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is(n,i.child,null,o),i=Rp(n,n.pendingProps.children),i.flags|=2,n.memoizedState=null,i}function wv(i,n,o){i.lanes|=n;var c=i.alternate;c!==null&&(c.lanes|=n),Qm(i.return,n,o)}function Op(i,n,o,c,m){var f=i.memoizedState;f===null?i.memoizedState={isBackwards:n,rendering:null,renderingStartTime:0,last:c,tail:o,tailMode:m}:(f.isBackwards=n,f.rendering=null,f.renderingStartTime=0,f.last=c,f.tail=o,f.tailMode=m)}function jv(i,n,o){var c=n.pendingProps,m=c.revealOrder,f=c.tail;if(Dt(i,n,c.children,o),c=jt.current,(c&2)!==0)c=c&1|2,n.flags|=128;else{if(i!==null&&(i.flags&128)!==0)e:for(i=n.child;i!==null;){if(i.tag===13)i.memoizedState!==null&&wv(i,o,n);else if(i.tag===19)wv(i,o,n);else if(i.child!==null){i.child.return=i,i=i.child;continue}if(i===n)break e;for(;i.sibling===null;){if(i.return===null||i.return===n)break 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Mt=!1,D5(i,n,o);Mt=(i.flags&131072)!==0}else Mt=!1,Ke&&(n.flags&1048576)!==0&&Jy(n,jc,n.index);switch(n.lanes=0,n.tag){case 16:e:{i=n.pendingProps;var c=n.elementType,m=c._init;if(c=m(c._payload),n.type=c,typeof c=="function")Gm(c)?(i=ar(c,i),n.tag=1,n=yv(null,n,c,i,o)):(n.tag=0,n=Cp(null,n,c,i,o));else{if(c!=null){if(m=c.$$typeof,m===C){n.tag=11,n=mv(null,n,c,i,o);break e}else if(m===V){n.tag=14,n=pv(null,n,c,i,o);break e}}throw n=se(c)||c,Error(s(306,n,""))}}return n;case 0:return Cp(i,n,n.type,n.pendingProps,o);case 1:return c=n.type,m=ar(c,n.pendingProps),yv(i,n,c,m,o);case 3:e:{if(ke(n,n.stateNode.containerInfo),i===null)throw Error(s(387));c=n.pendingProps;var f=n.memoizedState;m=f.element,np(i,n),Eo(n,c,null,o);var j=n.memoizedState;if(c=j.cache,tn(n,wt,c),c!==f.cache&&Zm(n,[wt],o,!0),To(),c=j.element,f.isDehydrated)if(f={element:c,isDehydrated:!1,cache:j.cache},n.updateQueue.baseState=f,n.memoizedState=f,n.flags&256){n=bv(i,n,c,o);break e}else 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i===null&&Ke&&((m=c=dt)&&(c=cI(c,n.type,n.pendingProps,ni),c!==null?(n.stateNode=c,Ut=n,dt=_a(c.firstChild),ni=!1,m=!0):m=!1),m||Qn(n)),la(n),m=n.type,f=n.pendingProps,j=i!==null?i.memoizedProps:null,c=f.children,uh(m,f)?c=null:j!==null&&uh(m,j)&&(n.flags|=32),n.memoizedState!==null&&(m=dp(i,n,I5,null,null,o),Jo._currentValue=m),Bc(i,n),Dt(i,n,c,o),n.child;case 6:return i===null&&Ke&&((i=o=dt)&&(o=dI(o,n.pendingProps,ni),o!==null?(n.stateNode=o,Ut=n,dt=null,i=!0):i=!1),i||Qn(n)),null;case 13:return vv(i,n,o);case 4:return ke(n,n.stateNode.containerInfo),c=n.pendingProps,i===null?n.child=is(n,null,c,o):Dt(i,n,c,o),n.child;case 11:return mv(i,n,n.type,n.pendingProps,o);case 7:return Dt(i,n,n.pendingProps,o),n.child;case 8:return Dt(i,n,n.pendingProps.children,o),n.child;case 12:return Dt(i,n,n.pendingProps.children,o),n.child;case 10:return c=n.pendingProps,tn(n,n.type,c.value),Dt(i,n,c.children,o),n.child;case 9:return m=n.type._context,c=n.pendingProps.children,Jn(n),m=qt(m),c=c(m),n.flags|=1,Dt(i,n,c,o),n.child;case 14:return pv(i,n,n.type,n.pendingProps,o);case 15:return hv(i,n,n.type,n.pendingProps,o);case 19:return jv(i,n,o);case 31:return c=n.pendingProps,o=n.mode,c={mode:c.mode,children:c.children},i===null?(o=Hc(c,o),o.ref=n.ref,n.child=o,o.return=n,n=o):(o=Ii(i.child,c),o.ref=n.ref,n.child=o,o.return=n,n=o),n;case 22:return fv(i,n,o);case 24:return Jn(n),c=qt(wt),i===null?(m=tp(),m===null&&(m=it,f=Jm(),m.pooledCache=f,f.refCount++,f!==null&&(m.pooledCacheLanes|=o),m=f),n.memoizedState={parent:c,cache:m},ip(n),tn(n,wt,m)):((i.lanes&o)!==0&&(np(i,n),Eo(n,null,null,o),To()),m=i.memoizedState,f=n.memoizedState,m.parent!==c?(m={parent:c,cache:c},n.memoizedState=m,n.lanes===0&&(n.memoizedState=n.updateQueue.baseState=m),tn(n,wt,c)):(c=f.cache,tn(n,wt,c),c!==m.cache&&Zm(n,[wt],o,!0))),Dt(i,n,n.pendingProps.children,o),n.child;case 29:throw n.pendingProps}throw Error(s(156,n.tag))}function Di(i){i.flags|=4}function Av(i,n){if(n.type!=="stylesheet"||(n.state.loading&4)!==0)i.flags&=-16777217;else if(i.flags|=16777216,!R0(n)){if(n=Ma.current,n!==null&&((Fe&4194048)===Fe?ri!==null:(Fe&62914560)!==Fe&&(Fe&536870912)===0||n!==ri))throw Co=ap,ob;i.flags|=8192}}function Gc(i,n){n!==null&&(i.flags|=4),i.flags&16384&&(n=i.tag!==22?ay():536870912,i.lanes|=n,os|=n)}function _o(i,n){if(!Ke)switch(i.tailMode){case"hidden":n=i.tail;for(var o=null;n!==null;)n.alternate!==null&&(o=n),n=n.sibling;o===null?i.tail=null:o.sibling=null;break;case"collapsed":o=i.tail;for(var c=null;o!==null;)o.alternate!==null&&(c=o),o=o.sibling;c===null?n||i.tail===null?i.tail=null:i.tail.sibling=null:c.sibling=null}}function lt(i){var n=i.alternate!==null&&i.alternate.child===i.child,o=0,c=0;if(n)for(var m=i.child;m!==null;)o|=m.lanes|m.childLanes,c|=m.subtreeFlags&65011712,c|=m.flags&65011712,m.return=i,m=m.sibling;else 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m=n.type;if(i!==null&&n.stateNode!=null)i.memoizedProps!==c&&Di(n);else{if(!c){if(n.stateNode===null)throw Error(s(166));return lt(n),null}i=re.current,wo(n)?eb(n):(i=A0(m,c,o),n.stateNode=i,Di(n))}return lt(n),null;case 5:if(Da(n),o=n.type,i!==null&&n.stateNode!=null)i.memoizedProps!==c&&Di(n);else{if(!c){if(n.stateNode===null)throw Error(s(166));return lt(n),null}if(i=re.current,wo(n))eb(n);else{switch(m=id(te.current),i){case 1:i=m.createElementNS("http://www.w3.org/2000/svg",o);break;case 2:i=m.createElementNS("http://www.w3.org/1998/Math/MathML",o);break;default:switch(o){case"svg":i=m.createElementNS("http://www.w3.org/2000/svg",o);break;case"math":i=m.createElementNS("http://www.w3.org/1998/Math/MathML",o);break;case"script":i=m.createElement("div"),i.innerHTML="<script><\/script>",i=i.removeChild(i.firstChild);break;case"select":i=typeof 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fetch(t,a){if(this.state.fetchStatus!=="idle"&&this.#i?.status()!=="rejected"){if(this.state.data!==void 0&&a?.cancelRefetch)this.cancel({silent:!0});else if(this.#i)return this.#i.continueRetry(),this.#i.promise}if(t&&this.setOptions(t),!this.options.queryFn){const g=this.observers.find(h=>h.options.queryFn);g&&this.setOptions(g.options)}const r=new AbortController,s=g=>{Object.defineProperty(g,"signal",{enumerable:!0,get:()=>(this.#s=!0,r.signal)})},l=()=>{const g=kj(this.options,a),x=(()=>{const b={client:this.#r,queryKey:this.queryKey,meta:this.meta};return s(b),b})();return this.#s=!1,this.options.persister?this.options.persister(g,x,this):g(x)},u=(()=>{const g={fetchOptions:a,options:this.options,queryKey:this.queryKey,client:this.#r,state:this.state,fetchFn:l};return 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this.state.data}}throw this.#o({type:"error",error:g}),this.#n.config.onError?.(g,this),this.#n.config.onSettled?.(this.state.data,g,this),g}finally{this.scheduleGc()}}#o(t){const a=r=>{switch(t.type){case"failed":return{...r,fetchFailureCount:t.failureCount,fetchFailureReason:t.error};case"pause":return{...r,fetchStatus:"paused"};case"continue":return{...r,fetchStatus:"fetching"};case"fetch":return{...r,...OC(r.data,this.options),fetchMeta:t.meta??null};case"success":const s={...r,...hw(t.data,t.dataUpdatedAt),dataUpdateCount:r.dataUpdateCount+1,...!t.manual&&{fetchStatus:"idle",fetchFailureCount:0,fetchFailureReason:null}};return this.#a=t.manual?s:void 0,s;case"error":const l=t.error;return{...r,error:l,errorUpdateCount:r.errorUpdateCount+1,errorUpdatedAt:Date.now(),fetchFailureCount:r.fetchFailureCount+1,fetchFailureReason:l,fetchStatus:"idle",status:"error",isInvalidated:!0};case"invalidate":return{...r,isInvalidated:!0};case"setState":return{...r,...t.state}}};this.state=a(this.state),$t.batch(()=>{this.observers.forEach(r=>{r.onQueryUpdate()}),this.#n.notify({query:this,type:"updated",action:t})})}};function OC(t,a){return{fetchFailureCount:0,fetchFailureReason:null,fetchStatus:Ij(a.networkMode)?"fetching":"paused",...t===void 0&&{error:null,status:"pending"}}}function hw(t,a){return{data:t,dataUpdatedAt:a??Date.now(),error:null,isInvalidated:!1,status:"success"}}function fw(t){const a=typeof t.initialData=="function"?t.initialData():t.initialData,r=a!==void 0,s=r?typeof 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this.options.meta}addObserver(t){this.#t.includes(t)||(this.#t.push(t),this.clearGcTimeout(),this.#a.notify({type:"observerAdded",mutation:this,observer:t}))}removeObserver(t){this.#t=this.#t.filter(a=>a!==t),this.scheduleGc(),this.#a.notify({type:"observerRemoved",mutation:this,observer:t})}optionalRemove(){this.#t.length||(this.state.status==="pending"?this.scheduleGc():this.#a.remove(this))}continue(){return this.#n?.continue()??this.execute(this.state.variables)}async execute(t){const a=()=>{this.#r({type:"continue"})},r={client:this.#e,meta:this.options.meta,mutationKey:this.options.mutationKey};this.#n=Cj({fn:()=>this.options.mutationFn?this.options.mutationFn(t,r):Promise.reject(new Error("No mutationFn found")),onFail:(d,u)=>{this.#r({type:"failed",failureCount:d,error:u})},onPause:()=>{this.#r({type:"pause"})},onContinue:a,retry:this.options.retry??0,retryDelay:this.options.retryDelay,networkMode:this.options.networkMode,canRun:()=>this.#a.canRun(this)});const s=this.state.status==="pending",l=!this.#n.canStart();try{if(s)a();else{this.#r({type:"pending",variables:t,isPaused:l}),this.#a.config.onMutate&&await this.#a.config.onMutate(t,this,r);const u=await this.options.onMutate?.(t,r);u!==this.state.context&&this.#r({type:"pending",context:u,variables:t,isPaused:l})}const d=await this.#n.start();return await this.#a.config.onSuccess?.(d,t,this.state.context,this,r),await this.options.onSuccess?.(d,t,this.state.context,r),await this.#a.config.onSettled?.(d,null,this.state.variables,this.state.context,this,r),await this.options.onSettled?.(d,null,t,this.state.context,r),this.#r({type:"success",data:d}),d}catch(d){try{await this.#a.config.onError?.(d,t,this.state.context,this,r)}catch(u){Promise.reject(u)}try{await this.options.onError?.(d,t,this.state.context,r)}catch(u){Promise.reject(u)}try{await this.#a.config.onSettled?.(void 0,d,this.state.variables,this.state.context,this,r)}catch(u){Promise.reject(u)}try{await this.options.onSettled?.(void 0,d,t,this.state.context,r)}catch(u){Promise.reject(u)}throw this.#r({type:"error",error:d}),d}finally{this.#a.runNext(this)}}#r(t){const a=r=>{switch(t.type){case"failed":return{...r,failureCount:t.failureCount,failureReason:t.error};case"pause":return{...r,isPaused:!0};case"continue":return{...r,isPaused:!1};case"pending":return{...r,context:t.context,data:void 0,failureCount:0,failureReason:null,error:null,isPaused:t.isPaused,status:"pending",variables:t.variables,submittedAt:Date.now()};case"success":return{...r,data:t.data,failureCount:0,failureReason:null,error:null,status:"success",isPaused:!1};case"error":return{...r,data:void 0,error:t.error,failureCount:r.failureCount+1,failureReason:t.error,isPaused:!1,status:"error"}}};this.state=a(this.state),$t.batch(()=>{this.#t.forEach(r=>{r.onMutationUpdate(t)}),this.#a.notify({mutation:this,type:"updated",action:t})})}};function LC(){return{context:void 0,data:void 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4",key:"1e3is1"}],["path",{d:"M17.598 6.5A3 3 0 1 0 12 5a3 3 0 1 0-5.598 1.5",key:"1gqd8o"}],["path",{d:"M17.997 5.125a4 4 0 0 1 2.526 5.77",key:"iwvgf7"}],["path",{d:"M18 18a4 4 0 0 0 2-7.464",key:"efp6ie"}],["path",{d:"M19.967 17.483A4 4 0 1 1 12 18a4 4 0 1 1-7.967-.517",key:"1gq6am"}],["path",{d:"M6 18a4 4 0 0 1-2-7.464",key:"k1g0md"}],["path",{d:"M6.003 5.125a4 4 0 0 0-2.526 5.77",key:"q97ue3"}]],be=B("brain",BM);const HM=[["path",{d:"M12 12h.01",key:"1mp3jc"}],["path",{d:"M16 6V4a2 2 0 0 0-2-2h-4a2 2 0 0 0-2 2v2",key:"1ksdt3"}],["path",{d:"M22 13a18.15 18.15 0 0 1-20 0",key:"12hx5q"}],["rect",{width:"20",height:"14",x:"2",y:"6",rx:"2",key:"i6l2r4"}]],GM=B("briefcase-business",HM);const UM=[["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"}]],fN=B("briefcase",UM);const XM=[["path",{d:"M12 20v-9",key:"1qisl0"}],["path",{d:"M14 7a4 4 0 0 1 4 4v3a6 6 0 0 1-12 0v-3a4 4 0 0 1 4-4z",key:"uouzyp"}],["path",{d:"M14.12 3.88 16 2",key:"qol33r"}],["path",{d:"M21 21a4 4 0 0 0-3.81-4",key:"1b0z45"}],["path",{d:"M21 5a4 4 0 0 1-3.55 3.97",key:"5cxbf6"}

9],["path",{d:"M22 13h-4",key:"1jl80f"}],["path",{d:"M3 21a4 4 0 0 1 3.81-4",key:"1fjd4g"}],["path",{d:"M3 5a4 4 0 0 0 3.55 3.97",key:"1d7oge"}],["path",{d:"M6 13H2",key:"82j7cp"}],["path",{d:"m8 2 1.88 1.88",key:"fmnt4t"}],["path",{d:"M9 7.13V6a3 3 0 1 1 6 0v1.13",key:"1vgav8"}]],$M=B("bug",XM);const WM=[["path",{d:"M10 12h4",key:"a56b0p"}],["path",{d:"M10 8h4",key:"1sr2af"}],["path",{d:"M14 21v-3a2 2 0 0 0-4 0v3",key:"1rgiei"}],["path",{d:"M6 10H4a2 2 0 0 0-2 2v7a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2V9a2 2 0 0 0-2-2h-2",key:"secmi2"}],["path",{d:"M6 21V5a2 2 0 0 1 2-2h8a2 2 0 0 1 2 2v16",key:"16ra0t"}]],yi=B("building-2",WM);const KM=[["path",{d:"M12 10h.01",key:"1nrarc"}],["path",{d:"M12 14h.01",key:"1etili"}],["path",{d:"M12 6h.01",key:"1vi96p"}],["path",{d:"M16 10h.01",key:"1m94wz"}],["path",{d:"M16 14h.01",key:"1gbofw"}],["path",{d:"M16 6h.01",key:"1x0f13"}],["path",{d:"M8 10h.01",key:"19clt8"}],["path",{d:"M8 14h.01",key:"6423bh"}],["path",{d:"M8 6h.01",key:"1dz90k"}],["path",{d:"M9 22v-3a1 1 0 0 1 1-1h4a1 1 0 0 1 1 1v3",key:"cabbwy"}],["rect",{x:"4",y:"2",width:"16",height:"20",rx:"2",key:"1uxh74"}]],YM=B("building",KM);const QM=[["path",{d:"M17 19a1 1 0 0 1-1-1v-2a2 2 0 0 1 2-2h2a2 2 0 0 1 2 2v2a1 1 0 0 1-1 1z",key:"trhst0"}],["path",{d:"M17 21v-2",key:"ds4u3f"}],["path",{d:"M19 14V6.5a1 1 0 0 0-7 0v11a1 1 0 0 1-7 0V10",key:"1mo9zo"}],["path",{d:"M21 21v-2",key:"eo0ou"}],["path",{d:"M3 5V3",key:"1k5hjh"}],["path",{d:"M4 10a2 2 0 0 1-2-2V6a1 1 0 0 1 1-1h4a1 1 0 0 1 1 1v2a2 2 0 0 1-2 2z",key:"1dd30t"}],["path",{d:"M7 5V3",key:"1t1388"}]],ZM=B("cable",QM);const JM=[["path",{d:"M16 14v2.2l1.6 1",key:"fo4ql5"}],["path",{d:"M16 2v4",key:"4m81vk"}],["path",{d:"M21 7.5V6a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h3.5",key:"1osxxc"}],["path",{d:"M3 10h5",key:"r794hk"}],["path",{d:"M8 2v4",key:"1cmpym"}],["circle",{cx:"16",cy:"16",r:"6",key:"qoo3c4"}]],Zs=B("calendar-clock",JM);const eT=[["path",{d:"M8 2v4",key:"1cmpym"}],["path",{d:"M16 2v4",key:"4m81vk"}],["rect",{width:"18",height:"18",x:"3",y:"4",rx:"2",key:"1hopcy"}],["path",{d:"M3 10h18",key:"8toen8"}]],gN=B("calendar",eT);const tT=[["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"}]],$e=B("chart-column",tT);const aT=[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"m19 9-5 5-4-4-3 3",key:"2osh9i"}]],Ug=B("chart-line",aT);const iT=[["path",{d:"M21 12c.552 0 1.005-.449.95-.998a10 10 0 0 0-8.953-8.951c-.55-.055-.998.398-.998.95v8a1 1 0 0 0 1 1z",key:"pzmjnu"}],["path",{d:"M21.21 15.89A10 10 0 1 1 8 2.83",key:"k2fpak"}]],nT=B("chart-pie",iT);const rT=[["path",{d:"m6 9 6 6 6-6",key:"qrunsl"}]],na=B("chevron-down",rT);const sT=[["path",{d:"m9 18 6-6-6-6",key:"mthhwq"}]],Ve=B("chevron-right",sT);const oT=[["path",{d:"m18 15-6-6-6 6",key:"153udz"}]],lT=B("chevron-up",oT);const cT=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["line",{x1:"12",x2:"12",y1:"8",y2:"12",key:"1pkeuh"}],["line",{x1:"12",x2:"12.01",y1:"16",y2:"16",key:"4dfq90"}]],dT=B("circle-alert",cT);const uT=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"m9 12 2 2 4-4",key:"dzmm74"}]],Me=B("circle-check",uT);const mT=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"M16 8h-6a2 2 0 1 0 0 4h4a2 2 0 1 1 0 4H8",key:"1h4pet"}],["path",{d:"M12 18V6",key:"zqpxq5"}]],xN=B("circle-dollar-sign",mT);const pT=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["circle",{cx:"12",cy:"12",r:"1",key:"41hilf"}]],hT=B("circle-dot",pT);const fT=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"M9.09 9a3 3 0 0 1 5.83 1c0 2-3 3-3 3",key:"1u773s"}],["path",{d:"M12 17h.01",key:"p32p05"}]],gT=B("circle-question-mark",fT);const xT=[["path",{d:"M18 20a6 6 0 0 0-12 0",key:"1qehca"}],["circle",{cx:"12",cy:"10",r:"4",key:"1h16sb"}],["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}]],yN=B("circle-user-round",xT);const yT=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"m15 9-6 6",key:"1uzhvr"}],["path",{d:"m9 9 6 6",key:"z0biqf"}]],bT=B("circle-x",yT);const vT=[["rect",{width:"18",height:"18",x:"3",y:"3",rx:"2",key:"afitv7"}],["path",{d:"M11 9h4a2 2 0 0 0 2-2V3",key:"1ve2rv"}],["circle",{cx:"9",cy:"9",r:"2",key:"af1f0g"}],["path",{d:"M7 21v-4a2 2 0 0 1 2-2h4",key:"1fwkro"}],["circle",{cx:"15",cy:"15",r:"2",key:"3i40o0"}]],wT=B("circuit-board",vT);

9const jT=[["rect",{width:"8",height:"4",x:"8",y:"2",rx:"1",ry:"1",key:"tgr4d6"}],["path",{d:"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2",key:"116196"}],["path",{d:"m9 14 2 2 4-4",key:"df797q"}]],Pt=B("clipboard-check",jT);const NT=[["rect",{width:"8",height:"4",x:"8",y:"2",rx:"1",ry:"1",key:"tgr4d6"}],["path",{d:"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2",key:"116196"}],["path",{d:"M12 11h4",key:"1jrz19"}],["path",{d:"M12 16h4",key:"n85exb"}],["path",{d:"M8 11h.01",key:"1dfujw"}],["path",{d:"M8 16h.01",key:"18s6g9"}]],Xg=B("clipboard-list",NT);const AT=[["path",{d:"M12 6v6l4 2",key:"mmk7yg"}],["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}]],bN=B("clock",AT);const kT=[["path",{d:"M17.5 19H9a7 7 0 1 1 6.71-9h1.79a4.5 4.5 0 1 1 0 9Z",key:"p7xjir"}]],Gi=B("cloud",kT);const ST=[["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"}]],wi=B("code-xml",ST);const IT=[["path",{d:"m16 18 6-6-6-6",key:"eg8j8"}],["path",{d:"m8 6-6 6 6 6",key:"ppft3o"}]],CT=B("code",IT);const MT=[["path",{d:"m16.24 7.76-1.804 5.411a2 2 0 0 1-1.265 1.265L7.76 16.24l1.804-5.411a2 2 0 0 1 1.265-1.265z",key:"9ktpf1"}],["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}]],Hl=B("compass",MT);const TT=[["path",{d:"M12 20v2",key:"1lh1kg"}],["path",{d:"M12 2v2",key:"tus03m"}],["path",{d:"M17 20v2",key:"1rnc9c"}],["path",{d:"M17 2v2",key:"11trls"}],["path",{d:"M2 12h2",key:"1t8f8n"}],["path",{d:"M2 17h2",key:"7oei6x"}],["path",{d:"M2 7h2",key:"asdhe0"}],["path",{d:"M20 12h2",key:"1q8mjw"}],["path",{d:"M20 17h2",key:"1fpfkl"}],["path",{d:"M20 7h2",key:"1o8tra"}],["path",{d:"M7 20v2",key:"4gnj0m"}],["path",{d:"M7 2v2",key:"1i4yhu"}],["rect",{x:"4",y:"4",width:"16",height:"16",rx:"2",key:"1vbyd7"}],["rect",{x:"8",y:"8",width:"8",height:"8",rx:"1",key:"z9xiuo"}]],Xa=B("cpu",TT);const ET=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["line",{x1:"22",x2:"18",y1:"12",y2:"12",key:"l9bcsi"}],["line",{x1:"6",x2:"2",y1:"12",y2:"12",key:"13hhkx"}],["line",{x1:"12",x2:"12",y1:"6",y2:"2",key:"10w3f3"}],["line",{x1:"12",x2:"12",y1:"22",y2:"18",key:"15g9kq"}]],RT=B("crosshair",ET);const PT=[["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"}]],Ot=B("database",PT);const OT=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["circle",{cx:"12",cy:"12",r:"2",key:"1c9p78"}]],DT=B("disc",OT);const LT=[["line",{x1:"12",x2:"12",y1:"2",y2:"22",key:"7eqyqh"}],["path",{d:"M17 5H9.5a3.5 3.5 0 0 0 0 7h5a3.5 3.5 0 0 1 0 7H6",key:"1b0p4s"}]],Gl=B("dollar-sign",LT);const VT=[["path",{d:"M12 15V3",key:"m9g1x1"}],["path",{d:"M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4",key:"ih7n3h"}],["path",{d:"m7 10 5 5 5-5",key:"brsn70"}]],_T=B("download",VT);const zT=[["path",{d:"M21.54 15H17a2 2 0 0 0-2 2v4.54",key:"1djwo0"}],["path",{d:"M7 3.34V5a3 3 0 0 0 3 3a2 2 0 0 1 2 2c0 1.1.9 2 2 2a2 2 0 0 0 2-2c0-1.1.9-2 2-2h3.17",key:"1tzkfa"}],["path",{d:"M11 21.95V18a2 2 0 0 0-2-2a2 2 0 0 1-2-2v-1a2 2 0 0 0-2-2H2.05",key:"14pb5j"}],["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}]],Ui=B("earth",zT);const qT=[["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"}]],vN=B("external-link",qT);const FT=[["path",{d:"M10.733 5.076a10.744 10.744 0 0 1 11.205 6.575 1 1 0 0 1 0 .696 10.747 10.747 0 0 1-1.444 2.49",key:"ct8e1f"}],["path",{d:"M14.084 14.158a3 3 0 0 1-4.242-4.242",key:"151rxh"}],["path",{d:"M17.479 17.499a10.75 10.75 0 0 1-15.417-5.151 1 1 0 0 1 0-.696 10.75 10.75 0 0 1 4.446-5.143",key:"13bj9a"}],["path",{d:"m2 2 20 20",key:"1ooewy"}]],wN=B("eye-off",FT);const BT=[["path",{d:"M12 16h.01",key:"1drbdi"}],["path",{d:"M16 16h.01",key:"1f9h7w"}],["path",{d:"M3 19a2 2 0 0 0 2 2h14a2 2 0 0 0 2-2V8.5a.5.5 0 0 0-.769-.422l-4.462 2.844A.5.5 0 0 1 15 10.5v-2a.5.5 0 0 0-.769-.422L9.77 10.922A.5.5 0 0 1 9 10.5V5a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2z",key:"1iv0i2"}],["path",{d:"M8 16h.01",key:"18s6g9"}]],HT=B("factory",BT);const GT=[["path",{d:"M2.062 12.348a1 1 0 0 1 0-.696 10.75 10.75 0 0 1 19.876 0 1 1 0 0 1 0 .696 10.75 10.75 0 0 1-19.876 0",key:"1nclc0"}],["circle",{cx:"12",cy:"12",r:"3",key:"1v7zrd"}]],Xi=B("eye",GT);const UT=[["path",{d:"M4 22h14a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v4",key:"1pf5j1"}],["path",{d:"M14 2v4a2 2 0 0 0 2 2h4",key:"tnqrlb"}],["path",{d:"m3 15 2 2 4-4",key:"1lhrkk"}]],Ya=B("file-check-2",UT);const XT=[["path",{d:"M4 22h14a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v4",key:"1pf5j1"}],["path",{d:"M14 2v4a2 2 0 0 0 2 2h4",key:"tnqrlb"}],["path",{d:"m5 12-3 3 3 3",key:"oke12k"}],["path",{d:"m9 18 3-3-3-3",key:"112psh"}]],Hu=B("file-code-2",XT);const $T=[["path",{d:"M14 2v4a2 2 0 0 0 2 2h4",key:"tnqrlb"}],["path",{d:"m2.305 15.53.923-.382",key:"yfp9st"}],["path",{d:"m3.228 12.852-.924-.383",key:"bckynb"}],["path",{d:"M4.677 21.5a2 2 0 0 0 1.313.5H18a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v2.5",key:"1yo3oz"}],["path",{d:"m4.852 11.228-.383-.923",key:"1j88i9"}],["path",{d:"m4.852 16.772-.383.924",key:"sag1dv"}],["path",{d:"m7.148 11.228.383-.923",key:"rj39hk"}],["path",{d:"m7.53 17.696-.382-.924",key:"1uu5cs"}],["path",{d:"m8.772 12.852.923-.383",key:"13811l"}],["path",{d:"m8.772 15.148.923.383",key:"z1a5l0"}],["circle",{cx:"6",cy:"14",r:"3",key:"a1xfv6"}]],WT=B("file-cog",$T);const KT=[["path",{d:"M4 22h14a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v1",key:"jmtmu2"}],["path",{d:"M14 2v4a2 2 0 0 0 2 2h4",key:"tnqrlb"}

