,573 รายการพร้อมการแยกสาขาตามเงื่อนไข การกรองคำต้องห้าม และการควบคุมการตอบสนองที่แม่นยำโดยคำสำคัญ สร้างคำตอบที่สม่ำเสมอและมีความแม่นยำสูงสำหรับคำถามของผู้ใช้ในภาษาญี่ปุ่น อังกฤษ à¸

334ˆà¸µà¸™ และภาษาอื่นๆ ผ่านการค้นหาเชิงความหมายข้ามภาษา",ms:"Pembantu sembang pintar yang menyokong pertanyaan pelbagai bahasa. Membina indeks vektor 1,573 item dengan percabangan bersyarat, penapisan perkataan terlarang, dan kawalan respons tepat yang dicetuskan kata kunci. Menjana jawapan yang konsisten dan tepat tinggi untuk soalan pengguna dalam bahasa Jepun, Inggeris, Cina dan bahasa lain melalui carian semantik silang bahasa.",id:"Asisten obrolan cerdas yang mendukung pertanyaan multibahasa. Membangun indeks vektor dari 1.573 item dengan percabangan kondisional, penyaringan kata terlarang, dan kontrol respons presisi yang dipicu kata kunci. Menghasilkan jawaban yang konsisten dan akurat tinggi untuk pertanyaan pengguna dalam bahasa Jepang, Inggris, Cina, dan bahasa lain melalui pencarian semantik lintas bahasa.",de:"Ein intelligenter Chat-Assistent, der mehrsprachige Anfragen unterstützt. Erstellung eines Vektorindexes mit 1.573 Elementen mit bedingter Verzweigung, Filterung verbotener Wörter und schlüsselwortgesteuerter Präzisionsantwortkontrolle. Generiert konsistente, hochpräzise Antworten auf Benutzerfragen in Japanisch, Englisch, Chinesisch und anderen Sprachen durch sprachübergreifende semantische Suche."},techStack:{ja:["RAG","Vector Search","多言語Embedding","LLM","条件分岐ロジック"],en:["RAG","Vector Search","Multilingual Embedding","LLM","Conditional Logic"],zh:["RAG","向量搜索","多语言嵌入","LLM","条件逻辑"],ko:["RAG","벡터 검색","다국어 임베딩","LLM","ì¡°ê±´ 로직"],es:["RAG","Búsqueda Vectorial","Embedding Multilingüe","LLM","Lógica Condicional"],pt:["RAG","Busca Vetorial","Embedding Multilíngue","LLM","Lógica Condicional"],th:["RAG","Vector Search","Multilingual Embedding","LLM","Conditional Logic"],ms:["RAG","Carian Vektor","Embedding Berbilang Bahasa","LLM","Logik Bersyarat"],id:["RAG","Pencarian Vektor","Embedding Multibahasa","LLM","Logika Kondisional"],de:["RAG","Vektorsuche","Mehrsprachiges Embedding","LLM","Bedingte Logik"]},projectUrl:"https://app.lllm.tech/",imageSrc:"/images/multilingual-assistant-ui.png"},{id:"weather-forecast",number:"",title:{ja:"気象APIによるAI気象予測",en:"AI Weather Forecasting with Weather APIs",zh:"基于气象API的AI天气预测",ko:"기상 API를 활용한 AI 날씨 예측",es:"Pronóstico del Tiempo con IA mediante APIs Meteorológicas",pt:"Previsão do Tempo com IA usando APIs Meteorológicas",th:"การพยากรณ์อากาศด้วย AI ผ่าน API สภาพอากาศ",ms:"Ramalan Cuaca AI dengan API Cuaca",id:"Prakiraan Cuaca AI dengan API Cuaca",de:"KI-Wettervorhersage mit Wetter-APIs"},description:{ja:"過去の気象データを学習し、直近の気象状況をリアルタイム予測するAIシステムを開発。気象庁API、OpenWeatherMap等から取得した過去データをLLMで解析し、温度、降水確率、風速などの短期予測を生成します。時系列データのパターン認識とRAGによる過去類似パターン検索を組み合わせ、従来の統計モデルを補完する高精度な予測を実現しました。",en:"Developed an AI system that learns from historical weather data to predict real-time weather conditions. Analyzes past data from Japan Meteorological Agency API and OpenWeatherMap using LLM to generate short-term forecasts for temperature, precipitation probability, and wind speed. Achieved high-accuracy predictions by combining time-series pattern recognition with RAG-based retrieval of similar historical patterns, complementing traditional statistical models.",zh:"开发了一个从历史气象数据中学习的AI系统，用于实时预测天气状况。使用LLM分析来自日本气象厅API和OpenWeatherMap的历史数据，生成温度、降水概率和风速的短期预测。通过结合时间序列模式识别和基于RAG的历史相似模式检索，实现了高精度预测，补充了传统统计模型。",ko:"과거 기상 데이터를 학습하여 실시간 날씨 상황을 예측하는 AI 시스템을 개발했습니다. 일본 기상청 API와 OpenWeatherMap의 과거 데이터를 LLM으로 분석하여 기온, 강수 확률, 풍속의 단기 예보를 생성합니다. 시계열 패턴 인식과 RAG 기반 유사 과거 패턴 검색을 결합하여 기존 통계 모델을 보완하는 고정밀 예측을 실현했습니다.",es:"Desarrollamos un sistema de IA que aprende de datos meteorológicos históricos para predecir condiciones climáticas en tiempo real. Analiza datos pasados de la API de la Agencia Meteorológica de Japón y OpenWeatherMap usando LLM para generar pronósticos a corto plazo de temperatura, probabilidad de precipitación y velocidad del viento. Logró predicciones de alta precisión combinando reconocimiento de patrones de series temporales con recuperación basada en RAG de patrones históricos similares, complementando los modelos estadísticos tradicionales.",pt:"Desenvolvemos um sistema de IA que aprende com dados meteorológicos históricos para prever condições climáticas em tempo real. Analisa dados passados da API da Agência Meteorológica do Japão e OpenWeatherMap usando LLM para gerar previsões de curto prazo de temperatura, probabilidade de precipitação e velocidade do vento. Alcançou previsões de alta precisão combinando reconhecimento de padrões de séries temporais com recuperação baseada em RAG de padrões históricos similares, complementando modelos estatísticos tradicionais.",th:"พัฒนาระบบ AI ที่เรียนรู้จากข้อมูลสภาพอากาศในอดีตเพื่อคาดการณ์สภาพอากาศแบบเรียลไทม์ วิเคราะห์ข้อมูลในอดีตจาก API ของกรมอุตุนิยมวิทยาญี่ปุ่นและ OpenWeatherMap โดยใช้ LLM เพื่อสร้างการพยากรณ์ระยะสั้นสำหรับอุณหภูมิ ความน่าจะเป็นของฝน และความเร็วลม บรรลุการคาดการณ์ที่แม่นยำสูงโดยผสมผสานการจดจำรูปแบบอนุกรมเวลากับการดึงข้อมูลรูปแบบในอดีตที่คล้ายกันโดยใช้ RAG ซึ่งเสริมโมเดลทางสถิติแบบดั้งเดิม",ms:"Membangunkan sistem AI yang belajar dari data cuaca sejarah untuk meramalkan keadaan cuaca masa nyata. Menganalisis data lepas dari API Agensi Meteorologi Jepun dan OpenWeatherMap menggunakan LLM untuk menjana ramalan 