9],["rect",{width:"8",height:"5",x:"2",y:"13",rx:"1",key:"10y5wo"}],["path",{d:"M8 13v-2a2 2 0 1 0-4 0v2",key:"1pdxzg"}]],jN=B("file-lock-2",KT);const YT=[["path",{d:"m18 5-2.414-2.414A2 2 0 0 0 14.172 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2",key:"142zxg"}],["path",{d:"M21.378 12.626a1 1 0 0 0-3.004-3.004l-4.01 4.012a2 2 0 0 0-.506.854l-.837 2.87a.5.5 0 0 0 .62.62l2.87-.837a2 2 0 0 0 .854-.506z",key:"2t3380"}],["path",{d:"M8 18h1",key:"13wk12"}]],$g=B("file-pen-line",YT);const QT=[["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"}],["circle",{cx:"11.5",cy:"14.5",r:"2.5",key:"1bq0ko"}],["path",{d:"M13.3 16.3 15 18",key:"2quom7"}]],NN=B("file-search-2",QT);const ZT=[["path",{d:"M14 2v4a2 2 0 0 0 2 2h4",key:"tnqrlb"}],["path",{d:"M4.268 21a2 2 0 0 0 1.727 1H18a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v3",key:"ms7g94"}],["path",{d:"m9 18-1.5-1.5",key:"1j6qii"}],["circle",{cx:"5",cy:"14",r:"3",key:"ufru5t"}]],JT=B("file-search",ZT);const eE=[["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"}]],Ht=B("file-text",eE);const tE=[["path",{d:"M12 10a2 2 0 0 0-2 2c0 1.02-.1 2.51-.26 4",key:"1nerag"}],["path",{d:"M14 13.12c0 2.38 0 6.38-1 8.88",key:"o46ks0"}],["path",{d:"M17.29 21.02c.12-.6.43-2.3.5-3.02",key:"ptglia"}],["path",{d:"M2 12a10 10 0 0 1 18-6",key:"ydlgp0"}],["path",{d:"M2 16h.01",key:"1gqxmh"}],["path",{d:"M21.8 16c.2-2 .131-5.354 0-6",key:"drycrb"}],["path",{d:"M5 19.5C5.5 18 6 15 6 12a6 6 0 0 1 .34-2",key:"1tidbn"}],["path",{d:"M8.65 22c.21-.66.45-1.32.57-2",key:"13wd9y"}],["path",{d:"M9 6.8a6 6 0 0 1 9 5.2v2",key:"1fr1j5"}]],AN=B("fingerprint",tE);const aE=[["path",{d:"M14 2v6a2 2 0 0 0 .245.96l5.51 10.08A2 2 0 0 1 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DE=[["rect",{width:"7",height:"7",x:"3",y:"3",rx:"1",key:"1g98yp"}],["rect",{width:"7",height:"7",x:"14",y:"3",rx:"1",key:"6d4xhi"}],["rect",{width:"7",height:"7",x:"14",y:"14",rx:"1",key:"nxv5o0"}],["rect",{width:"7",height:"7",x:"3",y:"14",rx:"1",key:"1bb6yr"}]],Ki=B("layout-grid",DE);const LE=[["path",{d:"M11 20A7 7 0 0 1 9.8 6.1C15.5 5 17 4.48 19 2c1 2 2 4.18 2 8 0 5.5-4.78 10-10 10Z",key:"nnexq3"}],["path",{d:"M2 21c0-3 1.85-5.36 5.08-6C9.5 14.52 12 13 13 12",key:"mt58a7"}]],MN=B("leaf",LE);const VE=[["path",{d:"M15 14c.2-1 .7-1.7 1.5-2.5 1-.9 1.5-2.2 1.5-3.5A6 6 0 0 0 6 8c0 1 .2 2.2 1.5 3.5.7.7 1.3 1.5 1.5 2.5",key:"1gvzjb"}],["path",{d:"M9 18h6",key:"x1upvd"}],["path",{d:"M10 22h4",key:"ceow96"}]],TN=B("lightbulb",VE);const _E=[["path",{d:"M9 17H7A5 5 0 0 1 7 7h2",key:"8i5ue5"}],["path",{d:"M15 7h2a5 5 0 1 1 0 10h-2",key:"1b9ql8"}],["line",{x1:"8",x2:"16",y1:"12",y2:"12",key:"1jonct"}]],zE=B("link-2",_E);const qE=[["path",{d:"M16 8a6 6 0 0 1 6 6v7h-4v-7a2 2 0 0 0-2-2 2 2 0 0 0-2 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WE=[["path",{d:"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0",key:"1r0f0z"}],["circle",{cx:"12",cy:"10",r:"3",key:"ilqhr7"}]],Kg=B("map-pin",WE);const KE=[["path",{d:"M14.106 5.553a2 2 0 0 0 1.788 0l3.659-1.83A1 1 0 0 1 21 4.619v12.764a1 1 0 0 1-.553.894l-4.553 2.277a2 2 0 0 1-1.788 0l-4.212-2.106a2 2 0 0 0-1.788 0l-3.659 1.83A1 1 0 0 1 3 19.381V6.618a1 1 0 0 1 .553-.894l4.553-2.277a2 2 0 0 1 1.788 0z",key:"169xi5"}],["path",{d:"M15 5.764v15",key:"1pn4in"}],["path",{d:"M9 3.236v15",key:"1uimfh"}]],YE=B("map",KE);const QE=[["path",{d:"M11 6a13 13 0 0 0 8.4-2.8A1 1 0 0 1 21 4v12a1 1 0 0 1-1.6.8A13 13 0 0 0 11 14H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2z",key:"q8bfy3"}],["path",{d:"M6 14a12 12 0 0 0 2.4 7.2 2 2 0 0 0 3.2-2.4A8 8 0 0 1 10 14",key:"1853fq"}],["path",{d:"M8 6v8",key:"15ugcq"}]],ZE=B("megaphone",QE);const JE=[["path",{d:"M4 5h16",key:"1tepv9"}],["path",{d:"M4 12h16",key:"1lakjw"}],["path",{d:"M4 19h16",key:"1djgab"}]],eR=B("menu",JE);const tR=[["path",{d:"M2.992 16.342a2 2 0 0 1 .094 1.167l-1.065 3.29a1 1 0 0 0 1.236 1.168l3.413-.998a2 2 0 0 1 1.099.092 10 10 0 1 0-4.777-4.719",key:"1sd12s"}]],aR=B("message-circle",tR);const iR=[["path",{d:"M22 17a2 2 0 0 1-2 2H6.828a2 2 0 0 0-1.414.586l-2.202 2.202A.71.71 0 0 1 2 21.286V5a2 2 0 0 1 2-2h16a2 2 0 0 1 2 2z",key:"18887p"}],["path",{d:"M7.5 9.5c0 .687.265 1.383.697 1.844l3.009 3.264a1.14 1.14 0 0 0 .407.314 1 1 0 0 0 .783-.004 1.14 1.14 0 0 0 .398-.31l3.008-3.264A2.77 2.77 0 0 0 16.5 9.5 2.5 2.5 0 0 0 12 8a2.5 2.5 0 0 0-4.5 1.5",key:"1faxuh"}]],nR=B("message-square-heart",iR);const rR=[["path",{d:"M22 17a2 2 0 0 1-2 2H6.828a2 2 0 0 0-1.414.586l-2.202 2.202A.71.71 0 0 1 2 21.286V5a2 2 0 0 1 2-2h16a2 2 0 0 1 2 2z",key:"18887p"}]],ra=B("message-square",rR);const sR=[["path",{d:"M12 19v3",key:"npa21l"}],["path",{d:"M15 9.34V5a3 3 0 0 0-5.68-1.33",key:"1gzdoj"}],["path",{d:"M16.95 16.95A7 7 0 0 1 5 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9],["path",{d:"M3 7h2c2 0 5-1 7-2 2 1 5 2 7 2h2",key:"3gwbw2"}]],ON=B("scale",HR);const GR=[["path",{d:"M15 12h-5",key:"r7krc0"}],["path",{d:"M15 8h-5",key:"1khuty"}],["path",{d:"M19 17V5a2 2 0 0 0-2-2H4",key:"zz82l3"}],["path",{d:"M8 21h12a2 2 0 0 0 2-2v-1a1 1 0 0 0-1-1H11a1 1 0 0 0-1 1v1a2 2 0 1 1-4 0V5a2 2 0 1 0-4 0v2a1 1 0 0 0 1 1h3",key:"1ph1d7"}]],uu=B("scroll-text",GR);const UR=[["path",{d:"m21 21-4.34-4.34",key:"14j7rj"}],["circle",{cx:"11",cy:"11",r:"8",key:"4ej97u"}]],Gt=B("search",UR);const XR=[["path",{d:"M14.536 21.686a.5.5 0 0 0 .937-.024l6.5-19a.496.496 0 0 0-.635-.635l-19 6.5a.5.5 0 0 0-.024.937l7.93 3.18a2 2 0 0 1 1.112 1.11z",key:"1ffxy3"}],["path",{d:"m21.854 2.147-10.94 10.939",key:"12cjpa"}]],DN=B("send",XR);const $R=[["rect",{width:"20",height:"8",x:"2",y:"2",rx:"2",ry:"2",key:"ngkwjq"}],["rect",{width:"20",height:"8",x:"2",y:"14",rx:"2",ry:"2",key:"iecqi9"}],["line",{x1:"6",x2:"6.01",y1:"6",y2:"6",key:"16zg32"}],["line",{x1:"6",x2:"6.01",y1:"18",y2:"18",key:"nzw8ys"}]],Tn=B("server",$R);const WR=[["path",{d:"M14 17H5",key:"gfn3mx"}],["path",{d:"M19 7h-9",key:"6i9tg"}],["circle",{cx:"17",cy:"17",r:"3",key:"18b49y"}],["circle",{cx:"7",cy:"7",r:"3",key:"dfmy0x"}]],Qg=B("settings-2",WR);const KR=[["path",{d:"M9.671 4.136a2.34 2.34 0 0 1 4.659 0 2.34 2.34 0 0 0 3.319 1.915 2.34 2.34 0 0 1 2.33 4.033 2.34 2.34 0 0 0 0 3.831 2.34 2.34 0 0 1-2.33 4.033 2.34 2.34 0 0 0-3.319 1.915 2.34 2.34 0 0 1-4.659 0 2.34 2.34 0 0 0-3.32-1.915 2.34 2.34 0 0 1-2.33-4.033 2.34 2.34 0 0 0 0-3.831A2.34 2.34 0 0 1 6.35 6.051a2.34 2.34 0 0 0 3.319-1.915",key:"1i5ecw"}],["circle",{cx:"12",cy:"12",r:"3",key:"1v7zrd"}]],kl=B("settings",KR);const YR=[["circle",{cx:"18",cy:"5",r:"3",key:"gq8acd"}],["circle",{cx:"6",cy:"12",r:"3",key:"w7nqdw"}],["circle",{cx:"18",cy:"19",r:"3",key:"1xt0gg"}],["line",{x1:"8.59",x2:"15.42",y1:"13.51",y2:"17.49",key:"47mynk"}],["line",{x1:"15.41",x2:"8.59",y1:"6.51",y2:"10.49",key:"1n3mei"}]],Uu=B("share-2",YR);const QR=[["path",{d:"M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z",key:"oel41y"}],["path",{d:"M12 8v4",key:"1got3b"}],["path",{d:"M12 16h.01",key:"1drbdi"}]],oa=B("shield-alert",QR);const ZR=[["path",{d:"M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z",key:"oel41y"}],["path",{d:"m9 12 2 2 4-4",key:"dzmm74"}]],Be=B("shield-check",ZR);const JR=[["path",{d:"m2 2 20 20",key:"1ooewy"}],["path",{d:"M5 5a1 1 0 0 0-1 1v7c0 5 3.5 7.5 7.67 8.94a1 1 0 0 0 .67.01c2.35-.82 4.48-1.97 5.9-3.71",key:"1jlk70"}],["path",{d:"M9.309 3.652A12.252 12.252 0 0 0 11.24 2.28a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1v7a9.784 9.784 0 0 1-.08 1.264",key:"18rp1v"}]],$f=B("shield-off",JR);const eP=[["path",{d:"M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z",key:"oel41y"}]],We=B("shield",eP);const tP=[["rect",{width:"14",height:"20",x:"5",y:"2",rx:"2",ry:"2",key:"1yt0o3"}],["path",{d:"M12 18h.01",key:"mhygvu"}]],Js=B("smartphone",tP);const aP=[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"M8 14s1.5 2 4 2 4-2 4-2",key:"1y1vjs"}],["line",{x1:"9",x2:"9.01",y1:"9",y2:"9",key:"yxxnd0"}],["line",{x1:"15",x2:"15.01",y1:"9",y2:"9",key:"1p4y9e"}]],Xu=B("smile",aP);const iP=[["path",{d:"M11.017 2.814a1 1 0 0 1 1.966 0l1.051 5.558a2 2 0 0 0 1.594 1.594l5.558 1.051a1 1 0 0 1 0 1.966l-5.558 1.051a2 2 0 0 0-1.594 1.594l-1.051 5.558a1 1 0 0 1-1.966 0l-1.051-5.558a2 2 0 0 0-1.594-1.594l-5.558-1.051a1 1 0 0 1 0-1.966l5.558-1.051a2 2 0 0 0 1.594-1.594z",key:"1s2grr"}],["path",{d:"M20 2v4",key:"1rf3ol"}

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0&&!g)return;const h=this.getTransformTemplate();this.prevTransformTemplateValue=h?h(this.latestValues,""):void 0,this.updateSnapshot(),u&&this.notifyListeners("willUpdate")}update(){if(this.updateScheduled=!1,this.isUpdateBlocked()){const g=this.updateBlockedByResize;this.unblockUpdate(),this.updateBlockedByResize=!1,this.clearAllSnapshots(),g&&this.nodes.forEach(FL),this.nodes.forEach(Y1);return}if(this.animationId<=this.animationCommitId){this.nodes.forEach(Q1);return}this.animationCommitId=this.animationId,this.isUpdating?(this.isUpdating=!1,this.nodes.forEach(BL),this.nodes.forEach(HL),this.nodes.forEach(LL),this.nodes.forEach(VL)):this.nodes.forEach(Q1),this.clearAllSnapshots();const p=Kt.now();Rt.delta=Ja(0,1e3/60,p-Rt.timestamp),Rt.timestamp=p,Rt.isProcessing=!0,$h.update.process(Rt),$h.preRender.process(Rt),$h.render.process(Rt),Rt.isProcessing=!1}didUpdate(){this.updateScheduled||(this.updateScheduled=!0,Ys.read(this.scheduleUpdate))}clearAllSnapshots(){this.nodes.forEach(qL),this.sharedNodes.forEach($L)}scheduleUpdateProjection(){this.projectionUpdateScheduled||(this.projectionUpdateScheduled=!0,Ge.preRender(this.updateProjection,!1,!0))}scheduleCheckAfterUnmount(){Ge.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure(),this.snapshot&&!Yt(this.snapshot.measuredBox.x)&&!Yt(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 g=0;g<this.path.length;g++)this.path[g].updateScroll();const u=this.layout;this.layout=this.measure(!1),this.layoutVersion++,this.layoutCorrected||(this.layoutCorrected=At()),this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox);const{visualElement:p}=this.options;p&&p.notify("LayoutMeasure",this.layout.layoutBox,u?u.layoutBox:void 0)}updateScroll(u="measure"){let p=!!(this.options.layoutScroll&&this.instance);if(this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===u&&(p=!1),p&&this.instance){const g=s(this.instance);this.scroll={animationId:this.root.animationId,phase:u,isRoot:g,offset:r(this.instance),wasRoot:this.scroll?this.scroll.isRoot:g}}}resetTransform(){if(!l)return;const u=this.isLayoutDirty||this.shouldResetTransform||this.options.alwaysMeasureLayout,p=this.projectionDelta&&!t4(this.projectionDelta),g=this.getTransformTemplate(),h=g?g(this.latestValues,""):void 0,x=h!==this.prevTransformTemplateValue;u&&this.instance&&(p||fr(this.latestValues)||x)&&(l(this.instance,h),this.shouldResetTransform=!1,this.scheduleRender())}measure(u=!0){const p=this.measurePageBox();let g=this.removeElementScroll(p);return u&&(g=this.removeTransform(g)),QL(g),{animationId:this.root.animationId,measuredBox:p,layoutBox:g,latestValues:{},source:this.id}}measurePageBox(){const{visualElement:u}=this.options;if(!u)return At();const p=u.measureViewportBox();if(!(this.scroll?.wasRoot||this.path.some(ZL))){const{scroll:h}=this.root;h&&(mi(p.x,h.offset.x),mi(p.y,h.offset.y))}return p}removeElementScroll(u){const p=At();if(za(p,u),this.scroll?.wasRoot)return p;for(let g=0;g<this.path.length;g++){const h=this.path[g],{scroll:x,options:b}=h;h!==this.root&&x&&b.layoutScroll&&(x.wasRoot&&za(p,u),mi(p.x,x.offset.x),mi(p.y,x.offset.y))}return p}applyTransform(u,p=!1,g){const h=g||At();za(h,u);for(let x=0;x<this.path.length;x++){const b=this.path[x];!p&&b.options.layoutScroll&&b.scroll&&b!==b.root&&(mi(h.x,-b.scroll.offset.x),mi(h.y,-b.scroll.offset.y)),fr(b.latestValues)&&eu(h,b.latestValues,b.layout?.layoutBox)}return fr(this.latestValues)&&eu(h,this.latestValues,this.layout?.layoutBox),h}removeTransform(u){const p=At();za(p,u);for(let g=0;g<this.path.length;g++){const h=this.path[g];if(!fr(h.latestValues))continue;let x;h.instance&&(vg(h.latestValues)&&h.updateSnapshot(),x=At(),za(x,h.measurePageBox())),F1(p,h.latestValues,h.snapshot?.layoutBox,x)}return fr(this.latestValues)&&F1(p,this.latestValues),p}setTargetDelta(u){this.targetDelta=u,this.root.scheduleUpdateProjection(),this.isProjectionDirty=!0}setOptions(u){this.options={...this.options,...u,crossfade:u.crossfade!==void 0?u.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!==Rt.timestamp&&this.relativeParent.resolveTargetDelta(!0)}resolveTargetDelta(u=!1){const p=this.getLead();this.isProjectionDirty||(this.isProjectionDirty=p.isProjectionDirty),this.isTransformDirty||(this.isTransformDirty=p.isTransformDirty),this.isSharedProjectionDirty||(this.isSharedProjectionDirty=p.isSharedProjectionDirty);const g=!!this.resumingFrom||this!==p;if(!(u||g&&this.isSharedProjectionDirty||this.isProjectionDirty||this.parent?.isProjectionDirty||this.attemptToResolveRelativeTarget||this.root.updateBlockedByResize))return;const{layout:x,layoutId:b}=this.options;if(!this.layout||!(x||b))return;this.resolvedRelativeTargetAt=Rt.timestamp;const w=this.getClosestProjectingParent();w&&this.linkedParentVersion!==w.layoutVersion&&!w.options.layoutRoot&&this.removeRelativeTarget(),!this.targetDelta&&!this.relativeTarget&&(this.options.layoutAnchor!==!1&&w&&w.layout?this.createRelativeTarget(w,this.layout.layoutBox,w.layout.layoutBox):this.removeRelativeTarget()),!(!this.relativeTarget&&!this.targetDelta)&&(this.target||(this.target=At(),this.targetWithTransforms=At()),this.relativeTarget&&this.relativeTargetOrigin&&this.relativeParent&&this.relativeParent.target?(this.forceRelativeParentToResolveTarget(),bL(this.target,this.relativeTarget,this.relativeParent.target,this.options.layoutAnchor||void 0)):this.targetDelta?(this.resumingFrom?this.applyTransform(this.layout.layoutBox,!1,this.target):za(this.target,this.layout.layoutBox),Hk(this.target,this.targetDelta)):za(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget&&(this.attemptToResolveRelativeTarget=!1,this.options.layoutAnchor!==!1&&w&&!!w.resumingFrom==!!this.resumingFrom&&!w.options.layoutScroll&&w.target&&this.animationProgress!==1?this.createRelativeTarget(w,this.target,w.target):this.relativeParent=this.relativeTarget=void 0))}getClosestProjectingParent(){if(!(!this.parent||vg(this.parent.latestValues)||Bk(this.parent.latestValues)))return this.parent.isProjecting()?this.parent:this.parent.getClosestProjectingParent()}isProjecting(){return!!((this.relativeTarget||this.targetDelta||this.options.layoutRoot)&&this.layout)}createRelativeTarget(u,p,g){this.relativeParent=u,this.linkedParentVersion=u.layoutVersion,this.forceRelativeParentToResolveTarget(),this.relativeTarget=At(),this.relativeTargetOrigin=At(),ku(this.relativeTargetOrigin,p,g,this.options.layoutAnchor||void 0),za(this.relativeTarget,this.relativeTargetOrigin)}removeRelativeTarget(){this.relativeParent=this.relativeTarget=void 0}calcProjection(){const u=this.getLead(),p=!!this.resumingFrom||this!==u;let g=!0;if((this.isProjectionDirty||this.parent?.isProjectionDirty)&&(g=!1),p&&(this.isSharedProjectionDirty||this.isTransformDirty)&&(g=!1),this.resolvedRelativeTargetAt===Rt.timestamp&&(g=!1),g)return;const{layout:h,layoutId:x}=this.options;if(this.isTreeAnimating=!!(this.parent&&this.parent.isTreeAnimating||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!(h||x))return;za(this.layoutCorrected,this.layout.layoutBox);const b=this.treeScale.x,w=this.treeScale.y;W9(this.layoutCorrected,this.treeScale,this.path,p),u.layout&&!u.target&&(this.treeScale.x!==1||this.treeScale.y!==1)&&(u.target=u.layout.layoutBox,u.targetWithTransforms=At());const{target:N}=u;if(!N){this.prevProjectionDelta&&(this.createProjectionDeltas(),this.scheduleRender());return}!this.projectionDelta||!this.prevProjectionDelta?this.createProjectionDeltas():(D1(this.prevProjectionDelta.x,this.projectionDelta.x),D1(this.prevProjectionDelta.y,this.projectionDelta.y)),Tl(this.projectionDelta,this.layoutCorrected,N,this.latestValues),(this.treeScale.x!==b||this.treeScale.y!==w||!X1(this.projectionDelta.x,this.prevProjectionDelta.x)||!X1(this.projectionDelta.y,this.prevProjectionDelta.y))&&(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",N))}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(u=!0){if(this.options.visualElement?.scheduleRender(),u){const p=this.getStack();p&&p.scheduleRender()}this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}createProjectionDeltas(){this.prevProjectionDelta=Vs(),this.projectionDelta=Vs(),this.projectionDeltaWithTransform=Vs()}setAnimationOrigin(u,p=!1,g){const h=this.snapshot,x=h?h.latestValues:{},b={...this.latestValues},w=Vs();(!this.relativeParent||!this.relativeParent.options.layoutRoot)&&(this.relativeTarget=this.relativeTargetOrigin=void 0),this.attemptToResolveRelativeTarget=!p;const N=At(),A=h?h.source:void 0,k=this.layout?this.layout.source:void 0,I=A!==k,T=this.getStack(),E=!T||T.members.length<=1,R=!!(I&&!E&&this.options.crossfade===!0&&!this.path.some(KL));this.animationProgress=0;let C;const P=g?.interpolateProjection(u);this.mixTargetDelta=z=>{const V=z/1e3,D=P?.(V);D?(w.x.translate=D.x,w.x.scale=at(u.x.scale,1,V),w.x.origin=u.x.origin,w.x.originPoint=u.x.originPoint,w.y.translate=D.y,w.y.scale=at(u.y.scale,1,V),w.y.origin=u.y.origin,w.y.originPoint=u.y.originPoint):(J1(w.x,u.x,V),J1(w.y,u.y,V)),this.setTargetDelta(w),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(ku(N,this.layout.layoutBox,this.relativeParent.layout.layoutBox,this.options.layoutAnchor||void 0),WL(this.relativeTarget,this.relativeTargetOrigin,N,V),C&&NL(this.relativeTarget,C)&&(this.isProjectionDirty=!1),C||(C=At()),za(C,this.relativeTarget)),I&&(this.animationValues=b,SL(b,x,this.latestValues,V,R,E)),D&&D.rotate!==void 0&&(this.animationValues||(this.animationValues=b),this.animationValues.pathRotation=D.rotate),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=V},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(u){this.notifyListeners("animationStart"),this.currentAnimation?.stop(),this.resumingFrom?.currentAnimation?.stop(),this.pendingAnimation&&(Oa(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=Ge.update(()=>{au.hasAnimatedSinceResize=!0,this.motionValue||(this.motionValue=Ua(0)),this.motionValue.jump(0,!1),this.currentAnimation=ML(this.motionValue,[0,1e3],{...u,velocity:0,isSync:!0,onUpdate:p=>{this.mixTargetDelta(p),u.onUpdate&&u.onUpdate(p)},onStop:()=>{},onComplete:()=>{u.onComplete&&u.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 u=this.getStack();u&&u.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(OL),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const u=this.getLead();let{targetWithTransforms:p,target:g,layout:h,latestValues:x}=u;if(!(!p||!g||!h)){if(this!==u&&this.layout&&h&&o4(this.options.animationType,this.layout.layoutBox,h.layoutBox)){g=this.target||At();const b=Yt(this.layout.layoutBox.x);g.x.min=u.target.x.min,g.x.max=g.x.min+b;const w=Yt(this.layout.layoutBox.y);g.y.min=u.target.y.min,g.y.max=g.y.min+w}za(p,g),eu(p,x),Tl(this.projectionDeltaWithTransform,this.layoutCorrected,p,x)}}registerSharedNode(u,p){this.sharedNodes.has(u)||this.sharedNodes.set(u,new PL),this.sharedNodes.get(u).add(p);const h=p.options.initialPromotionConfig;p.promote({transition:h?h.transition:void 0,preserveFollowOpacity:h&&h.shouldPreserveFollowOpacity?h.shouldPreserveFollowOpacity(p):void 0})}isLead(){const u=this.getStack();return u?u.lead===this:!0}getLead(){const{layoutId:u}=this.options;return u?this.getStack()?.lead||this:this}getPrevLead(){const{layoutId:u}=this.options;return u?this.getStack()?.prevLead:void 0}getStack(){const{layoutId:u}=this.options;if(u)return this.root.sharedNodes.get(u)}promote({needsReset:u,transition:p,preserveFollowOpacity:g}={}){const h=this.getStack();h&&h.promote(this,g),u&&(this.projectionDelta=void 0,this.needsReset=!0),p&&this.setOptions({transition:p})}relegate(){const u=this.getStack();return u?u.relegate(this):!1}resetSkewAndRotation(){const{visualElement:u}=this.options;if(!u)return;let p=!1;const{latestValues:g}=u;if((g.z||g.rotate||g.rotateX||g.rotateY||g.rotateZ||g.skewX||g.skewY)&&(p=!0),!p)return;const h={};g.z&&af("z",u,h,this.animationValues);for(let x=0;x<tf.length;x++)af(`rotate${tf[x]}`,u,h,this.animationValues),af(`skew${tf[x]}`,u,h,this.animationValues);u.render();for(const x in h)u.setStaticValue(x,h[x]),this.animationValues&&(this.animationValues[x]=h[x]);u.scheduleRender()}applyProjectionStyles(u,p){if(!this.instance||this.isSVG)return;if(!this.isVisible){u.visibility="hidden";return}const g=this.getTransformTemplate();if(this.needsReset){this.needsReset=!1,u.visibility="",u.opacity="",u.pointerEvents=tu(p?.pointerEvents)||"",u.transform=g?g(this.latestValues,""):"none";return}const h=this.getLead();if(!this.projectionDelta||!this.layout||!h.target){this.options.layoutId&&(u.opacity=this.latestValues.opacity!==void 0?this.latestValues.opacity:1,u.pointerEvents=tu(p?.pointerEvents)||""),this.hasProjected&&!fr(this.latestValues)&&(u.transform=g?g({},""):"none",this.hasProjected=!1);return}u.visibility="";const x=h.animationValues||h.latestValues;this.applyTransformsToTarget();let b=AL(this.projectionDeltaWithTransform,this.treeScale,x);g&&(b=g(x,b)),u.transform=b;const{x:w,y:N}=this.projectionDelta;u.transformOrigin=`${w.origin*100}% ${N.origin*100}% 0`,h.animationValues?u.opacity=h===this?x.opacity??this.latestValues.opacity??1:this.preserveOpacity?this.latestValues.opacity:x.opacityExit:u.opacity=h===this?x.opacity!==void 0?x.opacity:"":x.opacityExit!==void 0?x.opacityExit:0;for(const A in jg){if(x[A]===void 0)continue;const{correct:k,applyTo:I,isCSSVariable:T}=jg[A],E=b==="none"?x[A]:k(x[A],h);if(I){const R=I.length;for(let C=0;C<R;C++)u[I[C]]=E}else T?this.options.visualElement.renderState.vars[A]=E:u[A]=E}this.options.layoutId&&(u.pointerEvents=h===this?tu(p?.pointerEvents)||"":"none")}clearSnapshot(){this.resumeFrom=this.snapshot=void 0}resetTree(){this.root.nodes.forEach(u=>u.currentAnimation?.stop()),this.root.nodes.forEach(Y1),this.root.sharedNodes.clear()}}}function LL(t){t.updateLayout()}function VL(t){const a=t.resumeFrom?.snapshot||t.snapshot;if(t.isLead()&&t.layout&&a&&t.hasListeners("didUpdate")){const{layoutBox:r,measuredBox:s}=t.layout,{animationType:l}=t.options,d=a.source!==t.layout.source;if(l==="size")ui(x=>{const b=d?a.measuredBox[x]:a.layoutBox[x],w=Yt(b);b.min=r[x].min,b.max=b.min+w});else if(l==="x"||l==="y"){const x=l==="x"?"y":"x";Ng(d?a.measuredBox[x]:a.layoutBox[x],r[x])}else o4(l,a.layoutBox,r)&&ui(x=>{const b=d?a.measuredBox[x]:a.layoutBox[x],w=Yt(r[x]);b.max=b.min+w,t.relativeTarget&&!t.currentAnimation&&(t.isProjectionDirty=!0,t.relativeTarget[x].max=t.relativeTarget[x].min+w)});const u=Vs();Tl(u,r,a.layoutBox);const p=Vs();d?Tl(p,t.applyTransform(s,!0),a.measuredBox):Tl(p,r,a.layoutBox);const g=!t4(u);let h=!1;if(!t.resumeFrom){const x=t.getClosestProjectingParent();if(x&&!x.resumeFrom){const{snapshot:b,layout:w}=x;if(b&&w){const N=t.options.layoutAnchor||void 0,A=At();ku(A,a.layoutBox,b.layoutBox,N);const k=At();ku(k,r,w.layoutBox,N),a4(A,k)||(h=!0),x.options.layoutRoot&&(t.relativeTarget=k,t.relativeTargetOrigin=A,t.relativeParent=x)}}}t.notifyListeners("didUpdate",{layout:r,snapshot:a,delta:p,layoutDelta:u,hasLayoutChanged:g,hasRelativeLayoutChanged:h})}else if(t.isLead()){const{onExitComplete:r}=t.options;r&&r()}t.options.transition=void 0}function _L(t){t.parent&&(t.isProjecting()||(t.isProjectionDirty=t.parent.isProjectionDirty),t.isSharedProjectionDirty||(t.isSharedProjectionDirty=!!(t.isProjectionDirty||t.parent.isProjectionDirty||t.parent.isSharedProjectionDirty)),t.isTransformDirty||(t.isTransformDirty=t.parent.isTransformDirty))}function zL(t){t.isProjectionDirty=t.isSharedProjectionDirty=t.isTransformDirty=!1}function qL(t){t.clearSnapshot()}function Y1(t){t.clearMeasurements()}function FL(t){t.isLayoutDirty=!0,t.updateLayout()}function Q1(t){t.isLayoutDirty=!1}function BL(t){t.isAnimationBlocked&&t.layout&&!t.isLayoutDirty&&(t.snapshot=t.layout,t.isLayoutDirty=!0)}function HL(t){const{visualElement:a}=t.options;a&&a.getProps().onBeforeLayoutMeasure&&a.notify("BeforeLayoutMeasure"),t.resetTransform()}function Z1(t){t.finishAnimation(),t.targetDelta=t.relativeTarget=t.target=void 0,t.isProjectionDirty=!0}function GL(t){t.resolveTargetDelta()}function UL(t){t.calcProjection()}