334jangka pendek suhu, kebarangkalian hujan, dan kelajuan angin. Mencapai ramalan ketepatan tinggi dengan menggabungkan pengecaman corak siri masa dengan pengambilan corak sejarah serupa berasaskan RAG, melengkapkan model statistik tradisional.",id:"Mengembangkan sistem AI yang belajar dari data cuaca historis untuk memprediksi kondisi cuaca secara real-time. Menganalisis data masa lalu dari API Badan Meteorologi Jepang dan OpenWeatherMap menggunakan LLM untuk menghasilkan prakiraan jangka pendek suhu, probabilitas hujan, dan kecepatan angin. Mencapai prediksi akurasi tinggi dengan menggabungkan pengenalan pola deret waktu dengan pengambilan pola historis serupa berbasis RAG, melengkapi model statistik tradisional.",de:"Entwicklung eines KI-Systems, das aus historischen Wetterdaten lernt, um Wetterbedingungen in Echtzeit vorherzusagen. Analysiert vergangene Daten von der japanischen Meteorologiebehörde-API und OpenWeatherMap mit LLM, um kurzfristige Vorhersagen für Temperatur, Niederschlagswahrscheinlichkeit und Windgeschwindigkeit zu erstellen. Erreichte hochpräzise Vorhersagen durch Kombination von Zeitreihen-Mustererkennung mit RAG-basiertem Abruf ähnlicher historischer Muster, die traditionelle statistische Modelle ergänzen."},techStack:{ja:["気象API連携","LLM","RAG","時系列データ解析","リアルタイム処理"],en:["Weather API Integration","LLM","RAG","Time-Series Analysis","Real-Time Processing"],zh:["气象API集成","LLM","RAG","时间序列分析","实时处理"],ko:["기상 API 연동","LLM","RAG","시계열 데이터 분석","실시간 처리"],es:["Integración de API Meteorológica","LLM","RAG","Análisis de Series Temporales","Procesamiento en Tiempo Real"],pt:["Integração de API Meteorológica","LLM","RAG","Análise de Séries Temporais","Processamento em Tempo Real"],th:["การเชื่อมต่อ API สภาพอากาศ","LLM","RAG","การวิเคราะห์อนุกรมเวลา","การประมวลผลแบบเรียลไทม์"],ms:["Integrasi API Cuaca","LLM","RAG","Analisis Siri Masa","Pemprosesan Masa Nyata"],id:["Integrasi API Cuaca","LLM","RAG","Analisis Deret Waktu","Pemrosesan Real-Time"],de:["Wetter-API-Integration","LLM","RAG","Zeitreihenanalyse","Echtzeitverarbeitung"]},projectUrl:"https://tenki.info/",imageSrc:"/images/weather-forecast.jpg"},{id:"local-llm",number:"",title:{ja:"社内サーバー／オンプレ ローカルLLM",en:"In-house Server / On-Prem Local LLM",zh:"内部服务器 / On-Prem Local LLM",ko:"사내 서버 / On-Prem Local LLM",es:"Servidor interno / On-Prem Local LLM",pt:"Servidor interno / On-Prem Local LLM",th:"เซิร์ฟเวอร์ภายใน / On-Prem Local LLM",ms:"Pelayan Dalaman / On-Prem Local LLM",id:"Server Internal / On-Prem Local LLM",de:"Interner Server / On-Prem Local LLM"},description:{ja:"お客様の業務に合わせたAIを、社内ですぐ使える状態でお届けします。Open WebUIを共通の入り口に据え、Ollama／MLXによる高速推論レーンと、UIDごとに完全に分離されたデータ領域を構築。社内ドキュメントは英語埋め込み × 日本語クエリ自動翻訳のRAGに取り込み、ニュース要約・資料整理・ルーチン業務などの業務スキルを安全に並走させます。スキル実行系はOpenClawコンテナとして隔離し、Cloudflareエッジ＋ローカルNginxの三層で守る設計。モデル選定からスキル設計、運用ポリシーまで一括で支援し、御社専用のAIを「導入したその日から使える」形で立ち上げます。",en:"We deliver AI tailored to your business operations, ready to use within your company, with Open WebUI as a common entry point, fast inference lanes with Ollama/MLX, and completely separated data domains for each UID. In-house documents are captured in a RAG with English embedding and automatic Japanese query translation, and business skills such as news summarization, document organization, and routine tasks are safely executed side by side. The skill execution system is isolated as an OpenClaw container and protected by three layers of Cloudflare edge and local Nginx. We provide comprehensive support for model selection, skill design, and operational policies, and start up your company's dedicated AI in a form that can be used from the day it is deployed.",zh:"我们提供为您的业务运营量身定制的人工智能，以开放式 WebUI 作为通用入口点，使用 Ollama/MLX 快速推理通道，并为每个 UID 提供完全分离的数据域，可在贵公司内部随时使用。在 RAG 中捕获内部文档，并进行英语嵌入和日语自动查询翻译，同时安全地执行新闻摘要、文档组织和日常任务等业务技能。技能执行系统作为 OpenClaw 容器进行隔离，并受到三层 Cloudflare 边缘和本地 Nginx 的保护。我们为模型选择、技能设计和运行策略提供全面支持，并以部署当天即可使用的形式启动贵公司的专用人工智能。",ko:"개방형 WebUI를 공통 진입점으로 삼아 회사 내에서 바로 사용할 수 있는 비즈니스 운영 맞춤형 AI를 제공하며, Ollama/MLX를 통한 빠른 추론 레인, 각 UID에 대해 완전히 분리된 데이터 도메인을 제공합니다. 사내 문서는 영어 임베딩과 일본어 쿼리 자동 번역 기능을 갖춘 RAG로 캡처되며, 뉴스 요약, 문서 정리, 일상 업무와 같은 비즈니스 스킬이 안전하게 나란히 실행됩니다. 스킬 실행 시스템은 OpenClaw 컨테이너로 격리되어 있으며 Cloudflare 에지 및 로컬 Nginx의 3계층으로 보호됩니다. 모델 선택, 스킬 설계, 운영 정책 등을 종합적으로 지원하며, 배포한 날부터 바로 사용할 수 있는 형태로 귀사의 전용 AI를 가동합니다.",es:"Proporcionamos IA adaptada a sus operaciones empresariales, lista para usar en su empresa, con Open WebUI como punto de entrada común, vías de inferencia rápidas con Ollama/MLX y dominios de datos completamente separados para cada UID. Los documentos internos se capturan en un RAG con incrustación en inglés y traducción automática de consultas al japonés, y las habilidades empresariales como el resumen de noticias, la organización de documentos y las tareas rutinarias se ejecutan de forma segura codo con codo. El sistema de ejecución de habilidades está aislado como un contenedor OpenClaw y protegido por tres capas de Cloudflare edge y Nginx local. Proporcionamos asistencia completa para la selección de modelos, el diseño de habilidades y las políticas operativas, y ponemos en marcha la IA dedicada de su empresa de forma que pueda utilizarse desde el mismo día de su despliegue.",pt:"Fornecemos IA adaptada às suas operações comerciais, pronta para uso em sua empresa, com Open WebUI como ponto de entrada comum, pistas de inferência rápidas com Ollama/MLX e domínios de dados completamente separados para cada UID. Os documentos internos são capturados em um RAG com incorporação em inglês e tradução automática de consultas em japonês, e as habilidades de negócios, como resumo de notícias, organização de documentos e tarefas de rotina, são executadas com segurança lado a lado. O sistema de execução de habilidades é isolado como um contêiner OpenClaw e protegido por três camadas de borda Cloudflare e Nginx local. Fornecemos suporte abrangente para seleção de modelos, design de habilidades e políticas operacionais, e iniciamos a IA dedicada da sua empresa em um formato que pode ser usado desde o dia em que é implantado.",th:"เราส่งมอบ AI ที่ปรับแต่งให้เหมาะกับกระบวนการทำงานของธุรกิจคุณ พร้อมใช้งานภายในบริษัทของคุณ ด้วย Open WebUI เป็นจุดเข้าใช้งานทั่วไป ช่องทางประมวลผลที่รวดเร็วด้วย Ollama/MLX และโดเมนข้อมูลที่แยกออกจากกันอย่างสมบูรณ์สำหรับแต่ละ UID เอกสารภายในองค์กรจะถูกบันทึกไว้ใน RAG พร้อมก

334ารฝังภาษาอังกฤษและการแปลคำถามภาษาญี่ปุ่นโดยอัตโนมัติ และทักษะทางธุรกิจ เช่น การสรุปข่าว การจัดระเบียบเอกสาร และการทำงานประจำ จะถูกดำเนินการอย่างปลอดภัยควบคู่กันไประบบการใช้งานทักษะถูกแยกออกเป็นคอนเทนเนอร์ OpenClaw และได้รับการปกป้องโดยชั้นป้องกันสามชั้นจาก Cloudflare edge และ Nginx ท้องถิ่น เราให้การสนับสนุนอย่างครอบคลุมสำหรับการเลือกแบบจำลอง, การออกแบบทักษะ, และนโยบายการดำเนินงาน และเริ่มต้นระบบ AI เฉพาะของบริษัทคุณในรูปแบบที่สามารถใช้งานได้ตั้งแต่วันที่ติดตั้ง",ms:"Kami menyediakan AI yang disesuaikan dengan operasi perniagaan anda, sedia untuk digunakan dalam syarikat anda, dengan Open WebUI sebagai titik kemasukan biasa, laluan inferens pantas dengan Ollama/MLX, dan domain data yang dipisahkan sepenuhnya untuk setiap UID. Dokumen dalaman dirakam dalam RAG dengan pemadatan Bahasa Inggeris dan terjemahan pertanyaan Jepun secara automatik, dan kemahiran perniagaan seperti ringkasan berita, pengaturan dokumen, dan tugas rutin dijalankan dengan selamat secara serentak. Sistem pelaksanaan kemahiran diasingkan sebagai kontena OpenClaw dan dilindungi oleh tiga lapisan edge Cloudflare serta Nginx tempatan. Kami menyediakan sokongan menyeluruh untuk pemilihan model, reka bentuk kemahiran, dan dasar operasi, dan memulakan AI khusus syarikat anda dalam bentuk yang boleh digunakan sejak hari ia dikerahkan.",id:"Kami memberikan AI yang disesuaikan dengan operasi bisnis Anda, siap digunakan di dalam perusahaan Anda, dengan Open WebUI sebagai titik masuk umum, jalur inferensi cepat dengan Ollama/MLX, dan domain data yang sepenuhnya terpisah untuk setiap UID. Dokumen internal ditangkap dalam RAG dengan penyematan bahasa Inggris dan terjemahan kueri bahasa Jepang otomatis, dan keterampilan bisnis seperti ringkasan berita, organisasi dokumen, dan tugas rutin dijalankan dengan aman secara berdampingan. Sistem eksekusi keterampilan diisolasi sebagai wadah OpenClaw dan dilindungi oleh tiga lapisan Cloudflare edge dan Nginx lokal. Kami menyediakan dukungan komprehensif untuk pemilihan model, desain keterampilan, dan kebijakan operasional, dan memulai AI khusus perusahaan Anda dalam bentuk yang dapat digunakan sejak hari pertama digunakan.",de:"Wir liefern auf Ihre Geschäftsabläufe zugeschnittene KI, die in Ihrem Unternehmen sofort einsatzbereit ist, mit Open WebUI als gemeinsamen Einstiegspunkt, schnellen Inferenzspuren mit Ollama/MLX und vollständig getrennten Datendomänen für jede UID. Interne Dokumente werden in einem RAG mit englischer Einbettung und automatischer japanischer Abfrageübersetzung erfasst, und Business Skills wie Nachrichtenzusammenfassung, Dokumentenorganisation und Routineaufgaben werden sicher nebeneinander ausgeführt. Das Skill-Ausführungssystem ist als OpenClaw-Container isoliert und durch drei Schichten von Cloudflare Edge und lokalem Nginx geschützt. Wir bieten umfassende Unterstützung bei der Modellauswahl, dem Skill-Design und den Betriebsrichtlinien und machen die dedizierte KI Ihres Unternehmens vom ersten Tag an einsatzfähig."},techStack:{ja:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],en:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],zh:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],ko:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],es:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],pt:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],th:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],ms:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],id:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"],de:["CUDA","MLX","Anemll","OpenClaw","Ollama","LangChain","LlamaIndex","Docker","Kubernetes"]},projectUrl:"https://lllm.tech/",imageSrc:"/images/local-llm.jpg"},{id:"orbital-prediction",number:"",title:{ja:"惑星間軌道到達予測（AI）",en:"Interplanetary Transfer Window Prediction",zh:"行星际转移窗口预测",ko:"행성간 전이 윈도우 예측",es:"Predicción de Ventanas de Transferencia Interplanetaria",pt:"Previsão de Janelas de Transferência Interplanetária",th:"การคาดการณ์หน้าต่างการถ่ายโอนระหว่างดาวเคราะห์",ms:"Ramalan Tetingkap Pemindahan Antara Planet",id:"Prediksi Jendela Transfer Antarplanet",de:"Interplanetare Transferfenster-Vorhersage"},description:{ja:"NASA APIから惑星位置データ、軌道要素、重力パラメータを取得し、惑星間移動の最適な出発ポイントを解析するシステムを開発ã€