9function XL(t){t.resetSkewAndRotation()}function $L(t){t.removeLeadSnapshot()}function J1(t,a,r){t.translate=at(a.translate,0,r),t.scale=at(a.scale,1,r),t.origin=a.origin,t.originPoint=a.originPoint}function e2(t,a,r,s){t.min=at(a.min,r.min,s),t.max=at(a.max,r.max,s)}function WL(t,a,r,s){e2(t.x,a.x,r.x,s),e2(t.y,a.y,r.y,s)}function KL(t){return t.animationValues&&t.animationValues.opacityExit!==void 0}const YL={duration:.45,ease:[.4,0,.1,1]},t2=t=>typeof navigator<"u"&&navigator.userAgent&&navigator.userAgent.toLowerCase().includes(t),a2=t2("applewebkit/")&&!t2("chrome/")?Math.round:sa;function i2(t){t.min=a2(t.min),t.max=a2(t.max)}function QL(t){i2(t.x),i2(t.y)}function o4(t,a,r){return t==="position"||t==="preserve-aspect"&&!yL(U1(a),U1(r),.2)}function ZL(t){return t!==t.root&&t.scroll?.wasRoot}const JL=s4({attachResizeListener:(t,a)=>ql(t,"resize",a),measureScroll:()=>({x:document.documentElement.scrollLeft||document.body?.scrollLeft||0,y:document.documentElement.scrollTop||document.body?.scrollTop||0}),checkIsScrollRoot:()=>!0}),nf={current:void 0},l4=s4({measureScroll:t=>({x:t.scrollLeft,y:t.scrollTop}),defaultParent:()=>{if(!nf.current){const t=new JL({});t.mount(window),t.setOptions({layoutScroll:!0}),nf.current=t}return nf.current},resetTransform:(t,a)=>{t.style.transform=a!==void 0?a:"none"},checkIsScrollRoot:t=>window.getComputedStyle(t).position==="fixed"}),nm=y.createContext({transformPagePoint:t=>t,isStatic:!1,reducedMotion:"never"});function n2(t,a){if(typeof t=="function")return t(a);t!=null&&(t.current=a)}function eV(...t){return a=>{let r=!1;const s=t.map(l=>{const d=n2(l,a);return!r&&typeof d=="function"&&(r=!0),d});if(r)return()=>{for(let l=0;l<s.length;l++){const d=s[l];typeof d=="function"?d():n2(t[l],null)}}}}function tV(...t){return y.useCallback(eV(...t),t)}class aV extends y.Component{getSnapshotBeforeUpdate(a){const r=this.props.childRef.current;if(Ml(r)&&a.isPresent&&!this.props.isPresent&&this.props.pop!==!1){const s=r.offsetParent,l=Ml(s)&&s.offsetWidth||0,d=Ml(s)&&s.offsetHeight||0,u=getComputedStyle(r),p=this.props.sizeRef.current;p.height=parseFloat(u.height),p.width=parseFloat(u.width),p.top=r.offsetTop,p.left=r.offsetLeft,p.right=l-p.width-p.left,p.bottom=d-p.height-p.top,p.direction=u.direction}return null}componentDidUpdate(){}render(){return this.props.children}}function iV({children:t,isPresent:a,anchorX:r,anchorY:s,root:l,pop:d}){const u=y.useId(),p=y.useRef(null),g=y.useRef({width:0,height:0,top:0,left:0,right:0,bottom:0,direction:"ltr"}),{nonce:h}=y.useContext(nm),x=t.props?.ref??t?.ref,b=tV(p,x);return y.useInsertionEffect(()=>{const{width:w,height:N,top:A,left:k,right:I,bottom:T,direction:E}=g.current;if(a||d===!1||!p.current||!w||!N)return;const R=E==="rtl",C=r==="left"?R?`right: ${I}`:`left: ${k}`:R?`left: ${k}`:`right: ${I}`,P=s==="bottom"?`bottom: ${T}`:`top: ${A}`;p.current.dataset.motionPopId=u;const z=document.createElement("style");h&&(z.nonce=h);const V=l??document.head;return V.appendChild(z),z.sheet&&z.sheet.insertRule(`
10          [data-motion-pop-id="${u}"] {
11            position: absolute !important;
12            width: ${w}px !important;
13            height: ${N}px !important;
14            ${C}px !important;
15            ${P}px !important;
16          }
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l||this.visualElement.getValue(a,this.visualElement.latestValues[a]??0)}snapToCursor(a){ui(r=>{const{drag:s}=this.getProps();if(!Cd(r,s,this.currentDirection))return;const{projection:l}=this.visualElement,d=this.getAxisMotionValue(r);if(l&&l.layout){const{min:u,max:p}=l.layout.layoutBox[r],g=d.get()||0;d.set(a[r]-at(u,p,.5)+g)}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:a,dragConstraints:r}=this.getProps(),{projection:s}=this.visualElement;if(!Ts(r)||!s||!this.constraints)return;this.stopAnimation();const l={x:0,y:0};ui(u=>{const p=this.getAxisMotionValue(u);if(p&&this.constraints!==!1){const g=p.get();l[u]=HV({min:g,max:g},this.constraints[u])}});const{transformTemplate:d}=this.visualElement.getProps();this.visualElement.current.style.transform=d?d({},""):"none",s.root&&s.root.updateScroll(),s.updateLayout(),this.constraints=!1,this.resolveConstraints(),ui(u=>{if(!Cd(u,a,null))return;const 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w=this.getAxisMotionValue(b);w&&(this.originPoint[b]+=h[b].translate,w.set(w.get()+h[b].translate))}),this.visualElement.render())}));return()=>{p(),r(),u(),g&&g(),s&&s()}}getProps(){const a=this.visualElement.getProps(),{drag:r=!1,dragDirectionLock:s=!1,dragPropagation:l=!1,dragConstraints:d=!1,dragElastic:u=Ag,dragMomentum:p=!0}=a;return{...a,drag:r,dragDirectionLock:s,dragPropagation:l,dragConstraints:d,dragElastic:u,dragMomentum:p}}}function g2(t){let a=!0;return()=>{if(a){a=!1;return}t()}}function WV(t,a,r){const s=yg(t,g2(r)),l=yg(a,g2(r));return()=>{s(),l()}}function Cd(t,a,r){return(a===!0||a===t)&&(r===null||r===t)}function KV(t,a=10){let r=null;return Math.abs(t.y)>a?r="y":Math.abs(t.x)>a&&(r="x"),r}class YV extends Fn{constructor(a){super(a),this.removeGroupControls=sa,this.removeListeners=sa,this.controls=new $V(a)}mount(){const{dragControls:a}=this.node.getProps();a&&(this.removeGroupControls=a.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||sa}update(){const{dragControls:a}=this.node.getProps(),{dragControls:r}=this.node.prevProps||{};a!==r&&(this.removeGroupControls(),a&&(this.removeGroupControls=a.subscribe(this.controls)))}unmount(){this.removeGroupControls(),this.removeListeners(),this.controls.isDragging||this.controls.endPanSession()}}const of=t=>(a,r)=>{t&&Ge.update(()=>t(a,r),!1,!0)};class QV extends Fn{constructor(){super(...arguments),this.removePointerDownListener=sa}onPointerDown(a){this.session=new b4(a,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint(),contextWindow:y4(this.node)})}createPanHandlers(){const{onPanSessionStart:a,onPanStart:r,onPan:s,onPanEnd:l}=this.node.getProps();return{onSessionStart:of(a),onStart:of(r),onMove:of(s),onEnd:(d,u)=>{delete this.session,l&&Ge.postRender(()=>l(d,u))}}}mount(){this.removePointerDownListener=El(this.node.current,"pointerdown",a=>this.onPointerDown(a))}update(){this.session&&this.session.updateHandlers(this.createPanHandlers())}unmount(){this.removePointerDownListener(),this.session&&this.session.end()}}let lf=!1;class ZV extends y.Component{componentDidMount(){const{visualElement:a,layoutGroup:r,switchLayoutGroup:s,layoutId:l}=this.props,{projection:d}=a;d&&(r.group&&r.group.add(d),s&&s.register&&l&&s.register(d),lf&&d.root.didUpdate(),d.addEventListener("animationComplete",()=>{this.safeToRemove()}),d.setOptions({...d.options,layoutDependency:this.props.layoutDependency,onExitComplete:()=>this.safeToRemove()})),au.hasEverUpdated=!0}getSnapshotBeforeUpdate(a){const{layoutDependency:r,visualElement:s,drag:l,isPresent:d}=this.props,{projection:u}=s;return u&&(u.isPresent=d,a.layoutDependency!==r&&u.setOptions({...u.options,layoutDependency:r}),lf=!0,l||a.layoutDependency!==r||r===void 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57cture."},{phase:"03",title:"AI Priority pilot",body:"Activate the scoring engine for a representative sample so analysts can calibrate expectations and review score-breakdown logic before broader rollout."},{phase:"04",title:"Governance activation",body:"Configure KEV two-person integrity and finalize exception management workflows to ensure audit compliance before scaling to the full fleet."},{phase:"05",title:"Remediation integration",body:"Introduce ring-based deployment where patches roll out in controlled phases and post-change verification validates stability before expanding scope."},{phase:"06",title:"Continuous improvement",body:"Run regular trend analysis and AI model feedback loops, expand to the full fleet, and finalize executive reporting packages."}],Vq=["KEV prioritization accuracy: verify confirmed-exploitation vulnerabilities surface first, every time.","AI score breakdown verification: confirm every rank is explainable through its seven weighted signals.","Independent exception approval workflow: test two-person integrity on a real KEV-flagged exception.","Campaign remediation deployment: run a ring-based rollout with evidence gates on representative endpoints.","Post-deployment validation: prove which endpoints are verified remediated and which remain exposed."],_q=[{title:"Security",body:"Reduce critical exposure dwell time, increase KEV patch currency, and accelerate patch rate on assets deemed critical to business operations."},{title:"Operations",body:"Improve first-time deployment success, reduce time spent preparing remediation campaigns, and lower MTTR for failed deployments."},{title:"Governance",body:"Increase exception completeness, backed by independent approval coverage and retrievable, audit-ready documentation."},{title:"Leadership",body:"Gain clarity on the trajectory of open critical exposures and risk concentration by service, with far less time spent preparing executive briefings."}],zq=[{title:"Tenable One",body:"Broad scan coverage, mature agent and agentless options, and strong asset inventory. Lumin AI exposure scoring is a recognized standard in regulated industries.",bullets:["AI prioritization transparency varies by edition","Deep remediation orchestration often needs complex manual configuration beyond the platform"]},{title:"Qualys VMDR",body:"Cloud-native architecture with robust multi-cloud visibility and an integrated TruRisk score combining asset criticality with vulnerability severity.",bullets:["Evaluate the depth of AI explainability and per-finding score breakdowns","Confirm native support for KEV-specific two-person integrity workflows"]},{title:"Xpedioo",body:"A unified asset-to-remediation loop with seven-signal explainable AI scoring, built-in KEV two-person integrity, and verified endpoint state monitoring post-remediation.",bullets:["Best fit for enterprises requiring integrated remediation governance","Governance-first design rather than bolt-on compliance reporting"]}],c3=["Complete lifecycle tracking from procurement and deployment through maintenance to decommission and disposal","Automated depreciation and valuation calculations synced with financial systems for audit-ready reporting","Predictive maintenance scheduling that reduces unplanned downtime and unnecessary preventive visits","Real-time location and condition tracking across IT hardware, facilities equipment, and field assets","Native integration with IoT sensors, CMDB, and ITSM for a single source of asset truth","Contract, warranty, and vendor lifecycle management with automatic renewal and expiry alerts"],d3=["AI failure-risk scoring that ranks assets by likelihood and cost impact of failure","Predictive maintenance recommendations generated from usage, sensor telemetry, and historical failure patterns","AI-optimized repair-versus-replace analysis based on total cost of ownership and depreciation curves","Anomaly detection on IoT sensor streams that flags abnormal vibration, temperature, or usage before failure","Natural-language asset reporting for finance, operations, and compliance stakeholders","AI-generated audit and compliance evidence packages mapped to asset class and regulatory framework"],qq=[{heading:"Complete lifecycle visibility",body:"AI Powered Enterprise Asset Management tracks every physical asset from requisition through disposal in one continuous record, instead of separate spreadsheets for procurement, operations, and finance.",bullets:["Capture procurement requests, approvals, purchase orders, and vendor terms at intake","Register every deployed asset with owner, location, cost center, and warranty data","Preserve a single audit trail from acquisition to final disposal or resale"]},{heading:"Predictive maintenance and condition monitoring",body:"IoT sensor telemetry and usage history feed an AI model that flags at-risk assets before they fail, rather than waiting for a fixed calendar interval or a breakdown call.",bullets:["Ingest vibration, temperature, runtime, and usage signals from connected equipment","Rank assets by failure risk and business impact instead of treating every asset equally","Dispatch work orders automatically to the right technician 

57with the right parts context"]},{heading:"Financial accuracy and depreciation",body:"Every asset's book value, depreciation schedule, and disposal value stay synchronized with its real-world condition and location, closing the gap between the physical estate and the finance ledger.",bullets:["Apply configurable depreciation methods by asset class and useful life","Reconcile physical asset counts against financial records automatically","Model repair-versus-replace decisions against total cost of ownership"]},{heading:"Governance, compliance, and audit readiness",body:"Asset transfers, disposals, and financial adjustments move through a governed workflow with owners, evidence, and approvals attached, so audits are a query rather than a fire drill.",bullets:["Track chain-of-custody for every transfer, loan, and location change","Certify data wipe and environmentally compliant disposal for every retired asset","Generate framework-mapped compliance and audit evidence on demand"]}],Fq=[{title:"Spreadsheet-based tracking",body:"Manual registers decay the moment an asset moves. Ownership, location, and condition drift out of sync within weeks, leaving finance and operations working from different realities."},{title:"Reactive maintenance",body:"Without condition or usage signals, teams fix equipment only after it fails — driving up repair costs, unplanned downtime, and safety risk compared to catching the failure early."},{title:"Disconnected financial and physical records",body:"Finance's depreciation ledger and operations' physical inventory are reconciled manually, if at all, producing failed audits and assets that are paid for but no longer exist."},{title:"Blind decommissioning",body:'Assets get retired without a documented data wipe, certified disposal, or ledger closure — creating compliance exposure and "ghost assets" that quietly continue to consume budget.'}],Bq=["Continuous asset discovery combining IoT telemetry, manual audits, and procurement records","Financial and physical asset records unified in one system instead of reconciled after the fact","Explainable AI maintenance and replacement recommendations grounded in visible cost and risk factors","Governance-first change tracking for every asset transfer, disposal, and financial adjustment","Closed-loop verification that proves an asset's recorded state matches its physical and operational reality","Honest reporting that surfaces idle, ghost, and unaccounted-for assets instead of hiding them in aggregate totals"],Hq=[{title:"Acquire",body:"Capture requisitions, approvals, purchase orders, vendor terms, and warranty coverage the moment an asset enters the pipeline."},{title:"Deploy",body:"Assign an owner, location, and cost center, register the asset in the CMDB, and configure IoT tagging or telemetry where applicable."},{title:"Monitor",body:"Track location, condition, and utilization continuously through sensor telemetry, scheduled audits, and self-reported checks."},{title:"Maintain",body:"Schedule preventive and predictive maintenance, dispatch work orders with parts and technician context, and record completion evidence."},{title:"Optimize",body:"Track depreciation and utilization, and weigh repair-versus-replace decisions against total cost of ownership and remaining useful life."},{title:"Retire",body:"Run a governed decommission workflow with certified data wipe, compliant disposal or resale, and final closure of the financial and physical record."}],Gq=[{title:"IT and technology assets",body:"Track laptops, servers, network gear, and peripherals alongside software license and warranty terms, tying every device to its CMDB and ITSM record.",bullets:["Automatic reconciliation with CMDB configuration items and ITAM software records","Refresh-cycle planning based on age, performance, and warranty expiry"]},{title:"Facilities and physical infrastructure",body:"Manage HVAC systems, generators, elevators, and building equipment with IoT-connected condition monitoring and preventive maintenance scheduling.",bullets:["Space and occupancy-aware maintenance planning across sites","Energy and sustainability tracking tied to equipment condition"]},{title:"Mobile and field assets",body:"Track vehicles, field equipment, and tools that move between sites, technicians, and job locations without losing custody or maintenance history.",bullets:["GPS and geofencing-aware location tracking for mobile equipment","Technician-level custody and check-in/check-out history"]}],Uq=[{label:"Failure risk",title:"Predicted likelihood of failure",body:"AI-modeled probability of failure based on sensor telemetry, usage intensity, and historical patterns for similar assets."},{label:"Utilization",title:"Actual usage versus capacity",body:"Identifies overused assets approaching wear limits and underused assets that may be candidates for redeployment."},{label:"Warranty",title:"Coverage and vendor SLA status",body:"Flags assets nearing warranty expiry s

57o repairs and claims happen while still covered, avoiding unnecessary out-of-pocket cost."},{label:"Compliance",title:"Regulatory and safety requirements",body:"Elevates assets subject to safety inspections, certifications, or regulatory deadlines into an accelerated maintenance tier."},{label:"Depreciation",title:"Position on the useful-life curve",body:"Connects book value and remaining useful life to the repair-versus-replace decision instead of treating maintenance as a standalone cost."}],Xq=[{title:"Explainable maintenance scoring",body:"Every maintenance recommendation exposes the contribution of failure risk, utilization, warranty status, compliance urgency, and depreciation position."},{title:"Two-person disposal integrity",body:"High-value or regulated asset disposals require independent second-party approval, a documented reason, and a permanent audit record."},{title:"Depreciation policy as configuration",body:"Map asset classes to depreciation methods and useful-life assumptions that finance can review and adjust as accounting policy evolves."},{title:"Chain-of-custody tracking",body:"Every transfer, loan, and location change is timestamped with the requesting and receiving owner, preventing silent custody gaps."},{title:"Warranty and vendor SLA enforcement",body:"Track vendor response-time commitments and coverage terms, and flag violations automatically instead of relying on manual escalation."},{title:"Evidence and record integrity",body:"Attach photos, invoices, inspection certificates, and maintenance logs directly to each asset record for tamper-evident history."},{title:"Traceable work order objects",body:"Give every maintenance and inspection task a unique identity, assigned technician, parts list, and completion evidence."},{title:"Closed-loop decommission verification",body:"Confirm certified data wipe, compliant disposal or resale, and final ledger closure before an asset record is archived."},{title:"Bounded exceptions and reporting",body:"Require owners, justification, compensating controls, and expiry dates for every deferred maintenance or policy exception."}],$q=[{title:"Multi-factor failure-risk modeling",body:"Synthesize sensor telemetry, maintenance history, and usage intensity into a transparent, ranked failure-risk view per asset."},{title:"Compliance gap remediation",body:"Map asset condition and inspection history against safety and regulatory requirements to generate prioritized remediation plans."},{title:"Condition drift detection",body:"Compare live sensor readings against known-good baselines, classify deviations, and recommend action while keeping human approval in the loop."},{title:"Predictive maintenance windows",body:"Use historical usage and downtime-cost data to recommend maintenance windows that minimize operational disruption."},{title:"Repair-versus-replace proposals",body:"Generate total-cost-of-ownership comparisons with confidence scores to support capital planning decisions."},{title:"Operational copilots",body:"Give technicians asset and parts context in the field while producing finance-ready valuation and depreciation briefings."}],Wq=["Human approval gates remain mandatory for high-value disposals and capital replacement decisions.","Immutable logs capture every AI recommendation, supporting data point, override, and final decision.","Tenant data boundaries and prompt hygiene protect sensitive financial and vendor metadata.","Low-confidence flags surface stale sensor data or incomplete asset records instead of hiding uncertainty.","Natural-language answers link back to governed asset evidence; unsupported answers return no result."],Kq=[{phase:"01",title:"Establish the baseline",body:"Reconcile the physical asset register against financial records to quantify unknown, ghost, and misclassified assets before changing anything."},{phase:"02",title:"Align financial policy",body:"Import depreciation methods and useful-life assumptions by asset class, and validate them against current accounting practice."},{phase:"03",title:"Pilot a connected asset class",body:"Instrument one IoT-eligible asset class with telemetry and validate predictive maintenance recommendations against technician judgment."},{phase:"04",title:"Scale coverage",body:"Extend tracking and predictive maintenance across IT, facilities, and field asset classes, and integrate with CMDB and ITSM."},{phase:"05",title:"Formalize governance",body:"Define repeatable disposal, transfer, and exception workflows with independent approval and automatic expiry."},{phase:"06",title:"Continuously improve",body:"Use trend data and AI refinement cycles to extend useful life, reduce unplanned downtime, and lower total cost of ownership."}],Yq=["Coverage quality: distinguish tracked, stale, and unaccounted-for assets across every class.","Financial accuracy: reconcile physical counts against the depreciation ledger and quantify the gap.","Prioritization: compare AI failure-risk recommendations with technician judgment on real equipment.","Governance: test independent disposal approval and chain-of-custody traceability in reporting.","Maintenance: test work order dispatch, technician context, and completion evidence capture.","Commercial fit: evaluate sensor and integration depth, services, and total operating cost."],Qq=[{title:"Financial",body:"Reduce unaccounted-for and ghost assets, tighten book-value accuracy, and recover budget spent maintaining assets that no longer exist."},{title:"Operations",body:"Reduce unplanned downtime, extend useful asset life, and lower emergency repair costs through predictive maintenance."},{title:"Compliance",body:"Increase inspection and disposal evidence completeness while reducing audit evidence retrieval time."},{title:"Leadership",body:"Track total cost of ownership by asset class, capital replacement forecasts, and time to produce executive-ready briefings."}],u3=["Target population preview showing resolved hostnames, operating systems, Satellite Client availability, and matched-versus-capped counts before every job","Blast-radius percentage control that caps execution to a deliberate share of the resolved population","Dry-run mode that simulates the full job lifecycle — targeting, approval, and completion — with zero endpoint contact","Mandatory pending-approval state with full context (script, parameters, targets, risk) before any job can execute","Role-based approval authority that keeps job creators from approving their own work","Per-endpoint execution results with exit code, stdout, stderr, and completion timestamp for every targeted device","Immutable, chronological audit trail covering every workflow event from creation through completion","Skill library with typed runtime parameters, version history, and built-in versus custom classification"],m3=["AI script generation that turns a plain-language purpose and requirements into a complete PowerShell or Bash draft","Context-aware generation inside the skill editor that automatically inherits the skill's parameters, platform, and tags","AI script explanation that translates any script into a plain-language, step-by-step description for non-coding reviewers","AI security analysis that flags privilege requirements, sensitive operations, and destructive risks before approval","AI script validation that checks syntax, structure, output conventions, and safety flags before a version is saved","Ten built-in AI script templates — System Discovery, Disk Monitoring, Security Audit, Compliance Checks, Backup Status, and more","Compliance-aware generation that adds SOC 2, HIPAA, PCI-DSS, GDPR, or ISO 27001 context directly into the generated script","A tw

57o-step apply-then-save workflow that keeps every AI draft under explicit operator control before it enters the skill library"],Zq=[{heading:"The skill library: from personal scripts to organizational memory",body:"A skill is not just a script — it's a complete, self-describing automation unit with structured metadata that a team can discover, understand, and safely reuse without contacting the original author.",bullets:["Structured metadata for every skill: name, category, platform, risk level, elevation requirement, timeout, and typed parameters","Full-text search, category navigation, and platform filtering across built-in and custom skills","A natural lifecycle — created, reviewed, activated, paused, and revised — with active/paused status always visible"]},{heading:"Writing prompts that produce enterprise-grade scripts",body:"The AI generation model is only as precise as the requirements given to it. A vague purpose statement produces generic output; specific operational constraints produce production-oriented code.",bullets:["State the target condition, the action to take, and the expected end state in the purpose statement","Specify exit code conventions, output format, error handling, and safety constraints as explicit requirements","Reference skill parameters directly (e.g. {{ServiceName}}, {{WaitSeconds}}) so generated code uses correct substitution syntax"]},{heading:"AI-generated PowerShell with enterprise conventions",body:"PowerShell scripts leaving the platform are structured to look like they were written by a senior Windows engineer, not a text model.",bullets:["param() block with typed parameters and defaults, wrapped in try/catch/finally for reliable error handling","Explicit exit codes and Write-Output/Write-Error conventions that the platform's per-endpoint capture can interpret","Standard elevation checks using WindowsIdentity and WindowsPrincipal, plus ConvertTo-Json structured output"]},{heading:"Safe, portable Bash for Linux estates",body:"Linux automation must handle diverse distributions, privilege models, and service frameworks without hard-coded assumptions.",bullets:["set -euo pipefail with a cleanup trap on EXIT, so failed commands never continue silently","Distribution-portable patterns — systemctl for services, command -v for binary checks, no backticks","EUID checks at startup for root-required scripts, with clear stderr messages instead of buried permission errors"]},{heading:"Safety constraints the generation pipeline will not cross",body:"Safety constraints define the boundary between a useful automation assistant and an uncontrolled code generator.",bullets:["Idempotent by default — check current state before making a change, so retries are always safe","No hard-coded credentials, user-specific paths, hostnames, or IP addresses; these become skill parameters instead","Destructive actions require an explicit confirmation or dry-run branch, target specificity, and a documented irreversibility note"]},{heading:"From reactive tickets to proactive employee experience automation",body:"The best support interaction is the one the user never has to initiate because the endpoint already recovered.",bullets:["DEX flows triggered by anomaly detection, a schedule, an external alert, or a service-desk webhook","Automation skills become action nodes in the flow de

57signer, connecting to ServiceNow, Jira, PagerDuty, and Slack","Measured by mean time to automated recovery, ticket deflection rate, and first-contact resolution"]},{heading:"Connecting governed automation to the operational ecosystem",body:"Automation that connects to existing systems becomes part of the operational fabric instead of a separate workflow.",bullets:["Skill-scoped, expiring external API keys with one-time-visible secrets and immediate revocation","Integration patterns for service-desk auto-remediation, monitoring-triggered response, and CI/CD post-deployment verification","Externally triggered jobs still enter the same pending-approval workflow as operator-created jobs"]},{heading:"Automation as a measured operational service",body:"An automation program that cannot report its own effectiveness cannot improve, cannot be funded, and cannot be trusted.",bullets:["Dashboard visibility into total skills, job volume, running jobs, pending approvals, and success versus failure","Skill-level metrics: first-pass success rate, partial-failure rate, approval latency, and execution duration","Business outcome alignment through ticket deflection, technician time saved, and MTTR reduction"]}],Jq=[{title:"Automation fragmentation",body:"A typical enterprise spreads automation across ten or more sources — admin workstations, ticket attachments, GPOs, SCCM, RMM tools, and unclear Git repos — making a basic audit question nearly impossible to answer."},{title:"Scale multiplies blast radius",body:"At 1,000 endpoints, a careless script has 1,000 opportunities to create an incident. Without a target preview, scope and impact are guesses, not decisions."},{title:"The AI risk amplifier",body:"Generative AI makes it faster to produce plausible-looking scripts — which also makes it easier to deploy code that was never reviewed, tested, or authorized."},{title:"The evidence deficit",body:"Remote sessions, ad hoc PowerShell, and untracked scripts produce no usable evidence. When auditors ask what changed last quarter, the absence of records is itself a finding."}],eF=["AI-generated scripts are always visible and editable before they become part of the skill library","Target populations are resolved and previewed before a single command leaves the platform","Dry-run mode lets a job be fully simulated with zero endpoint contact","Approval gates create a separation of duties between the person who creates a job and the person who authorizes it","Per-endpoint results make outcomes explicit — the platform cannot hide the work it does"],tF=[{title:"Create",body:"Define a skill with metadata and a script — name, description, category, platform, risk level, and elevation requirement."},{title:"Parameterize",body:"Define typed runtime parameters and risk attributes so the same governed skill can serve many operational contexts."},{title:"Preview",body:"Resolve and preview the exact target population — hostnames, operating systems, and Satellite Client availability."},{title:"Validate",body:"Optionally run a dry-run simulation that produces a complete job record without touching any endpoint."},{title:"Approve",body:"Submit the job for human approval by a qualified administrator or manager — never the job's own creator."},{title:"Execute & evidence",body:"Dispatch through Satellite Clients, capture per-device results, and preserve the complete audit trail."}],aF=[{title:"Infrastructure & platform operations",body:"The highest-value skills address the conditions generating the most recurring tickets across the endpoint estate.",bullets:["Service recovery, disk capacity management, and DNS/network configuration repair","Windows Update compliance checking, system inventory, and connectivity diagnostics"]},{title:"Application support & recovery",body:"Convert individual administrator expertise into a governed, repeatable capability for the applications that generate the most support volume.",bullets:["IIS app-pool recovery, O365/Teams repair, and SCCM agent recovery","SAP/Java runtime repair, Citrix session cleanup, and backup client verification"]},{title:"Security enforcement",body:"Close the gap between written policy and verified endpoint state with paired detection and remediation skills.",bullets:["Execution-policy enforcement, SMBv1 disablement, and BitLocker verification","Windows Defender repair, local administrator audit, and firewall profile verification"]},{title:"Evidence & compliance collection",body:"Read-only, structured-output skills that produce audit-ready evidence as a byproduct of normal operations.",bullets:["Software inventory, event log extraction, and user/group membership snapshots","Security configuration snapshots, certificate expiry analysis, and scheduled task inventory"]}],iF=[{label:"01",title:"First use of a new skill",body:"Confirm targeting, parameters, and behavior before any real endpoint execution."},{label:"02",title:"Unfamiliar endpoint population",body:"Verify that the resolved target set matches the operator's mental model."},{label:"03",title:"High-risk changes",body:"Give approvers a concrete, simulated record to review before authorizing production impact."},{label:"04",title:"Change management records",body:"Create an RFC-supporting artifact that documents exactly what is planned."},{label:"05",title:"Training new operators",body:"Work through the full job creation and approval workflow without touching production endpoints."}],nF=[{title:"Pending-approval by default",body:"Every job created through the UI or the external API begins in pending_approval and cannot execute without an explicit approval action."},{title:"Role-based approval authority",body:"Approval, rejection, and cancellation are restricted to administrator or manager roles — job creators cannot approve their own work."},{title:"Full-context approval review",body:"Approvers see the script, parameters, resolved targets, blast radius, risk level, elevation requirement, and creator identity before deciding."},{title:"Recorded rejection reasoning",body:"Rejected jobs carry a reason visible to the creator and preserved as institutional guidance for future submissions."},{title:"Immutable audit trail",body:"Every workflow event — created, approved, rejected, execution_started, endpoint_completed, cancelled — is appended and never edited."},{title:"AI explainability as evidence",body:"The AI-generated explanation and security analysis are captured alongside the script for approvers to review."},{title:"Compliance framework alignment",body:"Audit evidence maps to SOC 2 (CC6.8/CC7.2), PCI-DSS (Requirements 6 and 12), ISO 27001 (Annex A.12.1.2), and HIPAA (§164.312(b))."},{title:"Skill-scoped external API keys",body:"Third-party systems receive keys authorized for specific skills only, with expiry dates and one-time-visible secrets."},{title:"Externally initiated jobs stay governed",body:"API-triggered jobs enter the same pending-approval workflow as operator-created jobs — external access cannot bypass governance."}],rF=[{title:"Six-input script generation",body:"Purpose, requirements, script type, target OS, complexity, and security level combine into a complete PowerShell or Bash draft."},{title:"Context-aware generation",body:"Opened from inside the skill editor, the prompt automatically inherits the skill's name, parameters, platform, and tags."},{title:"Plain-language script explanation",body:"Translate any script — AI-written or manually authored — into a step-by-step description for reviewers who don't read code."},{title:"Security analysis on every draft",body:"Surfaces privilege requirements, sensitive operations, destructive risks, and stated assumptions before approval."},{title:"Structured validation before save",body:"Checks syntax errors, structural issues, output conventions, and safety flags, returning explicit errors and warnings."},{title:"Iterative refinement",body:"Switch script types, raise complexity, or add a security-level constraint and regenerate — the platform preserves prior requirements for editing."}],sF=["AI has no access to the execution control plane — it cannot create, approve, or dispatch a job, change a target list, adjust blast radius, or write to the audit trail","Transparency: every generated script and its explanation is always visible to the operator before use","Controllability: every AI output can be edited, rejected, or replaced with manually authored content","Accountability: skill metadata, approval records, and the audit trail document the human decisions made around AI-generated content","A tw

57o-step apply-then-save workflow means generated code only enters the skill library through deliberate operator action"],oF=["Choose a first skill with high volume, clear success criteria, low blast-radius risk, and a documented runbook precedent","Define who can create skills, who approves by risk tier, and the mandatory dry-run policy before the first production job","Run the AI workflow end to end: describe the purpose and requirements, review the script and security analysis, then validate before applying","Pilot the first production job at a 10% blast radius and review every per-endpoint result before expanding to the next ring","Confirm the audit trail captures creator, approver, script, targets, and per-endpoint results without gaps","Track first-pass success rate, approval latency, and rejected-job rate from the first month of production use"],lF=[{title:"Operational efficiency",body:"Teams that automate their top-10 recurring incident categories typically report a 30–50% reduction in technician time on repetitive remediation within six months."},{title:"Risk reduction",body:"Blast-radius controls, dry-run mode, approval gates, and per-endpoint results make automation safer than the manual processes it replaces."},{title:"Compliance readiness",body:"Structured per-endpoint results, approval records, and immutable audit trails turn evidence production into a continuous operational output."},{title:"AI leverage",body:"Senior engineers spend their expertise on requirements and review while AI compresses the scripting phase from hours to minutes."}],cF=["Real-time event deduplication and grouping across connected monitoring and ITSM tools","Topology-aware correlation that merges related service alerts into one prioritized incident","Alert-storm and flap suppression during known incidents and scheduled maintenance windows","Predictive failure scoring from leading log-pattern and event-frequency indicators","Approval-gated auto-remediation playbooks with execution history and rollback visibility","Configurable hot, warm, and cold retention policies with lifecycle automation"],dF=["AI log clustering and template extraction that turns millions of log lines into recurring patterns","ML anomaly detection for log volume, rate, and pattern mix, including rare and first-occurrence events","Natural-language log search so operators can investigate without writing regex or query DSL","Confidence-scored root cause candidates ranked by evidence across logs, events, traces, and changes","AI-generated incident summaries and remediation playbooks mapped to recurring failure signatures","Closed-loop learning that uses past remediation outcomes to refine future recommendations"],uF=["Complete asset lifecycle tracking from procurement and deployment through maintenance, refresh, and decommission","Continuous license-position calculation that reconciles entitlements against installations and flags at-risk estates before an audit","Automated reclamation workflow that identifies unused licenses, notifies the user, and uninstalls after the configured confirmation window","Governed software delivery with package approval, dependency resolution, targeted deployment, ring staging, and rollback thresholds","Shadow IT and prohibited-software detection with allowlist policies, notification, and script-based remediation","Warranty, contract, subscription-expiry, vendor, and renewal tracking with configurable lead-time alerts"],mF=["Multi-factor asset risk scoring across age, compliance gaps, security posture, usage anomalies, and hardware health","AI compliance gap analysis mapped to CIS Benchmarks, NIST CSF, ISO 27001, and custom policies with prioritized remediation playbooks","Predictive maintenance recommendations and optimal maintenance windows generated from usage patterns and historical failure signals","AI remediation engine that creates executable scripts, policies, or deployment proposals with confidence scores for human approval","Cost anomaly detection that surfaces duplicate purchases, unexpected cloud spend, orphaned resources, and over-grown subscriptions","Natural-language asset reporting and executive briefings for finance, operations, compliance, CIO, CISO, and CFO stakeholders"],p3=["250+ governed automation rules across 8 trigger categories and 40+ specific trigger types","Every rule is built through a three-step wizard — Configuration, Script Builder, Review — and enters pending_approval until an administrator or manager signs off","Blast-radius percentage c

57aps and cooldown windows bound every execution to a safe, reversible share of the affected population","Dry-run simulation shows exactly which endpoints a rule would touch before it ever runs live","A rollback endpoint reverses a completed execution's effects on the endpoints where it succeeded","Composite rules chain multiple actions — restart a service, then ticket, then alert — into a single governed playbook","Visual Flow Designer extends automation into multi-step, multi-system orchestration with native Jira and ServiceNow nodes","Scoped external API keys let ITSM, monitoring, and CI/CD systems trigger specific rules without console access"],h3=["Per-endpoint AI analysis with an overall experience summary, four pillar-level insights, and recommended actions ranked by impact","AI gap analysis scans the rule library against fleet conditions to surface automation coverage gaps and recommend new rules","Help Desk AI Copilot converts plain-English questions into safe, read-only SQL queries against live fleet data","AI-generated executive briefings translate DEX telemetry into board-ready business language, saved and compared across periods","AI Tool Adoption Intelligence tracks Microsoft Copilot, ChatGPT Enterprise, and Claude Enterprise usage against industry benchmarks","AI-assisted script builder drafts PowerShell, Bash, or Python remediation scripts inside the rule wizard","Graceful fallback guides operators to the right pillar telemetry for manual review whenever the AI service is unavailable"],pF=[{heading:"Platform architecture and telemetry",body:"Every score, AI insight, and automation rule fires on real endpoint telemetry collected through Satellite Clients and the browser extension — not samples, estimates, or self-reported data.",bullets:["Satellite Clients capture CPU, memory, disk I/O, application events, Wi-Fi, VPN, DNS, and agent heartbeat data from every managed endpoint","The DexScoreBackgroundService continuously recomputes composite scores and maintains per-endpoint score history for trend analysis","The browser extension adds page load time, interaction latency, and session telemetry for SaaS applications endpoint agents can't see alone"]},{heading:"DEX peer benchmarking",body:"A DEX score means more with context — the Benchmarking module compares composite and pillar scores against peer organizations by sector and company size.",bullets:["Benchmarks are expressed as peer median scores with 25th and 75th percentile boundaries for the composite score and all four pillars","Pillar-level comparison reveals where investment is misaligned — for example, strong device and connectivity scores but below-median app health and sentiment","Export and report functionality packages benchmark data for board technology committees, procurement, and compliance reviewers"]},{heading:"VDI and external network intelligence",body:"As work moves through virtual desktops and SaaS, experience monitoring follows the full path — not just the physical endpoint.",bullets:["The VDI Experience module tracks composite scores, infrastructure-versus-endpoint fault splitting, and session reliability for Citrix and Azure Virtual Desktop","The External Network module scores LAN, ISP, and cloud/SaaS path segments independently, pinpointing whether a broken call is the Wi-Fi, the home ISP, or the cloud service","Network fault attribution compares all three path segments simultaneously to identify the most likely root cause of a degraded remote experience"]},{heading:"Visual Flow Designer",body:"Rules handle single-event response. Flows extend automation into multi-step, multi-system orchestration built on a visual canvas.",bullets:["A flow is a directed graph of trigger and action nodes connected by edges, built with drag-and-drop and saved, versioned, validated, and dry-run simulated","Native integration nodes for Jira and ServiceNow create issues, incidents, or requests with dynamic template variables like {{hostname}} and {{agent_id}}","A curated gallery of seven pre-built flow templates gives teams a complete starting point instead of building every flow from scratch"]},{heading:"Digital adoption journeys",body:"Deploying software is not the same as adopting it — in-app journeys close the gap between installation and confident daily use.",bullets:["Journeys combine tooltips, spotlights, and multi-step walkthroughs targeted at specific UI elements, published without changing the target application's code","Step-level completion analytics identify exactly where employees disengage, so authors can simplify the steps with the highest drop-off","Journey completion feeds the Employee Sentiment pillar, correlating adoption with lower crash and hang rates on the applications covered"]},{heading:"Employee self-service (Employee Assist)",body:"The fastest support experience is one where the employee resolves their own issue, guided by AI, in less t

57ime than it takes to type a ticket.",bullets:["A conversational assistant selects the most relevant playbook for the employee's described problem and device context","Every recommended action requires explicit employee confirmation before it executes, with the full session transcript recorded","Failed self-service escalates to the help desk with the complete diagnostic history already attached, cutting resolution time"]},{heading:"Work-from-home intelligence",body:"WFH is a permanent operating model that needs the same management discipline as the office — without resorting to invasive monitoring.",bullets:["The WFH dashboard reports remote device compliance rate, productivity threshold compliance, and per-employee productive/non-productive hours","Application usage is classified into productive, non-productive, communication, development, creative, or custom categories for fleet-level visibility","Employees whose productive hours fall below threshold submit a reason directly, visible to administrators alongside schedule and policy management"]},{heading:"External integration and scoped API access",body:"The DEX External API connects automation to the broader operational ecosystem without creating a separate automation silo.",bullets:["External systems can list enabled rules, execute a rule against a target endpoint set, and poll execution status through a REST interface","Scoped API keys restrict each integration to a specific list of rules it is authorized to trigger, with full rotation and revocation support","Webhook action nodes extend flows to PagerDuty, Opsgenie, Microsoft Teams, Slack, and custom internal systems beyond the native Jira/ServiceNow nodes"]}],hF=[{title:"The measurement gap",body:"Server uptime, patch compliance, and ticket SLAs measure IT's view of the system. They don't answer whether a device is fast enough, applications are reliable, or employees feel supported."},{title:"The delay problem",body:"A service desk ticket is a lagging indicator — by the time an employee submits one, they've already lost time and formed a negative judgment. Many never submit a ticket at all."},{title:"The scale problem",body:"A network issue affecting 40 devices in one subnet, or a crash-rate spike after an update, is invisible to a help desk watching for tickets across thousands of endpoints."},{title:"The proof-of-value problem",body:"Without a measurement framework, IT can't credibly show a CHRO or CFO that a refresh, upgrade, or consolidation actually improved employee productivity."}],fF=["Every score, AI insight, and automation rule fires on real endpoint telemetry — not samples, estimates, or self-reported figures","A composite four-pillar score pinpoints exactly which dimension — device, app, connectivity, or sentiment — is responsible for a poor experience","AI is an operating layer woven throughout the platform, not a single bolt-on feature","250+ governed automation rules resolve conditions before they generate a service desk ticket","Every automated action is governed — approval workflows, blast-radius caps, cooldown windows, dry-run simulation, and an immutable audit trail","Peer benchmarking and AI-generated executive briefings connect daily telemetry to boardroom investment decisions"],gF=[{title:"Measure the baseline",body:"Deploy telemetry across the fleet and run an initial score rebuild to establish the composite and pillar-level starting point."},{title:"Activate the highest-value rule",body:"Use AI gap analysis to identify the automation opportunity with the highest fleet coverage, then build and approve the rule."},{title:"Review results and expand coverage",body:"Monitor execution history and tickets prevented, adjust blast-radius and cooldown settings, and add the next rule."},{title:"Report outcomes to leadership",body:"Generate an executive briefing that connects score improvement and ticket deflection to the investment decision."}],xF=[{title:"Device Performance",body:"Hardware health, resource utilisation, and failure prediction — the foundation every application experience runs on.",bullets:["CPU utilisation, memory pressure, disk I/O saturation, and disk capacity across every managed endpoint","Hardware failure forecasting, battery health tracking, and boot-time/uptime anomaly triggers"]}

57,{title:"Application Health",body:"Crash rates, launch latency, and real application reliability — where digital experience becomes a productivity fact.",bullets:["Crash events, hang detection, launch latency, and version compliance from endpoint agents and the browser extension","Automated response to crash-rate spikes, version-floor violations, and shadow IT detection"]},{title:"Connectivity",body:"Wi-Fi stability, VPN reliability, DNS quality, and network path intelligence for office, remote, and hybrid employees.",bullets:["Wi-Fi signal and stability, VPN authentication, DNS resolution, and gateway reachability","Three-segment LAN / ISP / cloud path attribution for remote and hybrid workforces"]},{title:"Employee Sentiment",body:"Quantifying how employees feel about their digital experience — the dimension objective telemetry can't measure alone.",bullets:["Targeted DEX campaign surveys scored into the Sentiment pillar of every endpoint's composite score","Correlate subjective sentiment against objective device, app, and connectivity telemetry"]}],yF=[{label:"Top factors",title:"Ranked contributing factors",body:"Each score's top contributing factors combine a pillar, a factor name (e.g. memory pressure, Wi-Fi signal strength), a detail explanation, and an impact weight."},{label:"Score history",title:"Trend and regression detection",body:"Per-endpoint score history enables before-and-after measurement for every automation intervention and detects cyclical or sudden degradation."},{label:"Data confidence",title:"Confidence-weighted scoring",body:"Scores from full four-pillar telemetry carry high confidence; partial telemetry — a missing browser extension or intermittent Satellite Client — is flagged as lower confidence."},{label:"Band filtering",title:"Excellent / Good / Fair / Poor",body:"Operators filter the fleet by score band to focus remediation effort on the endpoints with the most severe experience conditions first."}],bF=[{title:"Three-step rule wizard",body:"Every rule is created through Configuration, Script Builder, and Review steps before it can be submitted."},{title:"Pending-approval by default",body:"New and edited rules enter pending_approval state automatically and cannot execute until approved."},{title:"Role-separated approval",body:"Only administrator or manager roles can approve, reject, or submit rules for approval."},{title:"Re-approval on change",body:"A rule whose configuration is modified while active is reset to pending_approval until re-approved."},{title:"Blast-radius percentage caps",body:"Every rule limits the proportion of matched endpoints it can act on in a single execution."},{title:"Cooldown windows",body:"A minimum interval between firings prevents a condition from triggering a rule repeatedly on the same endpoint."},{title:"Dry-run simulation",body:"Rules can log a planned execution and report the affected agent count without touching any endpoint."},{title:"Rollback endpoint",body:"A successful execution's effects can be reversed on the endpoints where the action succeeded."},{title:"Recorded rejection reasons",body:"Rejected rules carry a documented rejection reason that becomes part of their operational record."}],vF=[{title:"Per-endpoint AI analysis",body:"On-demand experience summary plus Device, App, Connectivity, and Sentiment insights with ranked recommended actions."},{title:"AI gap analysis",body:"Scans the rule library and fleet conditions to find automation coverage gaps and recommend new rules with remediation steps."},{title:"Help Desk AI Copilot",body:"Turns natural-language questions into read-only SQL against live fleet data, with schema visibility and saved queries."},{title:"Executive AI briefings",body:"Generates board-ready summaries connecting telemetry to business language, saved and compared period over period."},{title:"AI Tool Adoption Intelligence",body:"Tracks Microsoft Copilot, ChatGPT Enterprise, and Claude Enterprise adoption, engagement, and friction against industry benchmarks."},{title:"AI-assisted script builder",body:"Drafts PowerShell, Bash, or Python remediation scripts inside the rule wizard for validation and review."}],wF=["The Help Desk AI Copilot is architecturally constrained to read-only queries — it can never modify fleet data","AI-recommended automation rules and script drafts still enter pending_approval and require administrator or manager sign-off before they can run","When the AI analysis service is unavailable, the platform returns a structured fallback that directs operators to the relevant pillar telemetry for manual review","Every copilot query and its generated SQL is recorded in an immutable query history for governance and audit","AI gap analysis identifies coverage gaps and recommends remediation steps — it does not create or activate rules automatically"],jF=[{phase:"01",title:"Establish the measurement baseline (Weeks 1–4)",body:"Deploy Satellite Clients, verify telemetry across all four pillars, run the initial score rebuild, deploy the browser extension, and run the first AI gap analysis."},{phase:"02",title:"Activate the highest-value automation rules (Weeks 5–10)",body:"Prioritize System Health and Security rules from the gap analysis, build each through the three-step wizard, simulate and dry-run before activating, then track t

57ickets prevented weekly."},{phase:"03",title:"Expand to experience and adoption (Weeks 11–18)",body:"Launch sentiment campaigns for Poor-band endpoints, stand up AI tool adoption tracking, configure the WFH dashboard, publish adoption journeys, and connect the automation API to the service desk."},{phase:"04",title:"Program maturity and continuous improvement (Week 19+)",body:"Review the fleet score distribution quarterly, run gap analysis monthly, and expand the rule library toward full coverage across all eight trigger categories."}],NF=["Define a scoped starting population — a business unit, location, or device category — to establish a clean measurement baseline","Run the first AI gap analysis and identify the three most prevalent Poor-band conditions with the clearest automated response","Create, validate, simulate, and activate those first three rules, then track tickets prevented in week one as evidence","Generate the first executive briefing within the first month to build organizational alignment, then repeat it on a monthly cadence"],AF=[{title:"Productivity recovery",body:"Mature DEX programs resolve endpoint experience problems 3–5x faster than ticket-driven support, recovering hours of productive time across the fleet every week."},{title:"Ticket deflection",body:"Organizations typically report 20–40% reductions in service desk ticket volume in the categories where automation rules are active."},{title:"Security posture",body:"Security and compliance rules detect and remediate posture conditions in near real time, lowering mean-time-to-detection and mean-time-to-response."},{title:"Talent and retention",body:"Four-pillar DEX scores, sentiment trends, and executive briefings give HR and IT leadership evidence that technology quality — a top-three driver of job satisfaction — is improving."}