334‚ホーマン遷移軌道、重力アシストを考慮した軌道計算をAPIとPythonで算定し、打ち上げウィンドウ、所要時間、燃料効率を表示します。教育機関・宇宙関連企業向けのシミュレーションツールとして活用されています。※AI機能を現在外しています、APIとPythonで計算中",en:"Developed a system that retrieves planetary position data, orbital elements, and gravity parameters from NASA APIs to analyze optimal departure points for interplanetary travel. Calculates trajectories considering Hohmann transfer orbits and gravity assists using APIs and Python, displaying launch windows, travel time, and fuel efficiency. Used as a simulation tool for educational institutions and space-related companies. *AI features currently disabled; calculations performed via API and Python.",zh:"开发了一个从NASA API获取行星位置数据、轨道要素和重力参数的系统，用于分析行星际旅行的最佳出发点。使用API和Python计算考虑霍曼转移轨道和重力辅助的轨迹，显示发射窗口、旅行时间和燃料效率。作为教育机构和航天相关公司的模拟工具使用。*AI功能目前已禁用；通过API和Python进行计算。",ko:"NASA API에서 행성 위치 데이터, 궤도 요소 및 중력 매개변수를 검색하여 행성간 여행의 최적 출발 지점을 분석하는 시스템을 개발했습니다. API와 Python을 사용하여 호만 전이 궤도와 중력 어시스트를 고려한 궤적을 계산하고 발사 윈도우, 이동 시간 및 연료 효율을 표시합니다. 교육 기관 및 우주 관련 기업의 시뮬레이션 도구로 사용됩니다. *AI 기능 현재 비활성화됨; API와 Python으로 계산 수행 중.",es:"Desarrollamos un sistema que recupera datos de posición planetaria, elementos orbitales y parámetros gravitacionales de las APIs de la NASA para analizar puntos de partida óptimos para viajes interplanetarios. Calcula trayectorias considerando órbitas de transferencia de Hohmann y asistencias gravitacionales usando APIs y Python, mostrando ventanas de lanzamiento, tiempo de viaje y eficiencia de combustible. Usado como herramienta de simulación para instituciones educativas y empresas espaciales. *Funciones de IA actualmente deshabilitadas; cálculos realizados mediante API y Python.",pt:"Desenvolvemos um sistema que recupera dados de posição planetária, elementos orbitais e parâmetros gravitacionais das APIs da NASA para analisar pontos de partida ideais para viagens interplanetárias. Calcula trajetórias considerando órbitas de transferência de Hohmann e assistências gravitacionais usando APIs e Python, exibindo janelas de lançamento, tempo de viagem e eficiência de combustível. Usado como ferramenta de simulação para instituições educacionais e empresas espaciais. *Recursos de IA atualmente desativados; cálculos realizados via API e Python.",th:"พัฒนาระบบที่ดึงข้อมูลตำแหน่งดาวเคราะห์ องค์ประกอบวงโคจร และพารามิเตอร์แรงโน้มถ่วงจาก NASA API เพื่อวิเคราะห์จุดออกเดินทางที่เหมาะสมที่สุดสำหรับการเดินทางระหว่างดาวเคราะห์ คำนวณวิถีโดยพิจารณาวงโคจรถ่ายโอนฮอมันน์และการช่วยเหลือด้านแรงโน้มถ่วงโดยใช้ API และ Python แสดงหน้าต่างการปล่อย เวลาเดินทาง และประสิทธิภาพเชื้อเพลิง ใช้เป็นเครื่องมือจำลองสำหรับสถาบันการศึกษาและบริษัทที่เกี่ยวข้องกับอวกาศ *ฟีเจอร์ AI ปิดใช้งานในปัจจุบัน; การคำนวณดำเนินการผ่าน API และ Python",ms:"Membangunkan sistem yang mengambil data kedudukan planet, elemen orbit, dan parameter graviti dari API NASA untuk menganalisis titik berlepas optimum untuk perjalanan antara planet. Mengira trajektori mempertimbangkan orbit pemindahan Hohmann dan bantuan graviti menggunakan API dan Python, memaparkan tetingkap pelancaran, masa perjalanan, dan kecekapan bahan api. Digunakan sebagai alat simulasi untuk institusi pendidikan dan syarikat berkaitan angkasa. *Ciri AI kini dilumpuhkan; pengiraan dilakukan melalui API dan Python.",id:"Mengembangkan sistem yang mengambil data posisi planet, elemen orbit, dan parameter gravitasi dari NASA API untuk menganalisis titik keberangkatan optimal untuk perjalanan antarplanet. Menghitung lintasan dengan mempertimbangkan orbit transfer Hohmann dan bantuan gravitasi menggunakan API dan Python, menampilkan jendela peluncuran, waktu perjalanan, dan efisiensi bahan bakar. Digunakan sebagai alat simulasi untuk lembaga pendidikan dan perusahaan terkait luar angkasa. *Fitur AI saat ini dinonaktifkan; perhitungan dilakukan melalui API dan Python.",de:"Entwicklung eines Systems, das Planetenpositionsdaten, Orbitalelemente und Gravitationsparameter von NASA-APIs abruft, um optimale Abfahrtspunkte für interplanetare Reisen zu analysieren. Berechnet Trajektorien unter Berücksichtigung von Hohmann-Transferbahnen und Gravitationsassistenten mit APIs und Python, zeigt Startfenster, Reisezeit und Treibstoffeffizienz an. Wird als Simulationswerkzeug für Bildungseinrichtungen und raumfahrtbezogene Unternehmen verwendet. *KI-Funktionen derzeit deaktiviert; Berechnungen werden über API und Python durchgeführt."},techStack:{ja:["NASA API (Horizons, SPICE)","軌道力学計算","Python","データビジュアライゼーション"],en:["NASA API (Horizons, SPICE)","Orbital Mechanics","Python","Data Visualization"],zh:["NASA API (Horizons, SPICE)","轨道力学","Python","数据可视化"],ko:["NASA API (Horizons, SPICE)","궤도 역학","Python","데이터 시각화"],es:["NASA API (Horizons, SPICE)","Mecánica Orbital","Python","Visualización de Datos"],pt:["NASA API (Horizons, SPICE)","Mecânica Orbital","Python","Visualização de Dados"],th:["NASA API (Horizons, SPICE)","กลศาสตร์วงโคจร","Python","การแสดงข้อมูลเป็นภาพ"],ms:["NASA API (Horizons, SPICE)","Mekanik Orbit","Python","Visualisasi Data"],id:["NASA API (Horizons, SPICE)","Mekanika Orbit","Python","Visualisasi Data"],de:["NASA API (Horizons, SPICE)","Orbitalmechanik","Python","Datenvisualisierung"]},projectUrl:"https://telet-ai.github.io/space-travel/",imageSrc:"/images/optimized/orbital-simulation.webp"},{id:"real-estate",number:"",title:{ja:"不動産データAI解析",en:"Real Estate Data Analysis with AI",zh:"房地产数据AI分析",ko:"부동산 데이터 AI 분석",es:"Análisis de Datos Inmobiliarios con IA",pt:"Análise de Dados Imobiliários com IA",th:"การวิเคราะห์ข้อมูลอสังหาริมทรัพย์ด้วย AI",ms:"Analisis Data Hartanah dengan AI",id:"Analisis Data Properti dengan AI",de

334:"Immobiliendatenanalyse mit KI"},description:{ja:"特定地域の不動産情報をWebスクレイピングで収集し、LLMとPythonによる高度なデータ解析を行うシステムを開発。物件価格、坪単価、築年数、駅距離などの変数を抽出し、価格トレンド分析、相場予測、投資収益率シミュレーションを自動生成します。非構造化な物件説明文からもLLMで特徴を抽出し、定量データと組み合わせた総合的な市場分析を提供しました。※セカンドバージョンとして公開中（FULLバージョンは別途提供）",en:"Developed a system that collects real estate information for specific regions via web scraping and performs advanced data analysis using LLM and Python. Extracts variables such as property prices, price per tsubo, building age, and distance to stations, automatically generating price trend analysis, market forecasts, and investment ROI simulations. Also extracts features from unstructured property descriptions using LLM, providing comprehensive market analysis combined with quantitative data. *Currently available as second version (full version available separately).",zh:"开发了一个通过网络抓取收集特定地区房地产信息并使用LLM和Python进行高级数据分析的系统。提取房产价格、每坪单价、建筑年龄和到车站距离等变量，自动生成价格趋势分析、市场预测和投资回报率模拟。还使用LLM从非结构化的房产描述中提取特征，结合定量数据提供全面的市场分析。*目前作为第二版本发布（完整版本另行提供）。",ko:"웹 스크래핑을 통해 특정 지역의 부동산 정보를 수집하고 LLM과 Python을 사용한 고급 데이터 분석을 수행하는 시스템을 개발했습니다. 물건 가격, 평당 가격, 건물 연식, 역까지 거리 등의 변수를 추출하여 가격 추세 분석, 시장 예측, 투자 수익률 시뮬레이션을 자동 생성합니다. LLM을 사용하여 비구조화된 물건 설명에서도 특징을 추출하고 정량적 데이터와 결합한 종합적인 시장 분석을 제공했습니다. *현재 두 번째 버전으로 공개 중(풀 버전은 별도 제공).",es:"Desarrollamos un sistema que recopila información inmobiliaria de regiones específicas mediante web scraping y realiza análisis de datos avanzados usando LLM y Python. Extrae variables como precios de propiedades, precio por tsubo, antigüedad del edificio y distancia a estaciones, generando automáticamente análisis de tendencias de precios, pronósticos de mercado y simulaciones de ROI de inversión. También extrae características de descripciones de propiedades no estructuradas usando LLM, proporcionando análisis de mercado integral combinado con datos cuantitativos. *Actualmente disponible como segunda versión (versión completa disponible por separado).",pt:"Desenvolvemos um sistema que coleta informações imobiliárias de regiões específicas via web scraping e realiza análise de dados avançada usando LLM e Python. Extrai variáveis como preços de propriedades, preço por tsubo, idade do edifício e distância até estações, gerando automaticamente análise de tendências de preços, previsões de mercado e simulações de ROI de investimento. Também extrai características de descrições de propriedades não estruturadas usando LLM, fornecendo análise de mercado abrangente combinada com dados quantitativos. *Atualmente disponível como segunda versão (versão completa disponível separadamente).",th:"พัฒนาระบบที่รวบรวมข้อมูลอสังหาริมทรัพย์สำหรับภูมิภาคเฉพาะผ่าน web scraping และทำการวิเคราะห์ข้อมูลขั้นสูงโดยใช้ LLM และ Python ดึงตัวแปรเช่นราคาอสังหาริมทรัพย์ ราคาต่อตารางเมตร อายุอาคาร และระยะทางไปยังสถานี สร้างการวิเคราะห์แนวโน้มราคา การคาดการณ์ตลาด และการจำลอง ROI การลงทุนโดยอัตโนมัติ ยังดึงคุณลักษณะจากคำอธิบายอสังหาริมทรัพย์ที่ไม่มีโครงสร้างโดยใช้ LLM ให้การวิเคราะห์ตลาดที่ครอบคลุมรวมกับข้อมูลเชิงปริมาณ *มีให้บริการในเวอร์ชันที่สองในปัจจุบัน (เวอร์ชันเต็มมีให้แยกต่างหาก)",ms:"Membangunkan sistem yang mengumpul maklumat hartanah untuk kawasan tertentu melalui web scraping dan melakukan analisis data lanjutan menggunakan LLM dan Python. Mengekstrak pembolehubah seperti harga hartanah, harga per tsubo, umur bangunan, dan jarak ke stesen, menjana analisis trend harga, ramalan pasaran, dan simulasi ROI pelaburan secara automatik. Juga mengekstrak ciri dari perihalan hartanah tidak berstruktur menggunakan LLM, menyediakan analisis pasaran komprehensif digabungkan dengan data kuantitatif. *Kini tersedia sebagai versi kedua (versi penuh tersedia secara berasingan).",id:"Mengembangkan sistem yang mengumpulkan informasi properti untu

334k wilayah tertentu melalui web scraping dan melakukan analisis data lanjutan menggunakan LLM dan Python. Mengekstrak variabel seperti harga properti, harga per tsubo, usia bangunan, dan jarak ke stasiun, secara otomatis menghasilkan analisis tren harga, perkiraan pasar, dan simulasi ROI investasi. Juga mengekstrak fitur dari deskripsi properti tidak terstruktur menggunakan LLM, memberikan analisis pasar komprehensif dikombinasikan dengan data kuantitatif. *Saat ini tersedia sebagai versi kedua (versi lengkap tersedia secara terpisah).",de:"Entwicklung eines Systems, das Immobilieninformationen für bestimmte Regionen per Web-Scraping sammelt und erweiterte Datenanalysen mit LLM und Python durchführt. Extrahiert Variablen wie Immobilienpreise, Preis pro Tsubo, Gebäudealter und Entfernung zu Bahnhöfen und generiert automatisch Preistrend-Analysen, Marktprognosen und Investitions-ROI-Simulationen. Extrahiert auch Merkmale aus unstrukturierten Immobilienbeschreibungen mit LLM und bietet umfassende Marktanalysen kombiniert mit quantitativen Daten. *Derzeit als zweite Version verfügbar (Vollversion separat erhältlich)."},techStack:{ja:["Webスクレイピング (Scrapy, Playwright)","Python","Pandas","LLM解析","データ可視化"],en:["Web Scraping (Scrapy, Playwright)","Python","Pandas","LLM Analysis","Data Visualization"],zh:["网络抓取 (Scrapy, Playwright)","Python","Pandas","LLM分析","数据可视化"],ko:["웹 스크래핑 (Scrapy, Playwright)","Python","Pandas","LLM 분석","데이터 시각화"],es:["Web Scraping (Scrapy, Playwright)","Python","Pandas","Análisis LLM","Visualización de Datos"],pt:["Web Scraping (Scrapy, Playwright)","Python","Pandas","Análise LLM","Visualização de Dados"],th:["Web Scraping (Scrapy, Playwright)","Python","Pandas","การวิเคราะห์ LLM","การแสดงข้อมูลเป็นภาพ"],ms:["Web Scraping (Scrapy, Playwright)","Python","Pandas","Analisis LLM","Visualisasi Data"],id:["Web Scraping (Scrapy, Playwright)","Python","Pandas","Analisis LLM","Visualisasi Data"],de:["Web Scraping (Scrapy, Playwright)","Python","Pandas","LLM-Analyse","Datenvisualisierung"]},projectUrl:"https://lookies.ai/",imageSrc:"/images/optimized/real-estate-dashboard.webp"},{id:"fortune-ai",number:"",title:{ja:"四柱推命AI鑑定マネージャー",en:"AI Four Pillars Fortune Analysis Manager",zh:"AI四柱推命运势分析管理器",ko:"AI 사주 운세 분석 매니저",es:"Gestor de Análisis de Fortuna de los Cuatro Pilares con IA",pt:"Gerenciador de Análise de Fortuna dos Quatro Pilares com IA",th:"ระบบจัดการวิเคราะห์ดวงชะตาสี่เสาด้วย AI",ms:"Pengurus Analisis Nasib Empat Tiang AI",id:"Manajer Analisis Keberuntungan Empat Pilar AI",de:"KI-Vier-Säulen-Schicksalsanalyse-Manager"},description:{ja:"生年月日・出生時間・出生地から四柱推命（命式）を算出し、運勢・相性・適職などを自然言語で生成するAI鑑定システム。",en:"An AI fortune analysis system that calculates Four Pillars of Destiny (BaZi chart) from birth date, time, and location, generating natural language readings for fortune, compatibility, and career suitability.",zh:"一个AI运势分析系统，根据出生日期、时间和地点计算四柱推命（八字命盘），以自然语言生成运势、相性和职业适合性的解读。",ko:"생년월일, 출생 시간, 출생지로부터 사주팔자를 계산하고 운세, 궁합, 적성 등을 자연어로 생성하는 AI 운세 분석 시스템입니다.",es:"Un sistema de análisis de fortuna con IA que calcula los Cuatro Pilares del Destino (carta BaZi) a partir de la fecha, hora y lugar de nacimiento, generando lecturas en lenguaje natural sobre fortuna, compatibilidad y aptitud profesional.",pt:"Um sistema de análise de fortuna com IA que calcula os Quatro Pilares do Destino (carta BaZi) a partir da data, hora e local de nascimento, gerando leituras em linguagem natural sobre fortuna, compatibilidade e adequação profissional.",th:"ระบบวิเคราะห์ดวงชะตาด้วย AI ที่คำนวณสี่เสาแห่งชะตากรรม (แผนภูมิ BaZi) จากวันเกิด เวลา และสถานที่เกิด สร้างคำทำนายเป็นภาษาธรรมชาติเกี่ยวกับดวง ความเข้ากันได้ และความเหมาะสมกับอาชีพ",ms:"Sistem analisis nasib AI yang mengira Empat Tiang Takdir (carta BaZi) dari tarikh, masa, dan lokasi lahir, menjana bacaan bahasa semula jadi untuk nasib, keserasian, dan kesesuaian kerjaya.",id:"Sistem analisis keberuntungan AI yang menghitung Empat Pilar Takdir (bagan BaZi) dari tanggal, waktu, dan lokasi lahir, menghasilkan pembacaan bahasa alami untu