57],kF=["Unified health visibility across cloud, infrastructure, databases, storage, application servers, and microservices","Topology-aware dependency mapping that connects resource health to business-service impact","Intelligent alert grouping, deduplication, quiet hours, and maintenance-window suppression","Approval-gated auto-remediation workflows with notify, escalate, script, ticket, and rollback steps","Cost and capacity intelligence that turns telemetry into actionable optimization plans","Reusable monitoring templates, audit trails, and tenant-branded status communication"],SF=["Evidence-backed root cause analysis across metrics, logs, traces, events, dependencies, and recent changes","AI Cloud Architects that generate cost-estimated, compliance-aware architectures with infrastructure-as-code","Cloud Auditors that map findings to CIS, SOC 2, PCI-DSS, HIPAA, GDPR, and NIST frameworks","AI Database Assistant with query optimization, anomaly detection, tuning, workload analysis, and execution-plan tools","Predictive capacity forecasting, baseline-relative drift detection, and impact analysis","Natural-language AI search, generated runbooks, daily operations summaries, and remediation recommendations"],IF=["Continuous control monitoring and automated evidence collection across 28 regulatory and standards frameworks","Configurable risk-appetite statements and tolerance thresholds with automatic breach monitoring and scenario stress tests","One-click compliance reports that turn uploaded evidence into scores, findings, root cause, and remediation plans","AI-generated vendor questionnaires and due-diligence workflows tailored to third-party risk and compliance needs","Risk-based, IIA-aligned audit planning with structured fieldwork, testing, evidence, and remediation tracking","Cross-framework policy mapping that exposes direct, partial, and no-coverage gaps for accountable follow-up"],CF=["AI vendor assessment that reads questionnaire responses and produces risk levels with Security, Compliance, Financial, and Operational breakdowns","AI risk-based audit scoring that combines inherent risk, control environment, audit history, complexity, and regulatory exposure","AI-generated findings, root-cause analysis, regulatory references, management responses, and corrective action plans","AI policy generation, document extraction, and evidence-backed crosswalks across ISO 27001, NIST CSF, SOC 2, PCI-DSS, GDPR, and DORA","AI emerging-risk scans, scenario analysis, dependency mapping, predictive risk trajectories, and early-warning alerts","AI board reports, regulatory filing drafts, evidence adequacy scores, and executive narratives from live GRC data"],MF=["Autonomous incident classification, routing, and escalation based on priority, service, skills, load, and SLA risk","End-to-end fulfilment automation for software installs, access provisioning, hardware orders, and other standard requests","Continuous CMDB discovery and reconciliation that keeps configuration items and their relationships accurate","Known-error workflows that connect recurring incidents to permanent problem fixes and published workarounds","Risk-scored change governance with dynamic approval routing, conflict detection, and CI/CD integration","Coordinated release planning with environment gates, rollback orchestration, and DORA performance tracking"],TF=["AI-powered root cause analysis correlating incidents with CMDB data, recent changes, monitoring telemetry, and similar history","Predictive change risk scoring based on CI impact, change frequency, implementation window, and historical outcomes","Real-time SLA breach prediction with automated escalation, reassignment, and stakeholder notification","Conversational request intake and semantic knowledge search that map plain-language needs to services and solutions","AI-generated knowledge articles from resolved incidents and problems, with confidence scoring and lifecycle review","AI service portfolio rationalisation that identifies redundant, underused, and high-cost services for review"],EF=["No-code and low-code agent builder with a visual drag-and-drop canvas, reusable templates, and rapid deployment","Deterministic business rules and visual decision graphs with priority ordering, explainable execution, testing, and version history","Multi-agent orchestration with stateful graph execution, A2A discovery, shared context, and specialized-agent delegation","Governed human escalation with approval rules, confidence gating, sentiment signals, and AI-generated handoff summaries","50+ enterprise connectors that let agents read and act across ITSM, ITAM, HRIS, CRM, ERP, and ITOM systems","Enterprise workspaces with RBAC, tenant isolation, audit logs, encrypted agent API keys, and usage tracking"],RF=["Multi-LLM provider support across Anthropic, OpenAI, Google Gemini, Meta Llama, and DeepSeek without vendor lock-in","RAG-powered knowledge base with PDF, Word, Excel, image OCR, logs, CSV, and Markdown ingestion plus hybrid semantic search","Semantic intent routing and automatic AI-to-AI handover that preserves conversation context when a better-fit agent is detected","AI-assisted business-rule generation that turns plain-language policy into structured, validated logic","AI-generated responses, code, runbooks, summaries, and next-best actions grounded in live enterprise context","AI conversation analytics with topic clustering, anomaly detection, knowledge-gap detection, and weekly leadership briefings"],PF=["Sub-second voice pipeline with streaming speech-to-text, parallel reasoning, low-latency text-to-speech, and full-duplex barge-in handling","Autonomous mid-call execution across CRM, ITSM, ERP, and 50+ enterprise connectors while the caller is still speaking","Enterprise telep

57hony coverage across SIP trunks, Twilio Programmable Voice, Cisco Unified Communications Manager, Avaya, and Genesys Cloud","Context-preserving human handoff with confidence gating, escalation triggers, warm transfer, and AI-generated call briefings","Call analytics with containment rate, first-call resolution, average handle time, CSAT, and intent-level performance tracking","Compliance controls for PCI-DSS and HIPAA including PAN and PHI redaction, consent management, and immutable call audit trails"],OF=["Context-aware multi-turn reasoning with persistent call-graph memory, entity tracking, intent history, and clarifying questions","Real-time multi-dimensional sentiment and intent scoring for frustration, urgency, satisfaction, churn risk, and escalation readiness","AI-generated call transcripts, summaries, extracted action items, outcome classification, and searchable conversation intelligence","RAG knowledge grounding for product manuals, compliance documents, FAQs, and enterprise content","Multi-language and accent-adaptive voice understanding with 40+ languages, code-switching, and locale-aware parsing","Multi-LLM provider flexibility across Anthropic Claude, OpenAI GPT, Google Gemini, and on-premises models"],f3=["Continuous discovery across Windows, macOS, Linux, servers, and cloud workloads","Risk-based prioritization using asset criticality, business service impact, and maintenance windows","Phased rollout rings with canary, pilot, broad deployment, and automatic hold points","One-click rollback, failure recovery, and post-deployment verification","Audit-ready compliance dashboards with ownership, evidence, and exception tracking","Native CMDB and ITAM synchronization for complete asset-to-patch traceability"],g3=["AI patch risk scoring that ranks what to deploy first and explains why","Change impact prediction across dependent devices, applications, and business services","Exploitability and exposure analysis that highlights urgent remediation candidates","Smart maintenance-window recommendations based on usage, geography, and device health","Failure prediction with AI-generated remediation steps before a rollout exp

57ands","Natural-language patch reporting for executives, auditors, and operations teams"],DF=[{heading:"Continuous patch visibility",body:"AI Powered Patch Management builds a live inventory of missing, superseded, failed, and successfully deployed patches across the enterprise. It normalizes vendor advisories and package metadata into a single operational view.",bullets:["Discover patch state from managed endpoints, servers, and cloud workloads","Classify updates by operating system, application, severity, and release type","Identify stale agents, unreachable devices, and assets outside standard maintenance coverage"]},{heading:"Risk-based prioritization",body:"Instead of treating every available update equally, the platform combines technical severity with real business context. Teams get a ranked action list that reflects exposure, service criticality, device importance, and deployment readiness.",bullets:["Prioritize by asset criticality, business service dependencies, and exposure","Explain the factors behind every recommended deployment order","Separate urgent remediation from routine maintenance without losing audit history"]},{heading:"Safe rollout and recovery",body:"Every deployment can move through controlled rings so teams can validate results before expanding across the fleet. Automated gates pause campaigns when failure rates, performance signals, or user-impact thresholds are exceeded.",bullets:["Use canary and pilot groups before broad production rollout","Capture pre- and post-deployment health checks with device-level outcomes","Rollback failed changes and route exceptions to the right owner with full context"]},{heading:"Governance and compliance",body:"Patch campaigns remain fully traceable from approval through completion. Compliance leaders can see coverage, overdue assets, approved exceptions, and evidence for every reporting period.",bullets:["Track policy targets, exceptions, approvals, and expiry dates","Generate fleet, business-unit, site, and executive compliance views","Export an audit trail showing who approved, deployed, deferred, or remediated each change"]}],LF=[{title:"CVSS-only prioritization",body:"Severity scores lack endpoint exposure, weaponization, and business context. Teams spend scarce maintenance windows on vulnerabilities that may not represent the highest real-world risk."},{title:"Fragmented tooling",body:"Disconnected scanners, endpoint tools, and deployment systems create stale data and a dangerous gap between identifying a CVE and verifying its remediation."},{title:"Reactive coverage",body:"Without automated discovery, shadow IT, transient remote devices, and unmanaged endpoints remain outside the patch scope even when deployment tooling is effective."},{title:"Deployment without verification",body:"A successful command is not proof of remediation. Pending reboots, configuration conflicts, and failed installs can leave an endpoint vulnerable after the job reports success."}],VF=["Continuous discovery through native agent telemetry and agentless scanning","Threat-informed priority using CISA KEV, CVSS, EPSS, patch age, and business context","Explainable AI with visible score components, rank changes, and analyst overrides","Governance-first automation with independent approval for high-risk exceptions","Closed-loop verification that proves the vulnerable state was actually mitigated","Honest reporting that exposes unknown, stale, managed, and non-compliant segments"],_F=[{title:"Discovery",body:"Identify managed, remote, transient, and unmanaged infrastructure so every subsequent stage works from a complete data set."},{title:"Enrichment",body:"Connect patch and CVE intelligence to software identity, endpoint metadata, asset criticality, exposure, and known exploitation activity."},{title:"Prioritization",body:"Generate an explainable, rank-ordered remediation list that shifts as threat intelligence, asset importance, and patch age change."},{title:"Staging",body:"Validate applicability, content integrity, dependencies, disk space, service state, and representative pilot targets before release."},{title:"Deployment",body:"Deliver through policy-driven maintenance windows and deployment rings with evidence gates, throttling, retries, and automatic failure holds."},{title:"Verification",body:"Run post-change telemetry and active vulnerability checks, reconcile before-and-after state, and return unresolved devices to the action queue."}],zF=[{title:"Windows",body:"Integrate Windows Update Agent intelligence, KB-level telemetry, pending reboot state, vulnerability context, ring-based deployment, and post-change verification.",bullets:["Precise identification of required updates from current system configuration","Pilot, production, and critical deployment rings with automated gates"]},{title:"Third-party applications",body:"Treat browsers, runtimes, productivity tools, and collaboration software as part of the same risk-based program instead of leaving the practical exploitation surface unmanaged.",bullets:["Normalize installer formats, silent switches, versions, and vendor catalogs","Map application updates to CVEs and validate packages before broad release"]},{title:"Windows, macOS, and Linux",body:"Apply one accountability model across native update frameworks, MDM profiles, package repositories, server cluster

57s, and workstation workflows.",bullets:["Normalize evidence across operating systems and package managers","Preserve service availability and cluster health during server patching"]}],qF=[{label:"CVSS",title:"Technical severity",body:"Measures potential impact, but does not describe the full environmental risk on its own."},{label:"EPSS",title:"Exploitation probability",body:"Adds a probability signal for how likely a vulnerability is to be exploited in the wild."},{label:"CISA KEV",title:"Confirmed active exploitation",body:"Elevates vulnerabilities known to be used by threat actors into an accelerated remediation tier."},{label:"Exposure",title:"Fleet and business context",body:"Connects the vulnerability to affected endpoints, owners, sites, services, and asset criticality."},{label:"Patch age",title:"Unresolved technical debt",body:"Amplifies vulnerabilities that remain unpatched despite an available fix, preventing forgotten exposure from accumulating."}],FF=[{title:"Explainable scoring",body:"Every rank exposes the contribution of CVSS, EPSS, KEV, exposure, asset criticality, and data freshness. Rank-change indicators show what moved the recommendation."},{title:"KEV exception integrity",body:"Known exploited vulnerabilities require accelerated timelines, independent second-party approval, management visibility, hard expiry dates, and tamper-evident audit history."},{title:"Risk policy as configuration",body:"Map asset classes, service criticality, severity, and KEV status to reproducible SLA tiers that are reviewed as the business and threat landscape change."},{title:"Ring-based deployment",body:"Representative pilots, evidence gates, staggered sites or business units, and automatic failure holds limit blast radius without sacrificing security velocity."},{title:"Maintenance and continuity",body:"Classify maintenance windows by asset class, manage reboot impact, accelerate KEV windows, and record technical blockers instead of allowing silent deferrals."},{title:"Content and delivery integrity",body:"Validate package provenance, signatures, hashes, applicability, dependencies, bandwidth, regional distribution points, and preflight readiness before release."},{title:"Traceable deployment objects",body:"Give every campaign a unique job identity, policy binding, execution state, idempotent retry behavior, and per-device outcome evidence."},{title:"Closed-loop verification",body:"Distinguish installed, pending reboot, blocked, unknown, and remediated states through active post-change scans and immutable evidence retention."},{title:"Bounded exceptions and reporting",body:"Require owners, justification, compensating controls, expiry, closure conditions, escalation, and audience-specific evidence views for every deferred risk."}],BF=[{title:"Multi-factor asset risk",body:"Synthesize patch posture, compliance gaps, hardware health, and user-behavior anomalies into a transparent risk view."},{title:"Compliance gap remediation",body:"Map endpoint configuration against CIS Benchmarks, NIST CSF, and ISO 27001 to generate prioritized remediation playbooks."},{title:"Drift detection",body:"Compare endpoints with known-good baselines, classify deviations, and suggest changes while keeping human approval in the loop."},{title:"Predictive maintenance",body:"Use historical usage telemetry to recommend reboot and maintenance windows that improve success rates and reduce user disruption."},{title:"Remediation proposals",body:"Generate executable recommendations with confidence scores as a bridge from vulnerability identification to controlled deployment."},{title:"Operational copilots",body:"Give help-desk teams device and policy context while producing executive-ready briefings from governed operational data."}],HF=["Human approval gates remain mandatory for production changes.","Immutable logs capture every recommendation, supporting data point, override, and final decision.","Tenant data boundaries and prompt hygiene protect sensitive corporate metadata.","Low-confidence flags surface stale or incomplete data instead of hiding uncertainty.","Natural-language answers link back to governed endpoint evidence; unsupported answers return no result."],GF=[{phase:"01",title:"Establish the baseline",body:"Deploy telemetry across the primary workstation population to validate coverage, data quality, and inventory visibility without changing endpoint configurations."},{phase:"02",title:"Align policy",body:"Import standards such as CIS Benchmarks and run the compliance engine in Audit Only mode to measure the gap between current state and policy targets."},{phase:"03",title:"Pilot with evidence gates",body:"Select a non-critical cohort and require human approval for each automated patch cycle while the organization validates the recommendation engine."},{phase:"04",title:"Scale coverage",body:"Add third-party catalogs and server populations, then automate risk-based prioritization across the global estate."},{phase:"05",title:"Formalize governance",body:"Define repeatable, time-bound exception workflows with automatic expiry and clear ownership."},{phase:"06",title:"Continuously improve",body:"Use trend data and AI refinement cycles to optimize patch intervals, reduce downtime, and lower the attack surface."}],UF=["Coverage quality: distinguish current, stale, unknown, and unmanaged assets.","Prioritization: compare AI recommendations with analyst judgment and verify KEV identification.","Governance: test independent KEV exception approval and traceability in reporting.","Deployment: test pilot rings, maintenance windows, failure handling, retries, and content delivery.","Verification: prove which endpoints are remediated and which remain vulnerable.","Commercial fit: evaluate catalog depth, integrations, services, and total operating cost."],XF=[{title:"Security",body:"Reduce time from intelligence ingestion to prioritized action, critical and KEV exposure age, and risk on critical assets."},{title:"Operations",body:"Improve first-time deployment success, reduce failed-deployment diagnosis time, and lower campaign preparation effort."},{title:"Governance",body:"Increase exception completeness and independent approvals while reducing audit evidence retrieval time."}

57,{title:"Leadership",body:"Track open critical exposures, risk concentration across business services, and time to produce executive-ready briefings."}],$F=["12 specialist AI agents spanning requirements, architecture, specifications, planning, coding, QA, deployment, and release","Structured handoffs from business requirements to technical specifications, acceptance criteria, dependency maps, and risk registers","Production delivery support with code generation, database design, automated testing, CI/CD pipelines, infrastructure-as-code, and release orchestration","AI-powered peer review and validation that catches security issues, anti-patterns, maintainability risks, and documentation gaps before release","Feature-flagged production rollouts to controlled release buckets with automated release-note generation","Versioned outputs and recoverable workflow iterations so teams can experiment without losing stable work"],WF=["Bring-your-own AI model support across OpenAI, Anthropic Claude, Google Gemini, Meta Llama, DeepSeek, and custom or self-hosted models","Multi-model comparison that runs one request across 2–5 models and lets teams compare and select the strongest output","Multi-agent Swarm Mode that runs specialized agents in parallel, synthesizes their outputs, and surfaces conflicts with rationale","Semantic code search that learns repository conventions, reuses existing utilities, and keeps generated code aligned with the codebase","Vibe Code conversational refinement that updates existing output in plain language while preserving numbered versions and rollback paths","AI-generated requirements, architecture, API contracts, data models, code, tests, documentation, and deployment runbooks"],KF=["No-code agent builder for ServiceNow with reusable tools, workflows, guardrails, and human approval checkpoints","Open AI architecture supporting public LLMs, Now Assist models, custom LLMs, and SLMs without vendor lock-in","Multi-step agent orchestration that connects ServiceNow workflows with enterprise systems and specialized agents","21 specialist compliance auditors with continuous, scored evidence analysis across major regulatory frameworks","Native ServiceNow execution across incidents, changes, knowledge, SLA operations, and cross-functional workflows","Production deployment paths for public, private, hybrid, and on-premises AI architectures"],YF=["AI model training and tuning with domain-specific prompts, feedback loops, and performance evaluation","RAG-grounded agent responses using enterprise knowledge, ServiceNow records, compliance evidence, and historical data","AI-driven incident classification, root-cause diagnosis, resolution recommendations, and major-incident prediction","Intelligent change-risk scoring with CMDB depen

57dency analysis, conflict detection, and adaptive execution","Self-evolving knowledge gap detection with automatically generated and refreshed knowledge articles","Cross-auditor correlation that connects findings across compliance, incident, change, SLA, and operational domains"],QF=[{value:"5",label:"Delivery frameworks"},{value:"1",label:"Governed portfolio view"},{value:"AI",label:"Forecasting and risk signals"},{value:"Traceable",label:"Decision history"}],x3=["Portfolio, program, project, workstream, milestone, dependency, risk, issue, and decision records connected in one operating model","Native Agile, Waterfall, Hybrid, Scrum, and Kanban planning with governance controls that adapt to the delivery context","Professional Services Automation for engagements, statements of work, utilization, time, deliverables, and margin visibility","Resource capacity planning with role, skill, availability, location, and allocation context for confident staffing decisions","Financial controls that connect approved funding, forecast, actuals, change requests, and variance commentary","Scenario planning that lets PMOs test sequencing, staffing, scope, and funding choices before committing to a plan","Executive reporting built from governed delivery data instead of manually reconciled slide decks","Audit-ready history for approvals, baselines, changes, AI recommendations, overrides, and evidence attachments"],Rg=["Schedule and completion forecasting that learns from delivery history, current throughput, dependencies, and remaining work","Early risk prediction that surfaces the leading indicators behind likely schedule, budget, capacity, or quality variance","AI-generated executive narratives that explain what changed, why it matters, and where leadership attention is needed","Resource-to-work matching that considers skills, seniority, availability, workload, location, and assignment constraints","Dependency impact analysis that traces downstream milestones and proposes sequencing options when a commitment moves","Scenario recommendations that compare delivery outcomes across scope, staffing, funding, and timing assumptions","Natural-language portfolio questions with source-linked answers grounded in governed plans and current records","Explainable confidence, source context, human approval, and override history for every AI-assisted recommendation"],ZF=[{title:"Strategy gets lost in translation",body:"Executive priorities are documented in one place, delivery plans in another, and the connection between an investment and its outcomes is rebuilt by hand.",icon:"strategy"},{title:"Capacity is managed after the fact",body:"Resource managers discover conflicts when work is already committed, leaving teams to negotiate skills, availability, and competing demand under pressure.",icon:"capacity"},{title:"Variance arrives before visibility",body:"Budget, schedule, and margin conversations begin with stale status reports because actuals, forecasts, changes, and delivery evidence are not connected.",icon:"variance"},{title:"AI advice lacks accountability",body:"A recommendation without source context, confidence, or an approval trail is difficult to operationalize in a governed enterprise environment.",icon:"trust"}],JF=[{name:"Agile",descriptor:"Outcome-led adaptability",body:"Keep strategy visible while teams refine scope through iterative planning, evidence-based prioritization, and regular value reviews.",practices:["Roadmaps and themes","Backlog hierarchy","Increment planning","Value and outcome tracking"]},{name:"Waterfall",descriptor:"Controlled stage delivery",body:"Govern sequential work with baselines, stage gates, critical path awareness, formal approvals, and traceable change control.",practices:["Phase gates","Baseline schedules","Acceptance criteria","Formal change requests"]},{name:"Hybrid",descriptor:"Governance with flexibility",body:"Combine portfolio-level controls with iterative team execution so regulated or complex programs can move without losing oversight.",practices:["Governed milestones","Iterative work packages","Cross-team dependencies","Integrated status"]},{name:"Scrum",descriptor:"Cadence and accountability",body:"Coordinate product owners, delivery teams, and stakeholders around sprint goals, increments, impediments, and inspect-and-adapt rituals.",practices:["Sprint planning","Product backlogs","Reviews and retrospectives","Impediment tracking"]}

57,{name:"Kanban",descriptor:"Flow and throughput",body:"Make work-in-progress, bottlenecks, service classes, and flow performance visible across operational and project teams.",practices:["Visual work queues","WIP limits","Flow metrics","Explicit service policies"]}],eB=[{id:"governance",eyebrow:"Portfolio and program governance",title:"Make every investment accountable to a governed plan",body:"XpediOo gives PMOs one connected hierarchy from strategic objective to portfolio, program, project, workstream, and deliverable. Decision rights, stage gates, health signals, and outcome measures stay attached to the work they govern.",bullets:["Model portfolios by business objective, funding envelope, strategic theme, business unit, or service line","Standardize intake, prioritization, approval, baseline, review, and closure workflows without forcing every team into one methodology","Roll up status, risk, dependencies, financials, benefits, and decisions from delivery teams to program and executive views","Keep ownership, accountable executives, steering groups, and escalation paths explicit at each level"]},{id:"psa",eyebrow:"Professional Services Automation",title:"Run client delivery and internal change from the same control center",body:"Professional services teams can connect the commercial promise to the delivery reality. Scope, statements of work, phases, deliverables, time, expenses, staffing, utilization, and margin context are managed as one engagement record.",bullets:["Translate contracted scope and milestones into deliverable plans with accountable owners and acceptance evidence","Track planned, scheduled, submitted, and approved time against engagement, work package, and billing context","View utilization, bench exposure, skill demand, and assignment risk across client and internal work","Give delivery leaders a current view of forecast effort, change impact, margin pressure, and client commitments"]},{id:"capacity",eyebrow:"Resource capacity and skills",title:"Put the right capability on the work that matters next",body:"Resource managers can plan at the level of people, roles, teams, or skills. XpediOo brings supply, demand, allocation, availability, and constraints into a single view so staffing decisions are made before commitments become conflicts.",bullets:["Match upcoming demand to skills, proficiency, certifications, location, time zone, and seniority","Compare committed, planned, tentative, and available capacity by team, role, practice, or period","Expose over-allocation, under-utilization, single points of failure, and knowledge concentration","Let managers test internal moves, contractors, hiring, sequencing, and scope changes in a governed scenario"]},{id:"delivery",eyebrow:"Milestones and dependencies",title:"See the delivery system, not just a list of tasks",body:"A milestone is only useful when its dependencies, assumptions, owners, and downstream commitments are visible. XpediOo maps the delivery network so teams can act on the consequences of movement before a date turns red.",bullets:["Link cross-project dependencies to milestones, decisions, risks, releases, vendors, and external commitments","Trace upstream blockers and downstream impact across programs and portfolios","Baseline target dates and capture the reason, approver, and evidence behind each change","Use critical path, slack, milestone confidence, and dependency aging to focus review conversations"]},{id:"financials",eyebrow:"Financial controls",title:"Connect delivery decisions to funding and forecast reality",body:"Financial governance belongs inside delivery planning. XpediOo ties approved budgets and funding decisions to forecasts, actuals, committed spend, change requests, and benefit assumptions without turning the project plan into a finance afterthought.",bullets:["Track approved funding, planned cost, actual cost, committed cost, estimate to complete, and estimate at completion","Manage funding gates, budget transfers, contingency, purchase commitments, and financial approvals","Explain variance with owner commentary, source records, trend history, and corrective action","Bring revenue, cost, margin, and billing milestones into professional-services engagement views"]},{id:"scenarios",eyebrow:"Scenario planning",title:"Explore the trade-offs before the portfolio moves",body:"Planning decisions rarely have a single variable. Build a safe working copy of the portfolio, change the assumptions, and compare delivery timing, capacity pressure, funding exposure, and strategic coverage before selecting a path.",bullets:["Test sequencing, scope reduction, staffing changes, funding shifts, and target-date alternatives","Compare baseline and scenario views with transparent assumptions and decision notes","Identify which milestones, commitments, teams, and financial controls change under each option","Promote an approved scenario into the governed plan with a complete decision record"]},{id:"reporting",eyebrow:"Executive reporting",title:"Give leaders the signal, context, and decision path",body:"Executive views should answer what changed, what is at risk, what is consuming capacity, and what decision is needed. XpediOo turns governed delivery records into concise portfolio narratives with drill-through context.",bullets:["Create role-aware views for executives, portfolio councils, PMOs, program leads, delivery managers, and resource leaders","Show investment health, strategic alignment, milestone confidence, dependency exposure, capacity, and financial trend together","Generate narrative status updates from current evidence, with owners and source records available for review","Export consistent reports for steering meetings without losing the underlying audit context"]},{id:"audit",eyebrow:"Auditability and controls",title:"Move quickly without losing the record of why",body:"Governed delivery depen

57ds on more than permissions. XpediOo preserves the operational context behind a plan: who changed it, what was approved, what the AI recommended, which evidence informed the decision, and how the outcome evolved.",bullets:["Capture immutable activity history for plan, status, scope, budget, resource, milestone, and approval changes","Retain baseline versions, decision logs, evidence attachments, and exception commentary","Separate AI suggestions from human-approved actions with confidence, source context, and override history","Support role-based access, accountable ownership, review cadences, and controlled report distribution"]}],tB=[{title:"Strategy becomes executable",body:"Priorities, investments, delivery plans, and outcomes stay connected so portfolio conversations begin with intent and end with accountable action.",signal:"Strategic alignment"},{title:"Capacity becomes a planning input",body:"Resource choices are made with demand, availability, skills, and competing commitments in view rather than after the plan is already sold.",signal:"Workforce readiness"},{title:"Variance becomes an early conversation",body:"Forecast movement, emerging risks, dependency pressure, and cost signals are visible early enough for leaders to choose a response.",signal:"Predictable delivery"},{title:"Governance becomes evidence",body:"Approvals, decisions, changes, recommendations, and outcomes are recorded in the flow of work instead of reconstructed for the next review.",signal:"Operational trust"}],aB=[{title:"Human-owned decisions",body:"AI can prioritize signals and propose a next action, while accountable leaders retain approval, override, and escalation authority."},{title:"Evidence-linked recommendations",body:"Forecasts and risk signals show the source records, contributing factors, confidence, and assumptions that shaped the recommendation."},{title:"Policy-aware workflows",body:"Stage gates, financial thresholds, change controls, and review cadences can be tailored to the portfolio and delivery framework."},{title:"One system of record",body:"Plans, people, money, dependencies, decisions, and status narratives reference the same governed objects across every level."},{title:"Reviewable history",body:"Every material change carries its author, timestamp, previous value, reason, approver, and supporting evidence."},{title:"Methodology freedom",body:"Teams can work in Agile, Waterfall, Hybrid, Scrum, or Kanban while the portfolio still receives consistent governance signals."}],iB=[{title:"Shape demand",body:"Capture ideas, requests, strategic themes, client demand, and investment cases with the context needed for a fair prioritization conversation."},{title:"Commit the plan",body:"Approve scope, funding, outcomes, milestones, dependencies, and capacity assumptions with accountable owners and explicit gates."},{title:"Deliver with context",body:"Let teams work in the framework that fits while portfolio leaders see progress, blockers, changes, and decisions as they happen."},{title:"Sense the future",body:"Use AI forecasting, risk prediction, capacity analysis, and scenario comparison to identify variance before it becomes a surprise."},{title:"Decide and learn",body:"Record the decision, apply the approved change, communicate the narrative, and carry lessons into the next planning cycle."}],Pg=["Major incident predictor with 87% accuracy and a 2–6 hour early warning horizon before P1/P2 escalation","Change risk scoring (0–100) with explainable factor breakdown, CAB recommendation engine, and freeze-window conflict detection","21 specialist AI Auditors covering SOX, HIPAA, GDPR, SOC 2, FedRAMP, ISO 27001, PCI-DSS v4.0, HITRUST, DORA, and ISO 22301 continuously","Cross-Auditor Correlation Engine that surfaces causal chains and compound risks across all 21 compliance domains simultaneously","Knowledge gap detection engine that auto-generates structured KB articles directly from incident pattern data","AI resolution enrichment with approval workflow — validated recommendations fed back into a continuous training loop"],Og=["ML-powered incident classification trained on historical patterns with 92%+ accuracy and instant queue routing","Compound anomaly scoring that correlates signals across infrastru

57cture, application, and process layers simultaneously","Predictive capacity forecasting — 90-day horizon, ±8% accuracy, and breach prediction weeks before SLA thresholds are hit","Workforce intelligence — agent fatigue scoring, bottleneck analysis, and attrition risk modelling across service teams","AI-generated change documentation — implementation plan, rollback procedure, test cases, and compliance checklist produced from a change description alone","Continuous compliance evidence collection with confidence-graded, dimension-scored, regulator-ready audit artefacts across 10+ frameworks"],nB=[{heading:"AI Insights & Incident Intelligence",body:"AI works at every step of the incident lifecycle — from first alert to closed ticket — to accelerate resolution and prevent recurrence across the enterprise.",bullets:["Automated classification routes every incident to the right team with 92%+ accuracy and zero manual triage","Similarity detection surfaces related open incidents and known problems in real time for immediate fix reuse","Root cause analysis pinpoints the most likely cause from incident timelines, change history, and CMDB relationships","Impact radius maps affected services, users, teams, and dependencies — providing a full blast-radius view instantly"]},{heading:"Major Incident Predictor",body:"Identifies emerging P1/P2 events hours before they escalate — enabling proactive intervention before business impact occurs.",bullets:["Signal aggregation monitors metric deviations, error rate spikes, change activity, and CMDB dependency graphs simultaneously","Pattern matching compares current signal clusters against historical major incident fingerprints using ML similarity models","Probability scoring outputs a 0–100 score with severity prediction, confidence level, and time-to-impact estimate","Automated alerting pre-populates incident bridge details and suggests immediate mitigation steps","45% reduction in P1 events and 87% prediction accuracy in production deployments"]},{heading:"Change Risk & Lifecycle Governance",body:"Every change request is scored for risk before it reaches the CAB — with a full AI breakdown of factors, confidence, and recommendation. Change lifecycle audits run pre-submission, pre-approval, pre-deployment, and post-deployment.",bullets:["AI risk score (0–100) with explainable factor breakdown across complexity, timing, dependencies, and history","CAB recommendation engine states whether CAB review is required with urgency level and human-readable rationale","Conflict detection identifies scheduling conflicts, dependency collisions, and freeze-window violations across all pending changes","AI-generated implementation plan, rollback procedure, test cases, release notes, and compliance checklist from the change description","80% reduction in change-related incidents across production deployments"]},{heading:"Knowledge & Problem Management AI",body:"AI identifies unresolved incident themes with no knowledge coverage, generates the missing articles automatically, and accelerates problem management from candidate detection through board-ready RCA reports.",bullets:["Gap detection scans incidents for recurring themes lacking a KB article — ranked by frequency, severity, and user impact","AI article generation drafts fully structured knowledge articles with symptoms, steps, resolution procedure, and verification","Problem candidate detection identifies chronic patterns by incident frequency, total downtime, and resolution cost","Batch RCA analyses multiple related incidents simultaneously and produces a board-ready report in minutes","3× faster knowledge creation and 68% self-service deflection lift"]},{heading:"Proactive Monitoring & Capacity Forecasting",body:"AI continuously monitors, scores, and alerts before thresholds are breached — and forecasts infrastructure demand and workforce capacity with a 90-day horizon.",bullets:["Anomaly scoring measures deviations from historical baselines (0–100) with compound-risk alert grouping","Capacity forecasting analyses CPU, memory, storage, network, and application utilisation trends with breach prediction","What-if scenario modelling calculates capacity impact of planned changes before they happen","Workforce intelligence tracks agent fatigue, bottleneck distribution, and attrition risk across service teams","35% fewer capacity incidents and ±8% forecast accuracy across production deployments"]},{heading:"21 Specialist AI Auditors",body:"Purpose-built compliance auditors continuously score, evidence, and benchmark each ITSM domain against regulatory requirements — producing regulator-ready artefacts with dimension breakdowns and trend history.",bullets:["SOX, HIPAA, GDPR/CCPA, SOC 2 Type II, FedRAMP/NIST 800-53, ISO 27001, PCI-DSS v4.0, HITRUST CSF, DORA, ISO 22301 — all native","CMDB Accuracy, Incident Management, Change Management, Problem Management, Knowledge Management — process domain auditors","SLA Performance, Capacity, Workforce, Vendor Risk, and Service Catalogue — operational domain auditors","Each auditor produces 0–100 composite scores per domain with dimension breakdowns and audit run history","70% reduction in audit preparation time and 3.2× faster regulatory compliance readiness"]},{heading:"Cross-Auditor Correlation Engine",body:"The intelligence layer that c

57onnects all 21 auditors — surfaces causal chains and compound risks that no single auditor can detect independently.",bullets:["Causal chain discovery hypothesises and scores relationships between findings across all 21 auditors","Compound risk detection surfaces scenarios where medium-severity findings in different domains combine into a critical organisational risk","Cross-domain quick wins identify single remediation actions that simultaneously improve three or more auditor domain scores","System health narrative produces a board-ready summary of overall ITSM and compliance posture from all 21 data points","Each correlation is graded by confidence (High / Medium / Low) and severity with supporting evidence cited"]}],rB=[{title:"Incident Management",body:"40% MTTR reduction, SLA performance +18 points, incident reopen rate -55%."},{title:"Change Management",body:"Failed changes -38%, emergency changes -60%, PIR completion +45%."},{title:"Compliance & Audit",body:"Audit prep time -70%, external audit fees -35%, breach risk -82%."},{title:"Knowledge & Self-Service",body:"Deflection rate +28%, agent onboarding -30% time, KEDB accuracy +60%."},{title:"Vendor Management",body:"Vendor SLA disputes -65%, contract negotiation effort -50%, concentration risk -40%."},{title:"Financial Impact",body:"£1.8M average annual saving from breach prevention. 3.2× faster regulatory compliance readiness."}],sB=["SOX — Financial statement accuracy, segregation of duties, ITGC, and audit trail completeness","HIPAA — PHI access logging, breach risk assessment, Security Rule controls, and Business Associate oversight","GDPR / CCPA — Data subject rights fulfilment, consent management, retention compliance, and breach notification","SOC 2 Type II — Trust service criteria (Security, Availability, Confidentiality, Processing Integrity, Privacy)","FedRAMP / NIST 800-53 — Control family coverage, POA&M tracking, ConMon evidence, and ATO readiness","ISO 27001 — Annex A control assessment, risk register currency, ISMS audit programme compliance","PCI-DSS v4.0 — CDE scoping, 12 requirement area scoring, network segmentation, pen-test schedule","HITRUST CSF — 19-domain control scoring, healthcare-specific inheritance mapping, certification readiness","DORA — ICT risk management, operational resilience testing, third-party ICT provider oversight","ISO 22301 BCMS — BIA completeness, RTO/RPO achievement, BCP test results, crisis communications","CMDB Accuracy & Hygiene — CI completeness, relationship integrity, staleness detection, CMDB coverage","Incident Management Maturity — SLA adherence, escalation patterns, reopen rates, resolver effectiveness","Change Management Maturity — Risk scoring accuracy, CAB quality, conflict rate, PIR completion","Problem Management Maturity — Backlog health, RCA quality, KEDB accuracy, incident recurrence","Knowledge Management — Article quality, deflection rates, gap coverage, contribution compliance","SLA Performance — SLA breach trending, breach cause attribution, escalation pattern analysis","Capacity & Availability — Utilisation trajectories, breach risk prediction, architectural risk identification","Workforce & Capacity — Agent fatigue, bottleneck distribution, attrition risk, workload balance","Vendor Risk Intelligence — SLA adherence, concentration risk, contract currency, dependency mapping","Service Catalogue — Catalogue accuracy, request fulfilment rates, catalogue hygiene and currency","Cross-Auditor Correlation Engine — Causal chains, compound risks, and system health narrative across all 21 domains"],y3=["Unified employee record from hire to retire — integrating payroll, benefits, performance, and compliance data in a single profile","AI-driven recruitment pipeline with intelligent job-matching, automated CV screening, and offer letter generation","Corporate HR policy engine that auto-generates jurisdiction-compliant policies by country with versioning and approval workflows","Automated onboarding and offboarding orchestration with task assignment, provisioning, and day-one readiness tracking","Absence and leave management with real-time entitlement tracking, calendar integration, and compliance rules by region","Workforce analytics and HR reporting across headcount, attrition, diversity, cost, and capability dimensions"],b3=["Employee self-service conversational chatbot for leave, expenses, payslips, policies, and HR case queries — available 24/7","Predictive attrition modelling that identifies flight-risk employees and surfaces personalised retention actions","AI skill-gap anal

57ysis with individual development recommendations, learning path generation, and career trajectory mapping","Automated absence anomaly detection with pattern analysis across teams, sites, and roles","Natural-language HR analytics — ask workforce questions in plain English and receive governed, sourced answers","AI-generated performance review summaries, goal alignment scores, and 360-feedback synthesis"],oB=[{heading:"Core People Management",body:"XpediOo HRMS maintains a single, authoritative employee record that spans personal data, employment history, contract terms, benefits, payroll linkage, and compliance attributes across every country of operation.",bullets:["Centralise employee master data with role-based access and data privacy controls","Track employment types, contract status, probation periods, and employment milestones","Manage multi-entity, multi-currency, and multi-jurisdiction structures without duplication"]},{heading:"Absence Management",body:"The absence engine enforces country-specific leave entitlements, public holiday calendars, and carry-over rules automatically, so HR teams spend time managing exceptions rather than recalculating balances.",bullets:["Configure leave types, accrual rules, and entitlement caps by country, entity, and employee class","Synchronise approved absences with workforce capacity planning and project resource schedules","Detect absence patterns and anomalies with AI-driven alerts before they become business-risk events"]},{heading:"Performance and Development",body:"Continuous performance management replaces the annual review cycle with real-time goal tracking, regular check-ins, and AI-assisted feedback that keeps employees and managers aligned throughout the year.",bullets:["Set, cascade, and track OKRs and objectives at company, team, and individual level","Generate AI-assisted review summaries and 360-feedback synthesis to reduce manager effort","Link performance outcomes to compensation, progression, and succession planning workflows"]},{heading:"Recruitment",body:"End-to-end talent acquisition from job requisition through offer acceptance, with AI matching and automated screening that shortens time-to-hire without sacrificing candidate quality.",bullets:["Publish roles to multiple job boards and internal career portals from a single requisition","Rank and screen candidates with AI matching against the job profile, skills, and company values","Generate offer letters, employment contracts, and pre-boarding packs automatically on approval"]},{heading:"Case Management",body:"HR case management gives employees a governed channel for raising HR enquiries, grievances, and requests, while giving HR teams a full audit trail, SLA visibility, and workload management across every case type.",bullets:["Categorise cases by type — employee relations, payroll, benefits, policy — with SLA targets per category","Assign, escalate, and collaborate on cases with full thread history and document attachments","Surface case trends and recurring themes to HR leadership for proactive policy intervention"]},{heading:"Expense Management",body:"AI-powered expense management captures, validates, and routes employee expense claims against policy rules automatically, reducing reimbursement cycle time and out-of-policy spend.",bullets:["Submit expenses via mobile OCR receipt capture with automatic merchant and amount extraction","Apply configurable policy rules by country, category, and employee grade at point of submission","Route claims for approval, integrate with finance systems, and generate audit-ready reports"]},{heading:"Skill Management",body:"A live skills inventory maps what the organisation knows today, identifies what it needs tomorrow, and surfaces targeted learning to close the gap — making workforce planning a data-driven activity.",bullets:["Build a skills taxonomy aligned to roles, grades, and business capabilities","Capture verified skills from certifications, assessments, project history, and manager endorsements","Identify critical skill gaps across teams and recommend targeted learning, mentoring, or hiring actions"]},{heading:"Corporate HR Policy Generator by Country",body:"The AI policy generator drafts compliant HR policies for any jurisdiction by combining local employment law, regulatory requirements, and the organisation's own standards into a reviewed, versioned document.",bullets:["Generate country-specific policies covering leave, pay, data privacy, conduct, and termination","Version-control policy documents with approval workflows, effective dates, and employee acknowledgement tracking","Alert HR team

57s to regulatory changes that affect existing policies and produce update drafts automatically"]},{heading:"Onboarding & Offboarding People",body:"AI-orchestrated onboarding and offboarding sequences ensure every task — from IT provisioning to compliance sign-off — is assigned, tracked, and completed on time, giving new hires a day-one ready experience.",bullets:["Trigger onboarding workflows on offer acceptance with task plans, welcome content, and buddy assignment","Coordinate across IT, facilities, payroll, and compliance with automated provisioning and checklist tracking","Run structured offboarding with knowledge transfer tasks, access revocation, and exit survey analysis"]},{heading:"HR Analytics",body:"Real-time workforce analytics give HR leaders, finance partners, and line managers accurate headcount, cost, diversity, and attrition data without manual report runs or spreadsheet consolidation.",bullets:["Track headcount, attrition, time-to-hire, absence rates, and cost-per-hire across any dimension","Model workforce scenarios — hiring plans, restructures, and capability investment — with financial impact projections","Deliver role-specific dashboards for HR business partners, COEs, and executive leadership"]},{heading:"Employee Self-Service Conversational Chatbot",body:"The AI HR chatbot gives every employee instant, accurate, and governed answers to HR questions — reducing tier-1 case volume and freeing HR team

57s to focus on complex employee needs.",bullets:["Answer questions on leave balances, payslips, policies, benefits, and expenses in natural language","Raise HR cases, submit expense claims, request approvals, and update personal details through conversation","Hand off to a human HR agent seamlessly when the query exceeds the chatbot's governed scope"]}],lB=[{title:"Fragmented HR systems",body:"When recruitment, performance, payroll, and absence live in separate tools, employee data is inconsistent, HR teams reconcile manually, and employees receive contradictory information."},{title:"Manual compliance management",body:"Managing country-specific leave entitlements, policy requirements, and employment law changes manually is error-prone and leaves organisations exposed to regulatory risk as they scale across borders."},{title:"Poor employee experience",body:"Employees waiting days for simple HR answers lose confidence in the HR function. Self-service portals that require navigation rather than conversation fail to reduce HR case volume."},{title:"Invisible skill gaps",body:"Without a live skills inventory, organisations plan headcount based on role counts rather than capability, and discover critical skill shortages only when a project or product initiative stalls."}],cB=["One authoritative employee record across all HR domains — no reconciliation between modules","AI-first design — every workflow has an AI layer that learns, predicts, or generates rather than just automating rules","Country compliance built in — not a configuration afterthought — across leave, policy, and data privacy","Employee experience at the centre — conversational self-service that works on any device, any time","Governed data with role-based access, audit trails, and consent management for every sensitive attribute","Open integration — connects to payroll, finance, directory, and identity systems without proprietary lock-in"],dB=[{title:"Attract",body:"AI-matched job discovery, intelligent screening, and candidate ranking reduce time-to-shortlist without sacrificing quality or diversity."},{title:"Hire",body:"Digital offer management, background check orchestration, and automated contract generation get candidates to signed status faster."},{title:"Onboard",body:"Structured task plans, IT provisioning, compliance sign-off, and buddy pairing give every new hire a confident first day."},{title:"Develop",body:"Continuous goal alignment, AI skill-gap analysis, and personalised learning paths keep employees growing in roles that matter to the business."},{title:"Retain",body:"Predictive attrition signals, recognition workflows, and performance-linked compensation help managers act before they lose key people."},{title:"Transition",body:"Structured offboarding protects institutional knowledge, closes access, and gathers insight that improves future retention."}],U2=[{group:"AI Video Recruiter",icon:"Video",tagline:"From job description to agentic video interview — without a single recruiter scheduling call.",features:[{title:"Profile-to-JD semantic matching",body:"AI reads every candidate CV and scores it against the full job description — skills, experience depth, seniority, culture signals, and role-specific requirements — producing a ranked shortlist with explainable match reasoning before a recruiter reviews a single document."},{title:"Agentic video interview dispatch",body:"Once a candidate is shortlisted, the platform automatically generates a personalised video interview link and sends it to the candidate with a branded invitation, role context, and a deadline — no scheduling required, no back-and-forth email."},{title:"AI interview question generation",body:"Questions are generated dynamically from the job description, mapped to competency frameworks, and tailored to the candidate's specific profile gaps — ensuring every interview covers the areas that matter most for that role."},{title:"Real-time interview analysis",body:"As the candidate records their responses, AI analyses verbal fluency, response structure, competency signal strength, and tone — producing a scored interview report with supporting evidence clips and a recommendation summary."},{title:"Bias detection and fairness scoring",body:"Every shortlisting decision and interview score is audited for demographic bias. The system flags inconsistencies across candidate groups and provides corrective prompts before decisions are finalised, keeping hiring legally defensible."},{title:"Offer acceptance probability scoring",body:"Predict the likelihood that a candidate will accept an offer based on market data, role level, interview engagement, and response latency — allowing recruiters to prioritise follow-up effort on high-risk acceptances."}]}

57,{group:"AI Employee Self-Service Chatbot",icon:"MessageSquare",tagline:"Every HR answer. Every employee. Every moment. No ticket, no wait, no frustration.",features:[{title:"Multi-intent HR query handling",body:"The AI chatbot understands complex, multi-part HR questions in natural language — 'How many days leave do I have left and can I carry them over?' — and returns governed, personalised answers drawn from live HR system data."},{title:"Proactive employee nudges",body:"Automatically remind employees of upcoming actions — expiring leave balances, overdue training, open goal-setting windows, and review deadlines — via conversational prompts rather than generic email notifications."},{title:"Sentiment detection and escalation",body:"AI monitors conversation sentiment in real time. When frustration, distress, or sensitive topics are detected, the chatbot seamlessly escalates to a named HR advisor with full conversation context — no repetition required."},{title:"Transactional HR self-service",body:"Employees can submit leave requests, raise expense claims, update personal details, request letters, and initiate HR cases directly through conversation — with approvals routed automatically and confirmations returned in the same thread."},{title:"Voice-enabled HR self-service",body:"The chatbot is available via voice on mobile — employees can ask HR questions hands-free, submit requests by speaking, and receive spoken responses — extending self-service to field workers and deskless employees."}]},{group:"AI Recruitment Intelligence",icon:"Briefcase",tagline:"End pipeline guesswork with AI that surfaces the right talent at every hiring stage.",features:[{title:"AI CV parser and skills extractor",body:"Automatically parse unstructured CVs from any format, extract skills, qualifications, tenure, and achievements, and map them to a normalised taxonomy — so the data model is clean before any human reviews a candidate."},{title:"Job description quality analyser",body:"Before a role is posted, AI scores the job description for clarity, specificity, bias language, and completeness — and suggests improvements that will attract a broader, more relevant talent pool."},{title:"Talent pool warm-up",body:"AI continuously monitors passive candidates in the internal talent pool and CRM, ranks them against open roles as they are created, and surfaces warm matches before external sourcing is needed."},{title:"Candidate drop-off risk predictor",body:"Flag candidates who are likely to withdraw before offer stage based on engagement signals — response delays, incomplete assessments, and interview rescheduling patterns — so recruiters can intervene early."},{title:"Interview summary and recommendation pack",body:"After every interview stage, AI generates a structured summary covering competency scores, key quotes, red flags, and a hire / hold / reject recommendation — ready for the hiring manager to review in under two minutes."}]},{group:"AI Performance Management",icon:"TrendingUp",tagline:"Replace generic annual reviews with AI that makes performance a continuous, data-driven conversation.",features:[{title:"AI Performance Goal Generator",body:"Generates SMART goals for any employee by combining their role profile, team OKRs, prior performance data, and competency gaps — producing a draft goal set the manager and employee can refine rather than write from scratch."},{title:"People SWOT Generator",body:"After each performance review cycle, AI produces a personalised SWOT analysis for every employee — Strengths to leverage, Weaknesses to address, Opportunities from skill adjacency, and Threats from attrition risk or market demand for their profile."},{title:"360 feedback synthesis",body:"Collects structured feedback from peers, direct reports, and stakeholders, then synthesises it into themed patterns — separating consistent signals from outlier opinions and presenting a balanced narrative the employee can act on."},{title:"Performance trajectory prediction",body:"Using historical goal completion rates, feedback trends, and engagement data, AI predicts which employees are on a rising trajectory and which are at risk of decline — giving managers a leading indicator rather than a lagging one."},{title:"AI coaching conversation prompts",body:"Before every one-to-one or performance discussion, AI surfaces suggested coaching questions tailored to the employee's current goals, recent feedback, and development plan — helping managers have more effective conversations."},{title:"Under-performer early warning system",body:"AI identifies performance degradation signals — missed check-ins, declining goal progress, increasing case submissions, and peer feedback shifts — before they reach formal performance management, allowing early supportive intervention."},{title:"High-potential identification",body:"Combine performance scores, learning agility signals, peer feedback, and stretch project delivery to automatically identify high-potential employees and surface them to succession planning workflows."}]},{group:"AI Skill & Career Intelligence",icon:"Brain",tagline:"Build an organisation that can see its own capability and close gaps before they become crises.",features:[{title:"Skills taxonomy auto-builder",body:"AI analyses job descriptions, performance reviews, and project records across the organisation to generate and continuously refine a skills taxonomy — no manual taxonomy maintenance required."},{title:"Individual and team skill gap analysis",body:"Map every employee's verified skills against their current role requirements and planned career path, then aggregate gaps at team, department, and organisation level to drive targeted development and hiring decisions."},{title:"AI career pathway generator",body:"For any employee, AI generates two or three plausible career paths based on their current skills, interests, and the roles most similar employees have moved into — making internal mobility a structured conversation rather than a guessing game."},{title:"Internal mobility matching",body:"When a new role opens, AI scans the internal talent pool for employees whose skills, performance, and career trajectory align with the requirements — presenting internal 

57candidates alongside external ones before sourcing begins."},{title:"Learning content recommendation engine",body:"Based on the identified skill gap, AI recommends specific courses, certifications, mentors, and stretch assignments from the integrated learning catalogue — personalised to the employee's learning history and preferred format."},{title:"Certification and licence expiry predictor",body:"Track professional certifications, compliance training completions, and licence renewals across the workforce — and surface at-risk employees before expiry creates a regulatory or operational gap."}]},{group:"AI People Analytics & Workforce Intelligence",icon:"BarChart3",tagline:"Move from reporting on the past to predicting and shaping the future of your workforce.",features:[{title:"Predictive attrition modelling",body:"AI analyses engagement signals, performance trends, absence patterns, compensation positioning, and manager effectiveness to score every employee's attrition risk on a rolling basis — and recommends specific retention actions for high-risk individuals."},{title:"Workforce demand forecasting",body:"Model headcount requirements 90 days ahead by combining business growth plans, project pipelines, attrition forecasts, and planned retirements — giving HR business partners data to drive proactive hiring plans rather than reactive backfills."},{title:"Organisational network analysis",body:"Map informal influence and collaboration patterns across the organisation to identify key connectors, knowledge brokers, and isolated individuals — informing team restructures, succession decisions, and change management strategy."},{title:"DEI analytics and gap identification",body:"Measure representation, pay equity, promotion rates, and interview-to-hire ratios across demographic groups — and surface statistically significant disparities with root-cause analysis to guide targeted DE&I interventions."},{title:"Headcount scenario modeller",body:"Run what-if scenarios for restructures, acquisitions, hiring freezes, and location expansions — modelling the cost, capability, and timeline impact of each option before any decision is communicated."},{title:"Manager effectiveness scoring",body:"Aggregate team attrition rates, engagement scores, performance distributions, and 360 feedback signals to produce a manager effectiveness index — identifying where leadership development investment will have the greatest impact."},{title:"Natural-language workforce querying",body:"Ask workforce questions in plain English — 'Which teams have the highest attrition risk this quarter?' or 'Show me skill gaps in the engineering function' — and receive governed, sourced answers with supporting data."}]},{group:"AI Absence & Wellbeing Intelligence",icon:"ClipboardCheck",tagline:"Detect wellbeing risk before it becomes absence, and manage absence before it becomes a pattern.",features:[{title:"Absence pattern anomaly detection",body:"AI flags statistically unusual absence patterns — Monday/Friday clustering, rolling short-term absences, and post-review spikes — at individual and team level, surfacing them to HR for supportive follow-up rather than disciplinary action."},{title:"Burnout risk prediction",body:"Combine absence frequency, overtime data, performance decline signals, and pulse survey sentiment to produce a burnout risk score for every employee — enabling proactive wellbeing interventions before extended leave occurs."},{title:"Return-to-work risk assessment",body:"When an employee returns from extended absence, AI generates a return-to-work risk score based on absence duration, nature, and historical recurrence — and suggests a phased return plan for high-risk cases."},{title:"Team capacity impact modelling",body:"When absence exceeds threshold levels in a team, AI models the capacity shortfall, identifies the highest-impact coverage gaps, and suggests redistribution or temporary resource options to maintain service delivery."}]},{group:"AI Onboarding & Offboarding Orchestration",icon:"Users",tagline:"Make every first day and every last day as smooth and risk-free as every day in between.",features:[{title:"Personalised onboarding journey generator",body:"AI generates a day-by-day onboarding plan tailored to the new hire's role, location, seniority, and team structure — including introductory meetings, compliance completions, system access milestones, and culture touchpoints."},{title:"Day-one readiness scoring",body:"Track onboarding task completion across IT, facilities, payroll, compliance, and line management in real time — and surface a day-one readiness score so HR can intervene if provisioning is at risk before the employee's first day."},{title:"Knowledge transfer risk assessment",body:"When an employee resigns, AI asse