334k keberuntungan, kompatibilitas, dan kesesuaian karier.",de:"Ein KI-Schicksalsanalysesystem, das die Vier Säulen des Schicksals (BaZi-Diagramm) aus Geburtsdatum, -zeit und -ort berechnet und natürlichsprachliche Lesungen für Glück, Kompatibilität und Berufseignung erstellt."},paragraphs:{ja:["四柱推命（生年月日・出生時間・出生地）を元に個人の運勢や性格をAIで解析し、自然言語でわかりやすくフィードバックする鑑定システムを開発。出生地からタイムゾーンを自動判定し、真太陽時を算出することで正確な命式計算を実現しました。","四柱推命の占い学に基づくロジックをPythonで指数化し、統計データとRAG（検索拡張生成）情報を組み合わせることで、命式解析の精度を高めています。","算出された命式データは、LLMが自然言語でわかりやすく解説。運勢の流れ、対人関係の相性、あなたに適した職業など、パーソナライズされた鑑定結果を生成します。専門の占い師に相談したときと同様の、深みのある回答を得ることができます。","現在、セカンドバージョンとして公開中です。基本的な命式解析と運勢鑑定をお試しいただけます。より詳細な分析と多角的なアドバイスを含むフルバージョンは、別途ご提供しております。"],en:["Developed a fortune analysis system that uses AI to analyze individual fortune and personality based on Four Pillars of Destiny (birth date, time, and location), providing easy-to-understand feedback in natural language. Achieved accurate BaZi chart calculation by automatically determining time zones from birthplace and computing true solar time.","The fortune-telling logic based on Four Pillars astrology is indexed in Python, combining statistical data with RAG (Retrieval-Augmented Generation) information to enhance the accuracy of chart analysis.","The calculated chart data is explained in natural language by the LLM, generating personalized fortune readings including life cycles, interpersonal compatibility, and suitable careers. Provides in-depth responses comparable to consulting a professional fortune teller.","Currently available as second version, offering basic chart analysis and fortune reading. A full version with more detailed analysis and multi-faceted advice is available separately."],zh:["开发了一个运势分析系统，使用AI基于四柱推命（出生日期、时间和地点）分析个人运势和性格，以自然语言提供易于理解的反馈。通过自动从出生地确定时区并计算真太阳时，实现了准确的八字命盘计算。","基于四柱占星术的算命逻辑在Python中被索引化，结合统计数据和RAG（检索增强生成）信息来提高命盘分析的准确性。","计算出的命盘数据由LLM以自然语言解释，生成包括人生周期、人际关系相性和适合职业在内的个性化运势解读。提供与咨询专业算命师相当的深入回答。","目前作为第二版本提供，提供基本的命盘分析和运势解读。包含更详细分析和多方面建议的完整版本另行提供。"],ko:["사주팔자(생년월일, 출생 시간, 출생지)를 기반으로 AI를 사용하여 개인의 운세와 성격을 분석하고 자연어로 이해하기 쉬운 피드백을 제공하는 운세 분석 시스템을 개발했습니다. 출생지에서 시간대를 자동으로 결정하고 진태양시를 계산하여 정확한 사주 계산을 실현했습니다.","사주 점성술에 기반한 운세 로직이 Python으로 인덱싱되어 통계 데이터와 RAG(검색 증강 생성) 정보를 결합하여 사주 분석의 정확도를 높였습니다.","계산된 사주 데이터는 LLM이 자연어로 설명하여 인생 주기, 대인 관계 궁합, 적합한 직업을 포함한 개인화된 운세 해석을 생성합니다. 전문 점술사에게 상담했을 때와 비슷한 깊이 있는 답변을 제공합니다.","현재 두 번째 버전으로 제공되며 기본적인 사주 분석과 운세 해석을 이용할 수 있습니다. 더 상세한 분석과 다각적인 조언ì

334„ 포함한 풀 버전은 별도로 제공됩니다."],es:["Desarrollamos un sistema de análisis de fortuna que utiliza IA para analizar la fortuna y personalidad individual basándose en los Cuatro Pilares del Destino (fecha, hora y lugar de nacimiento), proporcionando retroalimentación fácil de entender en lenguaje natural. Logramos un cálculo preciso de la carta BaZi determinando automáticamente las zonas horarias desde el lugar de nacimiento y calculando el tiempo solar verdadero.","La lógica de adivinación basada en la astrología de los Cuatro Pilares está indexada en Python, combinando datos estadísticos con información RAG (Generación Aumentada por Recuperación) para mejorar la precisión del análisis de la carta.","Los datos de la carta calculados son explicados en lenguaje natural por el LLM, generando lecturas de fortuna personalizadas incluyendo ciclos de vida, compatibilidad interpersonal y carreras adecuadas. Proporciona respuestas profundas comparables a consultar a un adivino profesional.","Actualmente disponible como segunda versión, ofreciendo análisis básico de carta y lectura de fortuna. Una versión completa con análisis más detallado y consejos multifacéticos está disponible por separado."],pt:["Desenvolvemos um sistema de análise de fortuna que usa IA para analisar a fortuna e personalidade individual com base nos Quatro Pilares do Destino (data, hora e local de nascimento), fornecendo feedback fácil de entender em linguagem natural. Alcançamos um cálculo preciso da carta BaZi determinando automaticamente os fusos horários a partir do local de nascimento e calculando o tempo solar verdadeiro.","A lógica de adivinhação baseada n

334a astrologia dos Quatro Pilares é indexada em Python, combinando dados estatísticos com informações RAG (Geração Aumentada por Recuperação) para melhorar a precisão da análise da carta.","Os dados da carta calculados são explicados em linguagem natural pelo LLM, gerando leituras de fortuna personalizadas incluindo ciclos de vida, compatibilidade interpessoal e carreiras adequadas. Fornece respostas aprofundadas comparáveis à consulta com um adivinho profissional.","Atualmente disponível como segunda versão, oferecendo análise básica de carta e leitura de fortuna. Uma versão completa com análise mais detalhada e conselhos multifacetados está disponível separadamente."],th:["พัฒนาระบบวิเคราะห์ดวงชะตาที่ใช้ AI เพื่อวิเคราะห์ดวงชะตาและบุคลิกภาพส่วนบุคคลตามสี่เสาแห่งชะตากรรม (วันเกิด เวลา และสถานที่) ให้ข้อเสนอแนะที่เข้าใจง่ายในภาษาธรรมชาติ บรรลุการคำนวณแผนภูมิ BaZi ที่แม่นยำโดยการกำหนดโซนเวลาจากสถานที่เกิดโดยอัตโนมัติและคำนวณเวลาสุริยะที่แท้จริง","ตรรกะการทำนายตามโหราศาสตร์สี่เสาถูกจัดทำดัชนีใน Python โดยรวมข้อมูลสถิติกับข้อมูล RAG (การสร้างเสริมการดึงข้อมูล) เพื่อเพิ่มความแม่นยำของการวิเคราะห์แผนภูมิ","ข้อมูลแผนภูมิที่คำนวณได้จะถูกอธิบายเป็นภาษาธรรมชาติโดย LLM สร้างการทำนายดวงชะตาส่วนบุคคลรวมถึงวัฏจักรชีวิต ความเข้ากันได้ระหว่างบุคคล และอาชีพที่เหมาะสม ให้คำตอบเชิงลึกเทียบเท่ากับการปรึกษาหมอดูมืออาชีพ","ปัจจุบันมีให้บริการเป็นเวอร์ชันที่สอง นำเสนอการวิเคราะห์แผนภูมิพื้นฐานและการทำนายดวงชะตา เวอร์ชันเต็มพร้อมการวิเคราะห์ที่ละเอียดกว่าและคำแนะนำหลากหลายมีให้แยกต่างหาก"],ms:["Membangunkan sistem analisis nasib yang menggunakan AI untuk menganalisis nasib dan personaliti individu berdasarkan Empat Tiang Takdir (tarikh, masa, dan lokasi lahir), memberikan maklum balas mudah difahami dalam bahasa semula jadi. Mencapai pengiraan carta BaZi yang tepat dengan menentukan zon masa dari tempat lahir secara automatik dan mengira masa solar sebenar.","Logik ramalan berdasarkan astrologi Empat Tiang diindeks dalam Python, menggabungkan data statistik dengan maklumat RAG (Penjanaan Ditingkatkan Pengambilan) untuk meningkatkan ketepatan analisis carta.","Data carta yang dikira dijelaskan dalam bahasa semula jadi oleh LLM, menjana bacaan nasib yang diperibadikan termasuk kitaran hidup, keserasian interpersonal, dan kerjaya yang sesuai. Memberikan respons mendalam setanding dengan berunding dengan peramal profesional.","Kini tersedia sebagai versi kedua, menawarkan analisis carta asas dan bacaan nasib. Versi penuh dengan analisis lebih terperinci dan nasihat pelbagai aspek tersedia secara berasingan."],id:["Mengembangkan sistem analisis keberuntungan yang menggunakan AI untuk menganalisis keberuntungan dan kepribadian individu berdasarkan Empat Pilar Takdir (tanggal, waktu, dan lokasi lahir), memberikan umpan balik yang mudah dipahami dalam bahasa alami. Mencapai perhitungan bagan BaZi yang akurat dengan secara otomatis menentukan zona waktu dari tempat lahir dan menghitung waktu matahari sejati.","Logika ramalan berdasarkan astrologi Empat Pilar diindeks dalam Python, menggabungkan data statistik dengan informasi RAG (Retrieval-Augmented Generation) untuk meningkatkan akurasi analisis bagan.","Data bagan yang dihitung dijelaskan dalam bahasa alami oleh LLM, menghasilkan pembacaan keberuntungan yang dipersonalisasi termasuk siklus hidup, kompatibilitas interpersonal, dan karier yang cocok. Memberikan respons mendalam yang sebanding dengan berkonsultasi dengan peramal profesional.","Saat ini tersedia sebagai versi kedua, menawarkan analisis bagan dasar dan pembacaan keberuntungan. Versi lengkap dengan analisis lebih detail dan saran multi-segi tersedia secara terpisah."],de:["Entwicklung eines Schicksalsanalysesystems, das KI verwendet, um individuelles Schicksal und Persönlichkeit basierend auf den Vier Säulen des Schicksals (Geburtsdatum, -zeit und 

334-ort) zu analysieren und leicht verständliches Feedback in natürlicher Sprache zu liefern. Erreichte genaue BaZi-Diagrammberechnung durch automatische Bestimmung der Zeitzonen vom Geburtsort und Berechnung der wahren Sonnenzeit.","Die Wahrsagelogik basierend auf der Vier-Säulen-Astrologie ist in Python indexiert und kombiniert statistische Daten mit RAG-Informationen (Retrieval-Augmented Generation), um die Genauigkeit der Diagrammanalyse zu verbessern.","Die berechneten Diagrammdaten werden vom LLM in natürlicher Sprache erklärt und generieren personalisierte Schicksalslesungen einschließlich Lebenszyklen, zwischenmenschlicher Kompatibilität und geeigneter Karrieren. Liefert tiefgehende Antworten vergleichbar mit der Konsultation eines professionellen Wahrsagers.","Derzeit als zweite Version verfügbar, bietet grundlegende Diagrammanalyse und Schicksalslesung. Eine Vollversion mit detaillierterer Analyse und vielseitigen Ratschlägen ist separat erhältlich."]},techStack:{ja:["Python","天文API (Sunrise-Sunset, TimeZoneDB)","四柱推命ロジック","LLM","座標・時刻計算"],en:["Python","Astronomical API (Sunrise-Sunset, TimeZoneDB)","BaZi Logic","LLM","Coordinate/Time Calculation"],zh:["Python","天文API (Sunrise-Sunset, TimeZoneDB)","四柱推命逻辑","LLM","坐标/时间计算"],ko:["Python","천문 API (Sunrise-Sunset, TimeZoneDB)","사주 로직","LLM","좌표/시간 계산"],es:["Python","API Astronómica (Sunrise-Sunset, TimeZoneDB)","Lógica BaZi","LLM","Cálculo de Coordenadas/Tiempo"],pt:["Python","API Astronômica (Sunrise-Sunset, TimeZoneDB)","Lógica BaZi","LLM","Cálculo de Coordenadas/Tempo"],th:["Python","API ดาราศาสตร์ (Sunrise-Sunset, TimeZoneDB)","ตรรกะ BaZi","LLM","การคำนวณพิกัด/เวลา"],ms:["Python","API Astronomi (Sunrise-Sunset, TimeZoneDB)","Logik BaZi","LLM","Pengiraan Koordinat/Masa"],id:["Python","API Astronomi (Sunrise-Sunset, TimeZoneDB)","Logika BaZi","LLM","Perhitungan Koordinat/Waktu"],de:["Python","Astronomische API (Sunrise-Sunset, TimeZoneDB)","BaZi-Logik","LLM","Koordinaten-/Zeitberechnung"]},projectUrl:"https://lookies.ai/",imageSrc:"",noImage:!0},{id:"google-workspace",number:"",title:{ja:"Google Workspace AI連携",en:"Google Workspace AI Integration",zh:"Google Workspace AI集成",ko:"Google Workspace AI 통합",es:"Integración de IA con Google Workspace",pt:"Integração de IA com Google Workspace",th:"การรวม AI กับ Google Workspace",ms:"Integrasi AI Google Workspace",id:"Integrasi AI Google Workspace",de:"Google Workspace KI-Integration"},description:{ja:"Google Workspace（Gmail、Drive、Calendar、Docs、Sheets）とLLMを連携し、業務データの読み込み・編集・自動処理を行うシステムを開発。メール内容の要約・自動返信下書き生成、Driveファイルの検索・内容解析、Calendarからのスケジュール抽出と調整提案など、日常業務をAIがアシストします。OAuth認証による安全なAPI接続と、MCP準拠のツール設計により、Agentからの自律的なWorkspace操作も実現しました。※セカンドバージョンとして公開中（FULLバージョンは別途提供）",en:"Developed a system integrating Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) with LLM for reading, editing, and automated processing of business data. AI assists daily tasks including email summarization and auto-reply draft generation, Drive file search and content analysis, and schedule extraction from Calendar with adjustment suggestions. Achieved autonomous Workspace operations from Agents through secure OAuth-authenticated API connections and MCP-compliant tool design. *Currently available as second version (full version available separately).",zh:"开发了一个将Google Workspace（Gmail、Drive、Calendar、Docs、Sheets）与LLM集成的系统，用于业务数据的读取、编辑和自动处理。AI协助日常任务，包括邮件摘要和自动回复草稿生成、Drive文件搜索和内容分析、以及从Calendar提取日程并提出调整建议。通过安全的OAuth认证API连接和符合MCP的工具设计，实现了Agent的自主Workspace操作。*目前作为第二版本发布（完整版本另行提供）。",ko:"Google Worksp

334ace(Gmail, Drive, Calendar, Docs, Sheets)와 LLM을 연동하여 업무 데이터의 읽기, 편집, 자동 처리를 수행하는 시스템을 개발했습니다. 이메일 요약 및 자동 회신 초안 생성, Drive 파일 검색 및 콘텐츠 분석, Calendar에서 일정 추출 및 조정 제안 등 일상 업무를 AI가 지원합니다. 안전한 OAuth 인증 API 연결과 MCP 준수 도구 설계를 통해 Agent의 자율적인 Workspace 작업을 실현했습니다. *현재 두 번째 버전으로 공개 중(풀 버전은 별도 제공).",es:"Desarrollamos un sistema que integra Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) con LLM para lectura, edición y procesamiento automatizado de datos empresariales. La IA asiste tareas diarias incluyendo resumen de correos y generación de borradores de respuesta automática, búsqueda de archivos en Drive y análisis de contenido, y extracción de programación desde Calendar con sugerencias de ajuste. Logró operaciones autónomas de Workspace desde Agentes mediante conexiones API autenticadas con OAuth seguras y diseño de herramientas compatible con MCP. *Actualmente disponible como segunda versión (versión completa disponible por separado).",pt:"Desenvolvemos um sistema integrando Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) com LLM para leitura, edição e processamento automatizado de dados empresariais. A IA auxilia tarefas diárias incluindo resumo de e-mails e geração de rascunhos de resposta automática, pesquisa de arquivos no Drive e análise de conteúdo, e extração de agenda do Calendar com sugestões de ajuste. Alcançou operações autônomas do Workspace a partir de Agentes através de conexões API autenticadas com OAuth seguras e design de ferramentas compatível com MCP. *Atualmente disponível como segunda versão (versão completa disponível separadamente).",th:"พัฒนาระบบที่รวม Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) กับ LLM สำหรับการอ่าน แก้ไข และประมวลผลอัตโนมัติของข้อมูลธุรกิจ AI ช่วยเหลืองานประจำวันรวมถึงการสรุปอีเมลและการสร้างร่างตอบกลับอัตโนมัติ การค้นหาไฟล์ Drive และการวิเคราะห์เนื้อหา และการดึงกำหนดการจาก Calendar พร้อมข้อเสนอแนะการปรับเปลี่ยน บรรลุการดำเนินการ Workspace อัตโนมัติจาก Agents ผ่านการเชื่อมต่อ API ที่รับรองความถูกต้องด้วย OAuth ที่ปลอดภัยและการออกแบบเครื่องมือที่สอดคล้องกับ MCP *มีให้บริการในเวอร์ชันที่สองในปัจจุบัน (เวอร์ชันเต็มมีให้แยกต่างหาก)",ms:"Membangunkan sistem yang menyepadukan Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) dengan LLM untuk membaca, mengedit, dan pemprosesan automatik data perniagaan. AI membantu tugas harian termasuk ringkasan e-mel dan penjanaan draf balasan automatik, carian fail Drive dan analisis kandungan, dan pengekstrakan jadual dari Calendar dengan cadangan penyesuaian. Mencapai operasi Workspace autonomi dari Ejen melalui sambungan API yang disahkan OAuth selamat dan reka bentuk alat yang mematuhi MCP. *Kini tersedia sebagai versi kedua (versi penuh tersedia secara berasingan).",id:"Mengembangkan sistem yang mengintegrasikan Google Workspace (Gmail, Drive, Calendar, Docs, Sheets) dengan LLM untuk membaca, mengedit, dan pemrosesan otomatis data bisnis. AI membantu tugas harian termasuk ringkasan email dan pembuatan draf balasan otomatis, pencarian file Drive dan analisis konten, dan ekstraksi jadwal dari Calendar dengan saran penyesuaian. Mencapai operasi Workspace otonom dari Agents melalui koneksi API yang diautentikasi OAuth yang aman dan desain alat yang sesuai MCP. *Saat ini tersedia sebagai versi kedua (versi lengkap tersedia secara terpisah).",de:"Entwicklung eines Systems, das Google Worksp