57sses their knowledge transfer risk based on role criticality, tenure, documentation coverage, and team dependency — and generates a structured knowledge capture plan automatically."},{title:"Exit interview sentiment analysis",body:"AI analyses exit interview responses to extract themes, sentiment patterns, and root causes of departure — aggregating signals across leavers to identify systemic retention risks that individual exit interviews mask."},{title:"Offboarding compliance tracker",body:"Automatically track access revocation, equipment return, final pay processing, reference readiness, and post-employment obligation compliance — with escalation alerts for any item that risks a regulatory or security gap."}]},{group:"AI Case Management Intelligence",icon:"BookOpen",tagline:"Resolve HR cases faster with AI that categorises, routes, and recommends before a human opens the ticket.",features:[{title:"Intelligent case categorisation and routing",body:"AI classifies every inbound HR case by type, urgency, and sensitivity, then routes it to the right HR specialist or business partner — with SLA assignment, priority scoring, and required documentation pre-populated."},{title:"Resolution recommendation engine",body:"For every new case, AI surfaces the three most similar resolved cases with their resolution steps and outcomes — giving the assigned HR advisor a starting point rather than a blank page."},{title:"SLA breach predictor",body:"Monitor active case workload and resolution pace in real time, flag cases that are trending toward an SLA breach, and recommend reassignment or escalation before the deadline is missed."},{title:"Sensitive case risk flagging",body:"Automatically identify cases that carry legal, regulatory, or reputational risk — grievances, whistleblowing, disciplinary matters — and route them to senior HR with a risk summary and recommended handling protocol."}]},{group:"AI Expense Intelligence",icon:"Receipt",tagline:"Approve the right claims instantly and catch policy violations before they cost the business.",features:[{title:"Receipt OCR and automatic expense coding",body:"Employees photograph a receipt and AI extracts the merchant, amount, currency, category, and VAT automatically — populating the claim form without manual entry and mapping the spend to the correct cost centre."},{title:"Policy violation detection at submission",body:"AI checks every expense claim against configurable policy rules at the point of submission — per-diem limits, category restrictions, receipt age, and duplicate detection — rejecting or flagging non-compliant items before they reach an approver."},{title:"Duplicate claim detection",body:"Cross-reference every new claim against the historical expense ledger to identify duplicate submissions — same merchant, amount, and date — and surface them to the approver with evidence before payment is processed."},{title:"Spend anomaly flagging",body:"AI monitors expense patterns at individual and team level, flagging statistically unusual spend — unexpected category shifts, rapid volume increases, and out-of-pattern merchants — for finance review without manual analysis."}]},{group:"AI Corporate HR Policy Generator",icon:"Globe2",tagline:"Stay compliant in every country without a legal review cycle for every regulatory change.",features:[{title:"Country-specific policy generation",body:"AI drafts compliant HR policies for any country by combining local employment law, regulatory requirements, and the organisation's own standards — producing a first draft that is legally grounded and ready for HR and legal review."},{title:"Regulatory change monitoring",body:"Continuously monitor employment law changes across all operating jurisdictions and automatically identify which existing policies are affected — surfacing a change impact assessment and a draft update before the effective date."},{title:"Policy gap analyser",body:"Compare the organisation's current policy library against a comprehensive regulatory checklist for each country and identify gaps — policies that are missing, outdated, or silent on a required topic — with remediation recommendations."},{title:"Employee acknowledgement compliance tracker",body:"Track which employees have read and acknowledged each policy version — by country, entity, and department — and automatically chase outstanding acknowledgements before audit periods or compliance deadlines."}]}],uB=[{title:"HR Efficiency",body:"Reduce HR case volume through conversational self-service, automate policy generation, and eliminate manual data reconciliation across systems."},{title:"Employee Experience",body:"Deliver a consumer-grade HR experience with instant answers, mobile expense capture, and a smooth first-day onboarding journey."},{title:"Compliance & Governance",body:"Stay ahead of local employment law changes with AI-generated policy updates, jurisdiction-aware leave rules, and complete audit trails."},{title:"Workforce Intelligence",body:"Move from reactive headcount reporting to predictive workforce planning with live skill maps, attrition forecasting, and scenario modelling."}],mB=["Visual Workflow Designer — drag-and-drop canvas with 100+ pre-built node types and live preview","LangGraph State Machine Engine — stateful, cyclical multi-agent orchestration with checkpoint persistence","AI Agent Nodes — embed and orchestrate AI agents as first-class workflow steps with model selection","Conditional Branching & AI Routing — LLM-evaluated rules for dynamic path selection based on content and context","Human-in-the-Loop (HITL) Gates — approval checkpoints with SLA enforcement, delegation, and escalation","Parallel Branch Execution — concurrent workflow branches with fan-out, fan-in, and merge synchronisation","Event-driven Triggers — webhook, cron schedule, AI-signal, and enterprise event source triggers","Sub-workflow Composition — nest and reuse workflows as building blocks across the enterprise","Error Handling & Retry Logic — configurable retry policies, circuit-breaker patterns, and fallback branches","Workflow Version Control & Rollback — immutable execution history with one-click rollback to any prior version","Real-time Execution Monitor — live step-by-step visualisation with latency telemetry and variable inspection","Integration Connectors Library — 500+ pre-built connectors to REST APIs, databases, and enterprise apps","Role-based Workflow Governance — designer, publisher, and executor roles with approval-gate ownership","Immutable Audit Trail — cryptographic log of every workflow execution for compliance and forensics","SLA Tracking & Breach Alerts — per

57-workflow SLA targets with predictive alerting and escalation chains","Multi-tenant Workflow Isolation — namespace-separated workflows with cross-tenant governance and quotas","AI-powered Workflow Optimiser — analyses execution patterns and auto-suggests latency and cost improvements","Natural Language Workflow Generation — describe a business process in plain English; AI builds the graph"],v3=["LangGraph stateful graph backend — supports cyclic, self-correcting agent loops that traditional tools cannot model","Human-in-the-Loop approval gates with full SLA enforcement — pause, redirect, or escalate any step","Parallel branch execution with dynamic merge — fan out to concurrent agents and reconvene at a collector node","500+ pre-built enterprise integration connectors — zero custom code to connect any system","Visual Workflow Designer with live debug console — step through executions with full variable inspection","Immutable cryptographic audit trail — every execution logged for regulatory compliance and forensic review"],w3=["Natural language workflow generation — describe a process; AI builds the LangGraph workflow graph","AI-evaluated conditional routing — LLM decides which branch to follow based on document content or data","Intelligent error recovery — AI analyses failures and proposes retry strategies or alternative execution paths","Predictive SLA alerting — ML model forecasts breach risk before it occurs from execution telemetry","Continuous optimisation recommendations — AI reviews historical runs and suggests graph refactoring","Agent-to-agent communication — shared-state message passing across multi-agent workflow nodes"],pB=[{title:"State Graph",description:"Every workflow is modelled as a 

57LangGraph StateGraph — nodes represent computation steps, edges represent transitions. State is typed, validated, and checkpointed at every node boundary."},{title:"Stateful Persistence",description:"Unlike stateless pipeline tools, LangGraph checkpoints workflow state after every node. Long-running workflows survive restarts, and humans can inspect or modify state at any point."},{title:"Cyclic Graphs",description:"LangGraph natively supports loops and cycles — enabling self-correcting agent patterns, retry logic with state memory, and iterative refinement workflows that DAG-only tools cannot express."},{title:"Human Interrupts",description:"Execution pauses at designated HITL nodes, persisting state until a human approves, rejects, or redirects. SLA timers and escalation chains activate automatically on breach."},{title:"Multi-agent Coordination",description:"Agents communicate through a shared, typed state object. Orchestrator nodes route work to specialist agents; reducer functions merge their outputs back into the main workflow state."},{title:"Streaming & Observability",description:"LangGraph streams token-level and event-level updates in real time. Every agent call, tool invocation, and state mutation is surfaced in the live execution monitor."}],hB=["AI-generated form layouts from plain-language descriptions — build a 20-field form in seconds with zero design effort","Conditional logic and branching workflows — show, hide, or route users to different sections based on their answers","Enterprise survey and NPS management with automated distribution and response analytics","Multi-step approval workflows embedded directly in forms — no external BPM tool required","Deep integration with ITSM, HRMS, GRC, and 200+ third-party systems via pre-built connectors"],fB=["Natural language form generation — describe the form you need; AI builds the fields, labels, and logic","AI-powered field validation and data enrichment — auto-complete, address lookup, and anomaly detection on submission","Intelligent response analysis — AI extracts themes, sentiment, and key insights from open-text submissions","Smart duplicate detection — AI identifies and merges repeat submissions or near-identical responses","Predictive form abandonment alerts — ML flags users likely to drop off and triggers contextual assistance"],j3=["Real-time space utilization heat maps built from badge, sensor, and booking data that expose underused floors, rooms, and desks across every site","Predictive maintenance scheduling that prevents unplanned downtime by acting on AI failure-risk scores before HVAC, electrical, or building systems break","Auto-prioritized work orders generated from requests, inspections, and sensor thresholds — ranked by criticality, SLA, and technician availability","Vendor and contractor governance with performance scoring, onboarding workflows, insurance tracking, and spend controls in one place","Automated compliance and inspection checklists with regulatory audit trails for every site, asset class, and statutory obligation","Facilities charge-back automation with cost centre allocation and budget-to-actual reporting for finance alignment","Energy optimisation workflows that turn consumption anomalies into assigned actions, measurable savings, and continuous sustainability improvement","Offline-capable mobile inspections with photo evidence, QR/barcode scanning, and automatic sync when teams reconnect"],N3=["AI demand forecasting for room and desk booking that right-sizes hybrid-work space and flags consolidation opportunities by floor, day, and team","Energy anomaly detection with AI alerts that distinguish equipment faults from occupancy changes and recommend the highest-impact corrective action","AI failure-risk scoring across HVAC, electrical, elevators, and building systems using IoT telemetry, age, maintenance history, and operating conditions","AI-generated executive briefings on utilization trends, maintenance backlog, energy anomalies, compliance exposure, and cost-saving opportunities","AI-assisted visitor pre-registration with identity capture, watchlist screening, arrival prediction, and automated entry logging","Intelligent work order routing that matches technicians by skill, location, certification, workload, and asset familiarity","Natural-language facilities assistant allowing employees to raise requests, find available rooms, report issues, and track work orders by conversation","Automated inspection checklist generation tailored to asset type, regulatory regime, site risk profile, and historical fault patterns","AI vendor performance scoring that detects declining service quality, predicts renewal risk, and recommends corrective actions before contract review","Space scenario modelling that compares consolidation, hybrid-work, and expansion options using predicted occupancy, cost, and employee demand"],gB=[{stage:"Request & Intake",heading:"AI turns every signal into the right action",description:"Requests arrive from employees, sensors, inspections, access systems, and scheduled maintenance. AI classifies the intent, identifies the affected asset or location, checks urgency, and creates a governed work item without manual triage.",points:["Natural-language intake through employee portal, assistant, email, or mobile","IoT threshold alerts automatically converted into actionable work orders","Duplicate request detection across buildings, rooms, and asset records","Priority scoring using criticality, occupancy impact, safety risk, and SLA"]},{stage:"Space & Scheduling",heading:"AI makes every square metre work harder",description:"Occupancy, booking, badge, and sensor data combine into a live picture of how the estate is used. AI forecasts demand and models consolidation, room conversion, and hybrid-work scenarios before decisions are made.",points:["Live occupancy heat maps by site, floor, room, desk, and time of day","Demand forecasting for rooms, desks, amenities, and visitor capacity","AI scenario comparison for consolidation, expansion, and hybrid working","Automated booking recommendations that reduce unused space and friction"]},{stage:"Work Order Execution",heading:"The right work reaches the right team first time",description:"AI prioritizes work by safety, business impact, asset risk, and SLA. It routes jobs to the best available technician or contractor and surfaces the asset context needed to resolve the issue efficiently.",points:["

57Skill, certification, location, workload, and asset familiarity in routing","AI-generated work orders from requests, inspection results, and sensors","Recommended resolution steps based on similar historical failures","Mobile execution with offline checklists, photos, QR scans, and sync"]},{stage:"Compliance & Inspection",heading:"AI prevents missed checks before they become exposure",description:"The platform knows which inspections, certificates, and controls each asset and site requires. AI generates the checklist, monitors completion risk, and escalates missing evidence before an audit or incident.",points:["Regime-aware inspection checklists generated for each asset class","Risk-based inspection frequency adjusted from historical faults","Evidence capture with photo, signature, timestamp, and location trail","Early warnings for overdue statutory checks and expiring certificates"]},{stage:"Closure & Insight",heading:"Every facility event improves the next decision",description:"On closure, AI updates asset history, summarizes the work, measures cost and SLA outcome, and feeds failure patterns into predictive maintenance. Leaders get a clear view of utilization, risk, and savings across the estate.",points:["Auto-generated closure summaries with root cause and next recommendation","Asset, location, vendor, and cost records updated from one completion event","Energy and maintenance trends converted into executive-ready briefings","Continuous learning loop from work orders, inspections, bookings, and outcomes"]}],xB=[{value:"30%+",label:"Reduction in reactive maintenance"},{value:"25%",label:"Improvement in space utilisation"},{value:"40%",label:"Faster facilities request resolution"},{value:"95%+",label:"Inspection and compliance completion"}],yB=[{problem:"Space decisions rely on occasional surveys and spreadsheets",solution:"AI combines live badge, sensor, booking, and demand data into utilization intelligence and scenario models for confident right-sizing."},{problem:"Maintenance is reactive and work queues are manually prioritised",solution:"Failure-risk scoring and automatic priority ranking turn building signals into preventive work before an outage occurs."},{problem:"Compliance evidence is scattered across sites and contractors",solution:"AI-generated checklists, risk-based scheduling, and a complete audit trail keep every obligation and piece of evidence visible."},{problem:"Energy anomalies are found after the bill arrives",solution:"AI distinguishes occupancy changes from equipment faults in near real time and recommends actions tied to measurable savings."}],A3=["AI-optimized scheduling and dispatch weighing technician skill, proximity, parts availability, and SLA risk to assign every job in seconds — no manual board management","Real-time technician tracking with dynamic ETA prediction and automated customer push notifications from dispatch through job completion","Predictive field service planning that recommends preventive visits based on AI failure-risk scores before assets generate reactive calls and emergency costs","Intelligent parts and inventory management with AI demand forecasting and automatic replenishment eliminating first-visit parts shortages across all depots","Contractor and third-party workforce management covering credentialing, job assignment, and live performance scoring against SLA commitments","Billing and time-capture integration automating work order costing, parts reconciliation, and invoice generation on job closure — zero manual reconciliation","Offline-capable mobile technician app — fully functional in basements, rural sites, and signal-dead zones with seamless sync on reconnect","Field workforce capacity forecasting — AI predicts volume spikes 30+ days ahead so staffing matches demand without overtime or idle time"],k3=["AI-generated job summaries and resolution notes capturing work performed, parts used, and recommended next steps — reducing admin time per job by up to 70%","SLA breach prediction engine that triggers proactive re-dispatch or escalation hours before a violation occurs — not minutes after it passes","Route and schedule optimisation that re-runs continuously throughout the day as jobs complete, cancel, or change priority — no manual re-planning","AI-powered first-time fix rate analysis identifying recurring failure patterns and recommending technician training or stock adjustments to close the gap","Customer sentiment scoring after every job with automatic follow-up triggers and service recovery workflows for low-satisfaction outcomes","Natural-language field analytics — ask plain-English questions about utilisation, travel time, and resolution trends across the entire service operation","AI failure-risk scoring per asset — integrates IoT telemetry, maintenance history, and age data to rank assets by breakdown probability","Intelligent parts recommendation engine — AI suggests parts needed before dispatch based on asset type, symptom, and historical resolution patterns","Technician skill-gap detection — AI identifies capability shortfalls from repeat-visit patterns and auto-generates training recommendations","Conversational job briefing — technicians ask the mobile app questions in natural language and get answers from asset history, KB, and prior resolutions"],bB=[{stage:"Job Request & Triage",heading:"AI classifies, prioritises, and queues every job instantly",description:"The moment a work request arrives — from a customer, a sensor alert, or a scheduled PM — AI reads the job type, asset history, urgency, and SLA window, and classifies it in milliseconds. No dispatcher touches it until it's already prioritised.",points:["Multi-source intake: portal, phone, IoT sensor, EAM trigger, and scheduled PM","Instant classification by job type, asset criticality, and SLA tier","Automatic priority scoring based on SLA deadline, customer tier, and asset risk","Duplicate and repeat-visit detection to flag systemic failures early"]},{stage:"Intelligent Dispatch",heading:"The right technician, right skills, right parts — every time",description:"AI matches each job to the optimal technician by evaluating skill certification, current location, travel time, van stock, and SLA risk simultaneously. The dispatch board becomes a confirmation screen, not a decision screen.",points:["Multi-factor optimisation: skill, proximity, parts availability, and SLA weighting","Continuous re-optimisation as jobs complete, cancel, or escalate during the day","Contractor and third-party workforce included in the same dispatch pool","AI-recommended parts list generated before dispatch based on asset and symptom"]},{stage:"In-Field Execution",heading:"AI guides technicians through every job",description:"On-site, the mobile app surfaces full asset history, AI-recommended resolution steps, parts lookup, and knowledge base articles. Technicians work faster and smarter — even offline in signal-dead environments.",points:["Offline-capable app with full asset history, job details, and parts lookup","AI-suggested resolution steps ranked by success rate on similar assets","Live knowledge base search with semantic retrieval — right answer without exact keywords","Conversational job briefing — technicians ask questions in natural language"]},{stage:"SLA & Escalation",heading:"Breaches predicted and prevented, not reported",description:"AI monitors every open job against its SLA window, calculates breach probability in real time, and triggers re-dispatch or escalation automatically. Customers receive proactive notifications throughout — no chasing required.",points:["Real-time SLA countdown with breach probability scoring per job","Automated re-dispatch to a closer technician 

57when breach risk crosses threshold","Multi-tier escalation chains with configurable owners and notification rules","Customer push notifications at dispatch, en-route, on-site, and completion"]},{stage:"Closure & Learning",heading:"Every job makes the operation smarter",description:"On job closure, AI writes the resolution summary, triggers CSAT collection, updates asset history, and feeds failure patterns back into the predictive maintenance model. The operation improves with every completed work order.",points:["Auto-generated closure summaries: work done, parts used, and next recommendations","Automated CSAT dispatch with sentiment scoring and low-score follow-up workflows","Asset history and CMDB updated automatically from job outcomes","First-time fix rate and failure pattern analysis feeding technician training recommendations"]}],vB=[{value:"35%+",label:"Increase in first-time fix rate"},{value:"40%",label:"Reduction in travel waste"},{value:"90%+",label:"SLA compliance with AI breach prediction"},{value:"70%",label:"Less admin time per job with AI summaries"}],wB=[{problem:"Dispatch is manual, skill-blind, and map-only",solution:"AI optimises every assignment across skill, proximity, parts, and SLA risk simultaneously — in seconds, not minutes of board-management."},{problem:"Parts shortages cause costly repeat visits",solution:"AI pre-calculates required parts per job from asset type, symptom, and resolution history — and auto-replenishes depot stock before shortages occur."},{problem:"SLA breaches are reported after they happen",solution:"Breach probability scoring runs continuously; re-dispatch and escalation trigger automatically while there is still time to act."},{problem:"Field data stays trapped in paper and verbal handoffs",solution:"AI generates structured resolution summaries on closure — feeding asset history, CMDB, and the predictive model automatically from every job."}],S3=["Omnichannel case management unifying email, chat, voice, social, and portal into a single queue with unified SLA enforcement across every channel","AI-suggested resolutions grounded in the knowledge base that reduce average handle time by up to 60% without agent retraining or configuration","Customer 360 view combining full interaction history, sentiment trend, account health, and product usage across every touchpoint in one panel","Proactive outreach automation that triggers remediation offers and notifications before customers report issues — turning reactive support into proactive service","Knowledge gap detection that clusters unresolved cases and automatically generates KB article drafts to close recurring deflection failures at scale","SLA enforcement with automated multi-tier escalation chains, manager notifications, and breach risk prediction hours ahead of deadlines","AI-powered agent onboarding assistant — new agents reach full productivity in days, not weeks, guided by AI context cards and suggested actions","Case deflection analytics — AI identifies which contact reasons are deflectable and generates self-service content to eliminate them systematically"],I3=["Real-time customer sentiment and intent analysis scoring tone throughout every interaction and surfacing early distress signals to agents and supervisors","Predictive escalation management that flags at-risk cases before SLA breach occurs and recommends the optimal escalation path and agent reassignment","Automated case classification and routing that assigns category, priority, and team in milliseconds with 92%+ accuracy — no manual triage queues","AI-generated case summaries and closure notes g

57iving agents picking up transferred cases instant full context with zero manual review or memory required","Conversational self-service AI on the customer portal handling 24/7 Tier-1 resolution without agent involvement for the majority of common request types","AI-powered CSAT and NPS analysis extracting themes, sentiment clusters, and trending failure patterns across 100% of survey responses — not a sample","Intelligent knowledge base search with semantic retrieval — agents and customers find the right answer even when they don't know the exact keyword","Generative AI response drafts — AI writes the first-draft reply grounded in KB, policy, and case history; agents review, personalise, and send","Agent next-best-action engine — AI recommends the next step (escalate, offer refund, send article, schedule callback) based on real-time case context","Continuous learning loop — every resolved case, approved draft, and CSAT score feeds back into the AI models to improve accuracy over time"],jB=[{stage:"First Contact",heading:"AI classifies and routes in milliseconds",description:"The moment a case arrives — via email, chat, voice, social, or portal — AI reads the intent, sets the priority, and routes to the right team or conversational bot. No manual triage. No queue-watching.",points:["Multi-channel intake with unified queue normalisation","Intent and urgency classification at 92%+ accuracy","Instant routing to specialist team, bot, or human agent","Priority override rules for VIP, breach-risk, and regulatory cases"]},{stage:"Triage & Context",heading:"Every agent starts with the full picture",description:"As an agent opens a case, AI assembles the Customer 360 — full history, open issues, sentiment baseline, product usage, and similar resolved cases — before the agent types a single character.",points:["Customer 360 auto-assembled from CRM, portal, and billing data","Sentiment baseline and trend from prior interactions surfaced immediately","Similar resolved cases and successful resolutions ranked by relevance","AI-generated case brief replacing the need to read a thread from scratch"]},{stage:"Resolution",heading:"AI drafts the answer before agents ask",description:"KB-grounded resolution suggestions, generative reply drafts, and a conversational AI bot handle the majority of Tier-1 contacts end to end. Complex cases get AI-assisted agent support throughout.",points:["Conversational self-service AI resolves common issues 24/7","AI reply drafts grounded in KB, policy, and case context","Semantic KB search surfaces the right article even without exact keywords","Next-best-action engine guides agents to the optimal resolution path"]},{stage:"Escalation & SLA",heading:"Breach predicted and prevented, not reported",description:"AI monitors every open case against its SLA, predicts breach risk hours ahead, and triggers escalation chains automatically. Supervisors intervene while there is still time to act.",points:["Real-time SLA countdown with breach probability scoring","Automated escalation chains with configurable tiers and owners","Supervisor dashboards showing at-risk cases ranked by urgency","AI re-routing to available agents when primary owner is unavailable"]},{stage:"Closure & Learning",heading:"Every case makes the system smarter",description:"AI writes closure notes, dispatches CSAT surveys, and analyses every response at scale. Unresolved case clusters automatically generate KB article drafts — so the same issue never requires an agent twice.",points:["Auto-generated closure summaries with root cause and resolution steps","CSAT and NPS dispatch with AI-extracted theme and sentiment analysis","Knowledge gap detection generating KB draft articles from case clusters","Continuous model improvement from resolved cases, approvals, and scores"]}],NB=[{value:"60%",label:"Reduction in average handle time"},{value:"40%",label:"Cases resolved by AI without agents"},{value:"92%+",label:"Case classification accuracy"},{value:"100%",label:"CSAT responses analysed — not sampled"}],AB=[{problem:"Agents switch between 5+ tools per case",solution:"One unified workspace combines case, customer 360, KB, sentiment, and AI suggestions — agents never leave the pane."},{problem:"Knowledge bases are static and keyword-only",solution:"Semantic retrieval surfaces the right answer even without exact keywords, and gap detection keeps the KB current automatically."},{problem:"Escalations happen after breaches, not before",solution:"Predictive SLA breach scoring triggers escalation chains hours ahead of deadlines — not minutes after they pass."},{problem:"CSAT surveys cover 5% of interactions",solution:"AI analyses 100% of resolved cases for sentiment, themes, and failure patterns — no s

57ampling bias, no blind spots."}],C3=["Continuous third-party risk scoring across financial, cyber, compliance, and operational dimensions — updated automatically as signals change, not refreshed once a year","Contract lifecycle management with AI-extracted obligations, renewal milestone alerts, and clause deviation flagging against approved templates before signature","AI-powered vendor risk reports generated in one click from uploaded evidence and questionnaire responses — eliminating weeks of manual preparation for audit cycles","Spend analytics and consolidation engine that identifies vendor overlap, redundancy, and negotiation leverage across the entire portfolio without manual data wrangling","Preferred vendor catalogue with AI-assisted selection scoring candidates against risk profile, capability match, and commercial pricing before procurement commits","Regulatory compliance monitoring with automated checks against GDPR, SOC 2, ISO 27001, and DORA — updated as frameworks evolve so teams never chase a moving target","Incident and breach notification tracking that maps vendor SLA obligations and regulatory reporting timelines to automated workflow triggers the moment an event is detected","Centralized audit trail and evidence repository linked to GRC — giving auditors complete third-party coverage without evidence-gathering fire drills at year end"],M3=["AI-guided onboarding questionnaires that adapt to vendor category and risk tier, with automated document verification and gap flagging — no manual chasing of missing evidence","Continuous risk signal ingestion from external data sources — financial filings, breach databases, and news feeds — scored and surfaced in real time as posture shifts","Natural-language vendor risk summarisation that translates complex scoring models into plain-English executive briefings on demand — readable in seconds, not hours","Clause deviation detection that compares uploaded contracts against approved templates and highlights non-standard terms for legal review before execution","Renewal and expiry management with AI-predicted risk trajectory alerts ensuring renegotiations begin before leverage is lost and deadlines are missed","AI performance scorecard generation that turns raw KPI data into trend narratives and ranked improvement recommendations ahead of vendor review meetings","Spend consolidation AI identifying duplicate vendor relationships, shadow procurement, and pricing anomalies across business units for immediate cost recovery","Regulatory gap analysis that maps each vendor's evidence against current framework requirements and generates a remediation plan with owner assignments","AI-powered vendor segmentation that assigns the right diligence depth, review cadence, and controls to every vendor category automatically","Predictive vendor failure alerts using financial distress, service degradation, security events, and missed commitments as leading signals before failure occurs","Conversational vendor intelligence allowing procurement, security, and business owners to ask plain-English questions about exposure, spend, or renewals"],kB=[{stage:"Onboarding & Due Diligence",heading:"AI compresses weeks of diligence into days",description:"When a new vendor relationship begins, AI-guided questionnaires adapt to vendor category and risk tier, verify uploaded documents automatically, and flag evidence gaps before onboarding progresses — no manual chasing, no spreadsheet tracking.",points:["Adaptive questionnaire generation by vendor category and risk tier","Automated document verification with completeness gap detection","Preliminary risk profile scored before the first human review","Integration with preferred vendor catalogue for fast-lane pre-vetted suppliers"]},{stage:"Risk Scoring",heading:"Continuous scoring, not annual snapshots",description:"AI ingests financial filings, breach databases, regulatory updates, and news feeds in real time — updating each vendor's risk posture the moment signals change, surfacing alerts before exposure becomes incident.",points:["Multi-dimensional scoring across financial, cyber, compliance, and operational risk","Real-time external signal ingestion from breach and financial databases","Natural-language executive briefings generated on demand from score changes","Threshold-based alerts triggering review workflows automatically"]},{stage:"Contract Lifecycle",heading:"AI reads every clause — so your team doesn't have to",de