334ace (Gmail, Drive, Calendar, Docs, Sheets) mit LLM für das Lesen, Bearbeiten und automatische Verarbeiten von Geschäftsdaten integriert. KI unterstützt tägliche Aufgaben einschließlich E-Mail-Zusammenfassung und automatische Antwort-Entwurfserstellung, Drive-Dateisuche und Inhaltsanalyse sowie Terminextraktion aus dem Kalender mit Anpassungsvorschlägen. Erreichte autonome Workspace-Operationen von Agenten durch sichere OAuth-authentifizierte API-Verbindungen und MCP-konformes Werkzeugdesign. *Derzeit als zweite Version verfügbar (Vollversion separat erhältlich)."},techStack:{ja:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],en:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],zh:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],ko:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],es:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],pt:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],th:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],ms:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],id:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"],de:["Google Workspace API","OAuth 2.0","LLM","MCP","LangChain Tools","FastAPI"]},projectUrl:"https://lookies.ai/",imageSrc:"/images/google-workspace.jpg"},{id:"world-news",number:"",title:{ja:"世界NEWS多言語化",en:"World News Multilingual Summarization",zh:"全球新闻多语言摘要",ko:"세계 뉴스 다국어 요약",es:"Resumen Multilingüe de Noticias Mundiales",pt:"Resumo Multilíngue de Notícias Mundiais",th:"การสรุปข่าวโลกหลายภาษา",ms:"Ringkasan Berita Dunia Berbilang Bahasa",id:"Ringkasan Berita Dunia Multibahasa",de:"Mehrsprachige Weltübersichtszusammenfassung"},description:{ja:"世界各国のニュースソースからリアルタイムで記事を収集し、LLMによる多言語翻訳・要約・カテゴリ分類を行うシステム開発。英語、中国語、フランス語、アラビア語など多言語の原文記事を日本語に翻訳し、トピック別に整理。単純な機械翻訳ではなく、LLMによる文脈理解に基づいた自然な翻訳と、記事の要点を抽出したサマリー生成により、効率的な国際ニュースモニタリングを実現しました。※セカンドバージョンとして公開中（FULLバージョンは別途提供）",en:"Developed a system that collects articles in real-time from news sources worldwide, performing multilingual translation, summarization, and category classification using LLM. Translates original articles in English, Chinese, French, Arabic, and other languages into Japanese, organized by topic. Achieved efficient international news monitoring through natural translation based on LLM context understanding rather than simple machine translation, along with summary generation extracting key points. *Currently available as second version (full version available separately).",zh:"开发了一个从全球新闻源实时收集文章的系统，使用LLM进行多语言翻译、摘要和分类。将英语、中文、法语、阿拉伯语等多语言原文文章翻译成日语，按主题组织。通过基于LLM上下文理解的自然翻译而非简单的机器翻译，以及提取要点的摘要生成，实现了高效的国际新闻监控。*目前作为第二版本发布（完整版本另行提供）。",ko:"전 세계 뉴스 소스에서 기사를 실시간으로 수집하고 LLM을 사용한 다국어 번역, 요약, 카테고리 분류를 수행하는 시스템을 개발했습니다. 영어, 중국어, 프랑스어, 아랍어 등 다국어 원문 기사를 일본어로 번역하여 주제별로 정리합니다. 단순한 기계 번역이 아닌 LLM 문맥 이해에 기반한 자연스러운 번역과 핵심 요점을 추출한 요약 생성을 통해 효율적인 국제 뉴스 모니터링을 실현했습니다. *현재 두 번째 버전으로 공개 중(풀 버전은 별도 제공).",es:"Desarrollamos un sistema que recopila artículos en tiempo real de fuentes de noticias de todo el mund

334o, realizando traducción multilingüe, resumen y clasificación por categorías usando LLM. Traduce artículos originales en inglés, chino, francés, árabe y otros idiomas al japonés, organizados por tema. Logró un monitoreo eficiente de noticias internacionales mediante traducción natural basada en comprensión contextual de LLM en lugar de traducción automática simple, junto con generación de resúmenes extrayendo puntos clave. *Actualmente disponible como segunda versión (versión completa disponible por separado).",pt:"Desenvolvemos um sistema que coleta artigos em tempo real de fontes de notícias em todo o mundo, realizando tradução multilíngue, resumo e classificação por categorias usando LLM. Traduz artigos originais em inglês, chinês, francês, árabe e outros idiomas para japonês, organizados por tópico. Alcançou monitoramento eficiente de notícias internacionais através de tradução natural baseada na compreensão contextual do LLM em vez de tradução automática simples, junto com geração de resumos extraindo pontos-chave. *Atualmente disponível como segunda versão (versão completa disponível separadamente).",th:"พัฒนาระบบที่รวบรวมบทความแบบเรียลไทม์จากแหล่งข่าวทั่วโลก ทำการแปลหลายภาษา สรุป และจำแนกหมวดหมู่โดยใช้ LLM แปลบทความต้นฉบับภาษาอังกฤษ à¸

334ˆà¸µà¸™ ฝรั่งเศส อาหรับ และภาษาอื่นๆ เป็นภาษาญี่ปุ่น จัดเรียงตามหัวข้อ บรรลุการติดตามข่าวระหว่างประเทศอย่างมีประสิทธิภาพผ่านการแปลที่เป็นธรรมชาติตามความเข้าใจบริบทของ LLM แทนการแปลด้วยเครื่องอย่างง่าย พร้อมกับการสร้างสรุปที่ดึงประเด็นสำคัญ *มีให้บริการในเวอร์ชันที่สองในปัจจุบัน (เวอร์ชันเต็มมีให้แยกต่างหาก)",ms:"Membangunkan sistem yang mengumpul artikel dalam masa nyata dari sumber berita di seluruh dunia, melaksanakan terjemahan berbilang bahasa, ringkasan, dan pengelasan kategori menggunakan LLM. Menterjemah artikel asal dalam bahasa Inggeris, Cina, Perancis, Arab, dan bahasa lain ke bahasa Jepun, disusun mengikut topik. Mencapai pemantauan berita antarabangsa yang cekap melalui terjemahan semula jadi berdasarkan pemahaman konteks LLM dan bukannya terjemahan mesin mudah, bersama dengan penjanaan ringkasan yang mengekstrak perkara utama. *Kini tersedia sebagai versi kedua (versi penuh tersedia secara berasingan).",id:"Mengembangkan sistem yang mengumpulkan artikel secara real-time dari sumber berita di seluruh dunia, melakukan terjemahan multibahasa, ringkasan, dan klasifikasi kategori menggunakan LLM. Menerjemahkan artikel asli dalam bahasa Inggris, Cina, Prancis, Arab, dan bahasa lain ke bahasa Jepang, diorganisir berdasarkan topik. Mencapai pemantauan berita internasional yang efisien melalui terjemahan alami berdasarkan pemahaman konteks LLM daripada terjemahan mesin sederhana, bersama dengan pembuatan ringkasan yang mengekstrak poin-poin kunci. *Saat ini tersedia sebagai versi kedua (versi lengkap tersedia secara terpisah).",de:"Entwicklung eines Systems, das Artikel in Echtzeit von Nachrichtenquellen weltweit sammelt und mehrsprachige Übersetzung, Zusammenfassung und Kategorisierung mit LLM durchführt. Übersetzt Originalartikel in Englisch, Chinesisch, Französisch, Arabisch und anderen Sprachen ins Japanische, organisiert nach Themen. Erreichte effizientes internationales Nachrichtenmonitoring durch natürliche Übersetzung basierend auf LLM-Kontextverständnis anstelle einfacher maschineller Übersetzung, zusammen mit Zusammenfassungsgenerierung, die Kernpunkte extrahiert. *Derzeit als zweite Version verfügbar (Vollversion separat erhältlich)."},techStack:{ja:["News API","RSS収集","LLM翻訳・要約","多言語処理","自動カテゴリ分類","定期バッチ処理"],en:["News API","RSS Collection","LLM Translation/Summarization","Multilingual Processing","Auto Category Classification","Scheduled Batch Processing"],zh:["News API","RSS收集","LLM翻译/摘要","多语言处理","自动分类","定时批处理"],ko:["News API","RSS 수집","LLM 번역/요약","다국어 처리","자동 카테고리 분류","정기 배치 처리"],es:["News API","Recopilación RSS","Traducción/Resumen LLM","Procesamiento Multilingüe","Clasificación Automática de Categorías","Procesamiento por Lotes Programado"],pt:["News API","Coleta RSS","Tradução/Resumo LLM","Processamento Multilíngue","Classificação Automática de Categorias","Processamento em Lote Programado"],th:["News API","การรวบรวม RSS","การแปล/สรุป LLM","การประมวลผลหลายภาษา","การจำแนกหมวดหมู่อัตโนมัติ","การประมวลผลแบบแบตช์ตามกำหนดเวลา"],ms:["News API","Pengumpulan RSS","Terjemahan/Ringkasan LLM","Pemprosesan Berbilang Bahasa","Pengelasan Kategori Automatik","Pemprosesan Kumpulan Berjadual"],id:["News API","Pengumpulan RSS","Terjemah

334an/Ringkasan LLM","Pemrosesan Multibahasa","Klasifikasi Kategori Otomatis","Pemrosesan Batch Terjadwal"],de:["News API","RSS-Sammlung","LLM-Übersetzung/Zusammenfassung","Mehrsprachige Verarbeitung","Automatische Kategorisierung","Geplante Stapelverarbeitung"]},projectUrl:"https://lookies.ai/",imageSrc:"/images/world-news.jpg"},{id:"security-monitoring",number:"",title:{ja:"セキュリティログ監視AIマネージャー",en:"AI Security Log Monitoring Manager",zh:"AI安全日志监控管理器",ko:"AI 보안 로그 모니터링 매니저",es:"Gestor de Monitoreo de Logs de Seguridad con IA",pt:"Gerenciador de Monitoramento de Logs de Segurança com IA",th:"ระบบจัดการติดตาม Log ความปลอดภัยด้วย AI",ms:"Pengurus Pemantauan Log Keselamatan AI",id:"Manajer Pemantauan Log Keamanan AI",de:"KI-Sicherheitsprotokoll-Überwachungsmanager"},description:{ja:"企業のセキュリティログをリアルタイム監視し、LLMによる高度な脅威検知・分析を行うシステムを開発。ファイアウォール、IDS/IPS、認証ログ、アプリケーションログなど多様なログソースをPythonモジュールで収集・解析し、RAGに蓄積された過去の攻撃パターン、脆弱性情報、インシデント事例と照合します。異常パターンを検知した際はLLMが脅威レベル、影響範囲、推奨対応を自然言語で解説し、担当者へ即時アラート通知。同一パターンの再発時は自動的に高優先度アラートを発報し、インシデント対応の迅速化を実現しました。",en:"Developed a system for real-time monitoring of enterprise security logs with advanced threat detection and analysis using LLM. Collects and analyzes diverse log sources including firewall, IDS/IPS, authentication logs, and application logs using Python modules, cross-referencing with past attack patterns, vulnerability information, and incident cases stored in RAG. When anomalous patterns are detected, the LLM explains threat level, scope of impact, and recommended responses in natural language with immediate alert notification to personnel. Automatically issues high-priority alerts upon recurrence of the same pattern, enabling faster incident response.",zh:"开发了一个使用LLM进行高级威胁检测和分析的企业安全日志实时监控系统。使用Python模块收集和分析包括防火墙、IDS/IPS、认证日志和应用程序日志在内的各种日志源，与存储在RAG中的过去攻击模式、漏洞信息和事件案例进行交叉引用。当检测到异常模式时，LLM以自然语言解释威胁级别、影响范围和建议响应，并即时向相关人员发送警报通知。在同一模式再次出现时自动发出高优先级警报，实现更快的事件响应。",ko:"LLM을 사용한 고급 위협 탐지 및 분석으로 기업 보안 로그를 실시간 모니터링하는 시스템을 개발했습니다. Python 모듈을 사용하여 방화벽, IDS/IPS, 인증 로그, 애플리케이션 로그를 포함한 다양한 로그 소스를 수집 및 분석하고 RAG에 저장된 과거 공격 패턴, 취약점 정보, 인시던트 사례와 교차 참조합니다. 이상 패턴이 감지되면 LLM이 위협 수준, 영향 범위, 권장 대응을 자연어로 설명하고 담당자에게 즉시 알림을 전송합니다. 동일 패턴 재발 시 자동으로 고우선순위 알림을 발송하여 빠른 인시던트 대응을 실현했습니다.",es:"Desarrollamos un sistema para monitoreo en tiempo real de logs de seguridad empresarial con detección y análisis avanzado de amenazas usando LLM. Recopila y analiza diversas fuentes de logs incluyendo firewall, IDS/IPS, logs de autenticación y logs de aplicaciones usando módulos Python, cruzando referencias con patrones de ataque pasados, información de vulnerabilidades y casos de incidentes almacenados en RAG. Cuando se detectan patrones anómalos, el LLM explica nivel de amenaza, alcance del impacto y respuestas recomendadas en lenguaje natural con notificación de alerta inmediata al personal. Emite automáticamente alertas de alta prioridad ante recurrencia del mismo patrón, permitiendo respuesta más rápida a incidentes.",pt:"Desenvolvemos um sistema para monitoramento em tempo real de logs de segurança empresarial com detecção e análise avançada de ameaças usando LLM. Coleta e analisa diversas fontes de logs incluindo firewall, IDS/IPS, logs de autenticação e logs de aplicação usando módulos Python, cruzando referências com padrões de ataque passados, informações de vulnerabilidades e casos de incidentes armazenados em RAG. Quando padrões anômalos são detectados, o LLM explica nível de ameaça, escopo de impacto e respostas re

334comendadas em linguagem natural com notificação de alerta imediata ao pessoal. Emite automaticamente alertas de alta prioridade na recorrência do mesmo padrão, permitindo resposta mais rápida a incidentes.",th:"พัฒนาระบบสำหรับการตรวจสอบ log ความปลอดภัยขององค์กรแบบเรียลไทม์พร้อมการตรวจจับและวิเคราะห์ภัยคุกคามขั้นสูงโดยใช้ LLM รวบรวมและวิเคราะห์แหล่ง log ที่หลากหลายรวมถึงไฟร์วอลล์ IDS/IPS log การยืนยันตัวตน และ log แอปพลิเคชันโดยใช้โมดูล Python อ้างอิงไขว้กับรูปแบบการโจมตีในอดีต ข้อมูลช่องโหว่ และกรณีเหตุการณ์ที่เก็บไว้ใน RAG เมื่อตรวจพบรูปแบบผิดปกติ LLM จะอธิบายระดับภัยคุกคาม ขอบเขตผลกระทบ และการตอบสนองที่แนะนำเป็นภาษาธรรมชาติพร้อมแจ้งเตือนทันทีต่อบุคลากร ออกการแจ้งเตือนความสำคัญสูงโดยอัตโนมัติเมื่อรูปแบบเดียวกันเกิดขึ้นซ้ำ ช่วยให้ตอบสนองต่อเหตุการณ์ได้เร็วขึ้น",ms:"Membangunkan sistem untuk pemantauan masa nyata log keselamatan perusahaan dengan pengesanan dan analisis ancaman lanjutan menggunakan LLM. Mengumpul dan menganalisis pelbagai sumber log termasuk firewall, IDS/IPS, log pengesahan, dan log aplikasi menggunakan modul Python, merujuk silang dengan corak serangan lepas, maklumat kerentanan, dan kes insiden yang disimpan dalam RAG. Apabila corak anomali dikesan, LLM menerangkan tahap ancaman, skop impak, dan respons yang disyorkan dalam bahasa semula jadi dengan pemberitahuan amaran segera kepada kakitangan. Mengeluarkan amaran keutamaan tinggi secara automatik apabila corak sama berulang, membolehkan respons insiden lebih pantas.",id:"Mengembangkan sistem untuk pemantauan real-time log keamanan perusahaan dengan deteksi dan analisis ancaman canggih menggunakan LLM. Mengumpulkan dan menganalisis berbagai sumber log termasuk firewall, IDS/IPS, log autentikasi, dan log aplikasi menggunakan modul Python, mereferensikan silang dengan pola serangan masa lalu, informasi kerentanan, dan kasus insiden yang disimpan dalam RAG. Ketika pola anomali terdeteksi, LLM menjelaskan tingkat ancaman, cakupan dampak, dan respons yang direkomendasikan dalam bahasa alami dengan pemberitahuan peringatan segera kepada personel. Secara otomatis mengeluarkan peringatan prioritas tinggi saat pola yang sama terulang, memungkinkan respons insiden yang lebih cepat.",de:"Entwicklung eines Systems zur Echtzeit-Überwachung von Unternehmenssicherheitsprotokollen mit fortschrittlicher Bedrohungserkennung und -analyse mittels LLM. Sammelt und analysiert diverse Protokollquellen einschließlich Firewall, IDS/IPS, Authentifizierungsprotokolle und Anwendungsprotokolle mit Python-Modulen und gleicht sie mit in RAG gespeicherten vergangenen Angriffsmustern, Schwachstelleninformationen und Vorfallsfällen ab. Bei Erkennung anomaler Muster erklärt das LLM Bedrohungsstufe, Auswirkungsbereich und empfohlene Reaktionen in natürlicher Sprache mit sofortiger Alarmbenachrichtigung an das Personal. Gibt bei Wiederholung desselben Musters automatisch Warnungen mit hoher Priorität aus und ermöglicht schnellere Vorfallsreaktion."},techStack:{ja:["Python監視モジュール","ログ収集 (Fluentd, Logstash)","RAG","脅威パターンDB","LLM解析","アラート通知 (Slack, PagerDuty)"],en:["Python Monitoring Module","Log Collection (Fluentd, Logstash)","RAG","Threat Pattern DB","LLM Analysis","Alert Notification (Slack, PagerDuty)"],zh:["Python监控模块","日志收集 (Fluentd, Logstash)","RAG","威胁模式数据库","LLM分析","告警通知 (Slack, PagerDuty)"],ko:["Python 모니터링 모듈","로그 수집 (Fluentd, Logstash)","RAG","위협 패턴 DB","LLM 분석","알림 통지 (Slack, PagerDuty)"],es:["Módulo de Monitoreo Python","Recopilación de Logs (Fluentd, Logstash)","RAG","BD de Patrones de Amenazas","Análisis 

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