57scription:"From upload to expiry, AI extracts obligations, flags clause deviations against approved templates, and tracks every milestone. No contract renews by surprise and no non-standard term slips through unreviewed.",points:["AI-extracted obligations, payment terms, and SLA commitments at upload","Clause deviation flagging against approved legal templates before execution","Renewal and expiry alerts timed to ensure renegotiation leverage is preserved","Full contract version history linked to vendor record and audit trail"]},{stage:"Performance Tracking",heading:"KPI evidence becomes intelligence, automatically",description:"Vendor scorecards are generated from raw KPI data — trend narratives, risk trajectory forecasts, and ranked improvement recommendations arrive before review meetings, not during them.",points:["AI-generated performance scorecard with trend analysis and benchmarks","Spend analytics identifying overlap, consolidation opportunities, and savings","Anomaly detection flagging KPI deterioration before it becomes a breach","Review meeting packs auto-generated with prior period comparisons"]},{stage:"Audit & Compliance",heading:"Audit-ready evidence — always on, never scrambled",description:"Regulatory compliance checks run continuously against GDPR, SOC 2, ISO 27001, and DORA. When an audit window opens, evidence is already compiled, gap analysis is already done, and the remediation plan is already assigned.",points:["Continuous framework checks against GDPR, SOC 2, ISO 27001, and DORA","AI-generated gap analysis mapped to each vendor's evidence on file","Remediation plan with owner assignments generated automatically","Centralized evidence repository with full chain-of-custody audit trail"]}],SB=[{value:"65%",label:"Reduction in third-party risk exposure"},{value:"70%",label:"Faster vendor onboarding"},{value:"30%",label:"Decrease in contract renewal misses"},{value:"3×",label:"Faster compliance evidence gathering"}],IB=[{problem:"Risk assessments are annual, not continuous",solution:"AI ingests breach databases, financial filings, and regulatory feeds in real time — so risk posture is current to the hour, not the calendar quarter."},{problem:"Contracts live in inboxes, not a governed system",solution:"Every contract is extracted, obligation-mapped, and milestone-tracked the moment it's uploaded — renewals and deviations surface automatically."},{problem:"Vendor performance reviews rely on anecdotes",solution:"AI-generated scorecards with trend analysis and improvement recommendations turn vendor meetings from opinion sessions into evidence reviews."},{problem:"Audit prep is a month-long fire drill",solution:"Continuous compliance monitoring and a centralised evidence repository mean the audit pack is ready before the window opens — no scrambling required."}],CB=["Consulting Agent maps current-state processes, gathers requirements, and translates business outcomes into a practical ServiceNow implementation roadmap","Architecture Agent designs the target ServiceNow architecture, data model, integrations, roles, workflows, and module dependencies before configuration begins","Implementation Agent configures, tests, documents, and prepares the ServiceNow solution for controlled deployment with implementation evidence at every stage","Five core ITSM modules delivered as one connected programme: Incident, Problem, Change, Service Request, and Service Catalog","Cross-module process design connects incidents to problems, changes, requests, catalog items, approvals, SLAs, and knowledge from the start","Human-led governance with AI acceleration — every recommendation, configuration decision, test result, and approval remains reviewable by the implementation team","Implementation playbooks, reusable configuration patterns, and delivery documentation reduce rework and create a repeatable ServiceNow rollout method","Go-live readiness support covering UAT, migration validation, training handover, cutover planning, and post-launch stabilization"],MB=["Consulting Agent uses discovery notes, stakeholder inputs, process documentation, and ITIL patterns to recommend the right implementation scope","Architecture Agent analyses module dependencies, data flows, integration boundaries, security roles, and scale requirements before design sign-off","Implementation Agent generates configuration guidance, workflow logic, test scenarios, migration checklists, and technical documentation for the delivery team","AI-assisted Incident Management design for categorisation, assignment, prioritisation, impact/urgency, SLAs, major incidents, and knowledge linkage","AI-assisted Problem Management design for recurring-incident analysis, root-cause workflows, known errors, workarounds, and problem-to-change linkage","AI-assisted Change Management design for standard, normal, and emergency changes, risk assessment, approvals, CAB workflows, testing, and rollback","AI-assisted Service Request design for request fulfilment, approvals, assignment groups, SLAs, notifications, and fulfilment task orchestration","AI-assisted Service Catalog design for catalog structure, item definitions, variables, policies, approvals, workflows, and user experience","RAG-grounded implementation guidance retrieves approved ServiceNow documentation, project decisions, process maps, and enterprise standards","Human-in-the-loop checkpoints keep architects, process owners, and ServiceNow administrators in control of every generated recommendation and configuration change"],T3=["Instant PR analysis with AI quality score, inline comments, and executive summary in under 30 seconds","837-rule architecture analysis engine with per-rule AI remediation guidance and before/after code examples","Multi-layer security scanning: SAST, secret detection, dependency audit, container scan, and taint analysis","One-click AI-generated code fixes with context-aware diff patches — no hallucinated or generic templates","DORA metrics benchmarked against Elite/High/Medium/Low tiers with AI bottleneck identification","Real-time quality gates that block, warn, or pass every PR against configurable thresholds automatically","AST-powered taint analysis tracking untrusted data from source to sink with 94% true-positive rate","Executive AI reports: security posture, license compliance, container findings, compliance framework mapping"],E3=["Multi-LLM routing engine: OpenAI for c

57ode review, Claude Sonnet for security, Gemini Pro for complexity","AI Security Advisor explains every vulnerability in plain English with OWASP mapping and exploit scenarios","AI-generated cyclomatic and cognitive complexity refactoring guidance with tailored before/after examples","Anchor-pair clone detection with AI extract-function and module-consolidation suggestions","AI DORA insights correlating code quality scores with deployment failure rates and recommending tier advancement","Vision AI analysis of architecture diagrams, UI screenshots, database ER schemas, and infrastructure diagrams","AI technical debt priority scoring by business impact, effort to fix, and production risk","Natural language PR summaries: what changed, what risk was introduced, and what must be addressed before merge","AI developer skill-gap detection with constructive mentoring recommendations framed as growth opportunities","Agentic code repair roadmap — autonomous AI agent opening fix PRs and self-reviewing before submission (2026)"],TB=[{stage:"01",heading:"Connect",description:"Link any repository in seconds — GitHub, GitLab, Azure DevOps, or Bitbucket — or upload files directly. GitHub token authentication unlocks 5,000 API requests per hour versus 60 unauthenticated.",points:["Paste a repository URL or drag-and-drop files and zip archives","GitHub OAuth token for private repos and higher API rate limits","Continuous monitoring mode: auto-trigger on every push or PR open","Real-time progress bar with pause, resume, and stop controls"]},{stage:"02",heading:"Analyze",description:"AI-powered multi-layer analysis runs automatically across every file: AST parsing for structural understanding, security scanning across SAST, secrets, dependencies, and containers, plus architecture rule enforcement.",points:["Tree-sitter AST parsing across 35+ languages for code-structure-level understanding","Parallel AI code review (OpenAI), security analysis (Claude), and complexity scoring (Gemini)","837 architecture rules applied across scalability, security, performance, and design patterns","Taint analysis tracks user-controlled data end-to-end from HTTP sources to dangerous sinks"]},{stage:"03",heading:"Prioritize",description:"Quality gates evaluate every PR against configured thresholds instantly. AI risk scoring ranks issues by severity, exploitability, and business impact so teams focus remediation effort where it counts most.",points:["Quality gates return PASS, FAIL, WARN, or INCONCLUSIVE — never a false pass on missing data","CVSS-aligned severity classification: CRITICAL blocks PR, HIGH requires fix before merge","AI technical debt priority matrix: impact vs. effort scoring pre-populated by the AI engine","DORA metrics benchmarked against Elite/High/Medium/Low industry tiers in real time"]},{stage:"04",heading:"Fix",description:"One click generates a context-aware AI fix patch for any detected issue. The AI uses full file context — not a generic template — and presents a side-by-side diff with a plain-English explanation before you apply.",points:["AI Fix Service uses full file content, surrounding functions, and imports as context","Fixes cover security (parameterized queries, sanitization), performance, architecture, and style issues","Honest 'fix unavailable' signal when the LLM cannot generate a reliable patch — no hallucinated code","Analysis re-runs automatically after each fix to confirm the issue is resolved"]},{stage:"05",heading:"Monitor",description:"Track engineering performance over time with DORA metrics, per-developer quality trends, and AI-generated executive reports. AI identifies inflection points, correlates quality scores with failure rates, and recommends process improvements.",points:["Deployment frequency, lead time, MTTR, and change failure rate across all repositories","Per-developer and per-team quality scores, PR review times, and technical debt trends","AI narrative reports exportable to PDF, JSON, and CSV for auditor and executive audiences","Scheduled report delivery and comparison reports (this period vs. last period)"]}

57],EB=[{value:"80%",label:"Faster code reviews"},{value:"95%",label:"Vulnerability detection rate"},{value:"60%",label:"Technical debt reduction"},{value:"4×",label:"Increase in deploy frequency"},{value:"2700+",label:"Language rules enforced"},{value:"35+",label:"Languages supported"},{value:"AI Rule Builder",label:"Chat-driven rule generation"}],RB=[{problem:"Manual code review bottlenecks slow release cycles. Senior engineers spend hours reviewing PRs that could be resolved in seconds — delaying deployment and draining engineering capacity.",solution:"AI reviews every PR in under 30 seconds with the expertise of a senior engineer — inline comments, quality scores, security flags, and an executive summary delivered before the first human reviewer opens the diff."},{problem:"Pattern-matching security scanners miss complex multi-hop injection chains. Regex-based tools cannot track tainted data flowing through function calls, variable assignments, and module boundaries.",solution:"AST-powered taint analysis models the full data flow graph from every HTTP input, file read, and external API response down to SQL executors, shell calls, and HTML renderers — catching vulnerabilities no regex can see."},{problem:"Technical debt accumulates invisibly until it causes production incidents. Without continuous measurement, teams have no objective signal for when debt has become a release risk.",solution:"AI continuously quantifies debt across five categories — code smells, architecture violations, security debt, test debt, and dependency debt — and ranks each item by business impact and remediation effort."},{problem:"Architecture drift goes undetected until refactoring costs are prohibitive. Coding standards encoded in documents are never enforced at the point of commit.",solution:"837 database-driven architecture rules covering scalability, security, performance, layering, design patterns, dependency management, and naming are enforced on every commit and PR automatically."}],R3=[{id:"grc",name:"AI Powered Governance, Risk & Compliance",shortName:"AI Powered GRC",icon:We,desc:"Unify governance, risk management, and compliance across 28 global regulatory frameworks in a single, audit-ready module.",capabilities:["28 global framework support (SOX, SOC2, HIPAA, etc.)","Continuous control monitoring and testing","Risk register and treatment workflows","Policy lifecycle management","Automated evidence collection for audits","AI-powered vendor risk and third-party due diligence","Risk-based, IIA-aligned audit planning and management","One-click AI compliance reports from uploaded evidence","Bow-tie mapping of risks, controls, and consequences","AI-generated findings, root cause, and remediation plans","Incident management with regulatory breach notification","Configurable risk appetite and tolerance breach alerts","MCP server exposing live risk and compliance data to AI clients","Centralized evidence storage with object-storage linkage","Executive dashboards for risk posture and audit performance"],highImpactFeatures:IF,aiFeatures:CF,cta:"Explore AI Powered GRC",href:"/grc",keywords:"governance risk compliance audit sox hipaa gdpr iso 27001 pci dss nist regulatory framework policy management vendor risk third party risk control testing evidence collection audit management risk assessment risk register enterprise risk management erm internal audit external audit compliance monitoring regulatory reporting data privacy"},{id:"itsm",name:"AI Powered ITSM",shortName:"AI Powered ITSM",badge:"100+ AI Features",icon:Xa,desc:"AI-driven ITIL 4 & ITIL 5 compliant service management that streamlines the entire service lifecycle.",capabilities:["Incident Management","Service Request","Service Catalogue","CMDB","Problem Management","Change Management","SLA Management","Knowledge Management","Service Portfolio","Release Management","Autonomous incident triage and routing","AI-powered root cause analysis","Predictive change risk scoring","SLA automation and escalation","Self-service knowledge base with AI search","AI Powered Form Builder, Workflow, Reporting Microservices"],highImpactFeatures:MF,aiFeatures:TF,cta:"Explore AI Powered ITSM",href:"/itsm",keywords:"itsm service management itil incident management problem management change management service desk help desk service catalog service catalogue knowledge management request management sla management cmdb configuration management asset service request fulfilment event management release management service level agreement it operations supp

57ort ticket ticketing system"},{id:"ai-auditor-suite",name:"AI Auditor Suite & Service Management AI Studio",shortName:"AI Auditor Suite",badge:"100+ AI Capabilities",icon:be,desc:"End-to-end AI coverage across every ITSM and ESM domain — 21 specialist AI Auditors, a Cross-Auditor Correlation Engine, major incident prediction, change risk scoring, and knowledge gap intelligence in one platform.",capabilities:["21 specialist AI Auditors — SOX, HIPAA, GDPR, SOC 2, FedRAMP, ISO 27001, PCI-DSS v4.0, HITRUST, DORA, ISO 22301","Cross-Auditor Correlation Engine — causal chains and compound risks across all 21 domains","Major incident predictor — 87% accuracy, 2–6 hour early warning, 45% P1 event reduction","Automated incident classification — 92%+ accuracy, 75% triage time reduction, instant routing","Change risk scoring (0–100) with CAB recommendation engine and conflict detection","AI-generated change plans — implementation, rollback, test, release, and compliance documentation","Knowledge gap detection and automatic KB article generation — 3× faster, 68% deflection lift","Problem management AI — candidate detection, duplicate matching, and batch root cause analysis","Capacity & workforce forecasting — 90-day horizon, bottleneck analysis, attrition risk modelling","Proactive anomaly monitoring with compound-risk scoring across infrastructure and process layers","AI resolution enrichment with approval workflow and continuous training feedback loop","70% audit prep time reduction · 40% MTTR reduction · 3.2× faster compliance readiness"],highImpactFeatures:Pg,aiFeatures:Og,cta:"Explore AI Auditor Suite & Service Management AI Studio",href:"/ai-auditor-suite",keywords:"ai auditor audit suite compliance auditor incident auditor change auditor problem auditor major incident mttr correlation engine service management studio sox auditor hipaa auditor iso auditor vendor auditor change risk auditor patch auditor vulnerability auditor knowledge auditor sla auditor"},{id:"itam",name:"AI Powered IT Asset Management",shortName:"AI Powered ITAM",badge:"50+ AI Features",icon:Nr,desc:"Complete visibility across software licenses, hardware assets, and their lifecycle — with built-in compliance enforcement.",capabilities:["Asset Life Cycle Management — Procurement to Decommission","Discovery","Hardware Asset Management","Software Asset Management","Governed Software Delivery & Deployment","S

57oftware License Compliance with Normalization and Auto Software Cataloging","Asset Forecasting","Preventive and Predictive Maintenance","Contract Management","Vendor Management","Shadow IT & Prohibited Software Detection","Warranty, Subscription & Renewal Tracking","Risk Analysis","Cloud & SaaS Asset Spend Optimisation with Licence Right-Sizing","AI-Powered Asset Refresh Forecasting and Budget Planning","End-of-Life and End-of-Support Tracking with Automated Remediation Alerts"],highImpactFeatures:uF,aiFeatures:mF,cta:"Explore AI Powered IT Asset Management",href:"/itam",keywords:"itam it asset management software asset management sam hardware asset management ham asset discovery software license licence management shadow it saas management cloud asset spend optimisation asset lifecycle procurement decommission cmdb inventory asset tracking depreciation contract management warranty renewal end of life end of support eosl eol"},{id:"agentic-studio",name:"Agentic 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57al terms and breach detail","SLA metric register with target vs. actual tracking","Supplier scorecard with per-domain scores, trend and review cadence"]},{name:"Third-Party Risk Register & Assessment Templates",desc:"Formal inherent/residual risk register and repeatable vendor assessment methodology.",highImpact:["AI-powered risk assessment and mitigation planning across ten risk categories, with inherent-vs-residual scoring","Structured control scoring across Information Security, Data Privacy, Business Continuity, Compliance, Financial Viability and Subcontractor Management"],standard:["Risk register with likelihood/impact, treatment, owner and control effectiveness","Six assessment templates (Standard, Enhanced Due Diligence, Critical Vendor, IT/Cyber, Financial Stability, ESG)"]},{name:"Vendor Document Repository & Self-Service Portal",desc:"A shared evidence store and a no-login vendor portal that turns due diligence into self-service.",highImpact:["AI reads uploaded evidence and flags whether it meets due-diligence expectations, with a one-click compliance check","AI generates a fully custom questionnaire from vendor context and emails a unique, no-login portal link","Automatic response scoring, AI risk review and AI evidence-compliance scanning"],standard:["Central document repository with category, expiry date and status","Questionnaire register with due dates, auto-score and submitted/reviewed state","Notification centre for due/expiring items, breaches and missing evidence"]}]},{icon:Pt,title:"Internal Audit Suite",tagline:"Risk-based, IIA-aligned audit management, end to end.",modules:[{name:"Audit Planning & Universe",desc:"Plan, prioritise and track the organisation's full population of auditable entities.",highImpact:["AI Risk-Based Audit Scoring evaluates inherent risk, control environment, last-audit rating, complexity and regulatory exposure into a composite score and priority","AI-designed sampling methodology recommends sample size, stratification and rationale"],standard:["Audit Universe Builder with entity type, department, owner, data classification and scope flag","Audit register spanning 17 frameworks with KPI cards for planned/in-progress/completed audits"]},{name:"Fieldwork, Testing & Evidence",desc:"Execute fieldwork with structured, evidence-backed workpapers instead of unstructured notes.",highImpact:["One-click AI Compliance Report reads uploaded evidence and produces a compliance score, executive summary, findings, root cause and remediation","AI-recommended framework controls with AI-generated per-control checklists"],standard:["Control Testing Sheet, Fieldwork Tracker and Evidence Collection Log with due/received dates and status","Evidence upload with automatic RAG ingestion; compliance progress bar and risk-level badges"]},{name:"Findings, KPIs & Remediation",desc:"Turn findings into accountable, board-visible actions and track audit-function performance.",highImpact:["AI Draft Findings generates root cause, recommendation, regulatory reference and management response","AI Add-KPIs suggests IIA-aligned KPIs aware of what's already tracked, for bulk addition","AI Management Action Plan drafting turns a finding into structured actions and success criteria"],standard:["Findings register with severity, status, repeat-finding and board-reported flags","KPI Dashboard across Efficiency, Quality, Timeliness, Coverage, Risk Management and Stakeholder categories","Issue Aging Dashboard and Management Action Plan register"]}]},{icon:ct,title:"Enterprise Risk Management Suite",tagline:"From risk register to predictive risk intelligence.",modules:[{name:"Enterprise Risk Management & ERM Suite",desc:"Executive risk-universe view plus emerging, scenario, strategic and dependency risk registers.",highImpact:["Configurable risk-appetite statements and tolerance thresholds with automatic breach monitoring; scenario stress tests apply likelihood/impact shocks","Bow-tie mapping of causes → preventive controls → top event → mitigating controls → consequences","AI scan for emerging risks, AI scenario anal

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57ource/target frameworks and produces evidence-backed mappings for bulk import","AI-generated cross-framework mappings exposing direct/partial/no-coverage gaps automatically"],standard:["Categorized policy library with governance/ownership, scope, controls and evidence/testing tabs; PDF/Word export","Regulatory Crosswalk register and Policy Management lifecycle register with version and review dates"]},{name:"Corrective Action Register (CAR)",desc:"Manage corrective, preventive and systemic actions from finding to effectiveness verification.",highImpact:["AI Portfolio Insights analyzes the entire CAR portfolio","Per-record AI analyzer generates root-cause analysis, action plan, milestones and containment applied directly to the form"],standard:["CAR register with source, severity, root-cause category, milestones and overdue detection"]}]},{icon:oa,title:"Resilience, Privacy & Cyber Suite",tagline:"Incidents, privacy, continuity, financial controls and cyber GRC.",modules:[{name:"Incident Management & Identity Governance",desc:"Incident-response governance, CAPA and IAM controls in one hub.",highImpact:["AI CAPA generation from an incident description produces title, root-cause method, actions, assignee, priority and regulatory linkage","Permission-matrix role modelling with built-in identity-provider connection testing"],standard:["CAPA register with open/closed status and due-date ageing; 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financial-controls register with effectiveness tracking"]},{name:"Cybersecurity GRC & RBI Model Risk Management",desc:"Enterprise cyber-GRC and AI/ML model risk governance aligned to RBI's 2026 guidance.",highImpact:["AI asset-criticality scoring (ISO 27005/NIST SP 800-60), AI risk narratives, AI ISO 27001 assessments with Annex A mapping and Treat/Transfer/Tolerate/Terminate recommendations","AI Shadow-IT detection, AI vendor scoring and AI vulnerability triage/clustering/executive summaries","AI model-inventory generator under RBI MRM Guidance 2026, batch-generating up to 100 models with tiering and per-model compliance assessment"],standard:["CMDB asset register with CSV import/validation; vulnerability dashboard (Total, P1 Critical, Actively Exploited, Remediated)","Model registry with methodology, tier and regulatory metadata"]}]},{icon:be,title:"AI Platform Layer",tagline:"The shared intelligence layer every suite above draws on.",modules:[{name:"Document & Content Intelligence",desc:"Turns uploaded regulatory, contract, evidence and policy documents into structured GRC intelligence.",highImpact:["Regulatory Text Ingestion identifies obligations, themes and affected areas with an implementation action list","Contract AI Review produces a contract risk score /100, clause analysis, red flags and key dates","Evidence Adequacy Scoring rates uploaded evidence /100 against a control objective with audit-readiness assessment","Policy Gap Comparison benchmarks a policy against ISO 27001, NIST CSF, SOC 2, PCI-DSS, GDPR, ISO 31000, COBIT, CIS or DORA with a phased remediation roadmap","Obligation Extraction pulls binding duties, deadlines and evidence requirements, flagging high-risk obligations and quick wins"],standard:["Five-tab workflow (Regulatory, Contract, Evidence, Policy Gap, Obligations) with drag-and-drop upload and severity/status badges"]},{name:"AI Knowledge Generation & Narrative Reporting",desc:"Converts live GRC data and meeting documents into training content, board narratives and regulatory filings.",highImpact:["Generates expert Q&A training content by industry vertical and compliance topic with one-click Markdown export","Board Report generator snapshots live risk, compliance, audit, vendor and policy data into an executive narrative","Automated regulatory filing drafting for MAS, FCA, DORA, PRA, GDPR/ICO, SEC, FINMA and APRA","Meeting Minutes AI Summariser extracts decisions, action items and regulatory commitments and maps them to GRC records"],standard:["Collapsible, copyable generated sections with live data preview cards; 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fixed KPI templates",xpedioo:"AI-drafted findings; AI-suggested, IIA-aligned KPIs on demand"},{capability:"Policy vs. framework mapping",legacy:"Manual crosswalk spreadsheets",xpedioo:"AI-generated crosswalks 

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evaluate first, then advanced scripts, then business rules, falling back to default AI reasoning"]},{name:"Case Study: Automating Active Directory Operations",desc:"A real customer configuration: one agent manages the full AD user lifecycle end-to-end through eight coordinated business rules.",highImpact:["Get AD User and Create AD User run as Action rules, while Manager Change and Account Reactivation trigger Approval and Security review before anything executes","Privileged Role Assignment gets Enhanced Monitoring, Employee ID gets a Validation data-integrity check, and Concurrent Login Detection raises a real-time Security alert"],standard:["Every critical operation is audited automatically — giving IT and HR a self-service front door for account requests that used to need a ticket, while giving security and compliance more oversight than a manual process, not less"]},{name:"Multi-LLM Intelligence & Knowledge (RAG)",desc:"Agents reason with the right model for the job and ground answers in the organization's own documents.",highImpact:["Multi-LLM Provider Support — Anthropic, OpenAI, Google Gemini, Meta Llama and DeepSeek through one centralized configuration, avoiding vendor lock-in","Unstructured Data Ingestion — PDF, Word, Excel, images (with OCR), text, logs, JSON, CSV and Markdown all attach to an agent as a live knowledge source","Hybrid Search RAG Pipeline — sentence-aware chunking, synonym-aware embeddings, and a hybrid of keyword and semantic (cosine similarity) search"],standard:["Per-Agent Tuning — each agent has its own result count and similarity threshold, so a compliance agent can be stricter than a general FAQ agent","Knowledge Chat & Search — a standalone experience to query the knowledge base directly, independent of a live conversation","Structured Output Validation — machine-readable model responses are schema-validated with automatic self-correction retries"]}]},{icon:vi,title:"Multi-Agent Orchestration, A2A & MCP",tagline:"Agents aren't 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trail before publishing","Knowledge-Base Fallback answers from grounded content rather than guessing when no agent confidently matches","Loop Protection detects and stops recursive transfer loops with a graceful fallback response"]}]}

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the Platform Integrations Hub exposes the full, current catalog"]},{name:"Workforce Management",highImpact:["Queue Settings configure how conversations queue and how auto-assignment behaves, switchable mid-shift","SLA Management defines response and resolution targets per queue or priority, with visibility into breaches before they happen"],standard:["Agent Teams & Skills Management groups and tags agents so routing matches the right expertise, not just the next available body","Business Hours drive after-hours routing, auto-replies and escalation automatically","Capacity-Aware Reassignment guards against overloading an already-full agent","Conversation Tags support flexible categorization for reporting, routing and follow-up campaigns"]}]},{icon:ze,title:"Quality, Coaching & Customer Experience",tagline:"Automation only scales when you can measure whether it's working — and supervisors can help agents in the moment.",modules:[{name:"Quality Assurance & Confidence Gating",highImpact:["Confidence Gating scores every AI response in real time with a blended heuristic + LLM model; low-confidence responses route to a human instead of sending as-is","QA Scoring computes and stores structured quality scores per conversation across the whole AI and human workforce"],standard:["Failure Visibility & Retry surfaces a persistent failure indicator and a clear retry path when a channel or AI call fails","Language Detection Resilience keeps European-language conversations from being silently mislabeled as English, and retries automatically once an outage clears"]},{name:"Supervisor Tools: Coaching & Live Monitoring",highImpact:["Whisper Mode lets supervisors send private, real-time coaching notes into a live conversation that only the agent sees","Supervisor Live-Queue Overview gives a real-time dashboard of every in-progress conversation and which team it routed to"],standard:["Supervisor Monitoring drops into any live conversation to observe quality without disrupting the agent's flow","Reassignment Controls offer one-click reassignment respecting skills, capacity and business hours","Agent Notifications alert agents to new assignments via sound and browser notices, even when the tab isn't focused"]},{name:"Customer Experience: CSAT, Sentiment & Memory",highImpact:["CSAT & Satisfaction Surveys capture a post-conversation score with zero login friction, feeding a dedicated analytics view","Churn Risk Alerts flag conversations showing churn-risk signals in real time, broadcast directly to agent and supervisor views","Next Best Action (NBA) recommends the agent's next move — offer a discount, escalate, follow up — based on conversation context"],standard:["Real-Time Emotion Detection analyzes tone in the background and surfaces frustration before it escalates","Customer Memory builds durable, per-customer context across conversations so returning customers aren't starting cold"]}]},{icon:Ki,title:"Analytics, BI & AI-Driven Pattern Detection",tagline:"Dashboards for the questions supp

57ort leaders actually ask, plus AI that mines conversations for patterns a human analyst would take weeks to find.",modules:[{name:"Analytics & Business Intelligence",highImpact:["Chat Analytics covers volume, resolution time, channel mix and outcome trends, filterable by team, agent and time range","Agent Scorecards compute per-agent performance summaries with daily trend detail directly from conversation history","LLM Cost Tracking logs estimated token usage and cost by model, giving finance-grade visibility per conversation, agent and organization"],standard:["CSAT Analytics segments satisfaction trends by agent, team or channel to catch quality drift early","Staffing Forecast computes on-demand staffing recommendations from historical volume and external calendar events"]},{name:"AI Analytics: Clustering, Anomalies & Predictive Signals",standard:["Topic Clustering automatically groups conversations into emerging topics, surfacing what customers are contacting support about this week vs. last","Anomaly Detection flags unusual spikes or patterns in volume or outcomes","Conversation Intelligence extracts deeper structured insight across the corpus, feeding dashboards and proactive alerts","Weekly Briefing automates a Monday summary of volume, quality and notable trends for leadership","Competitor Intelligence detects and logs competitor mentions inside conversations for product and sales visibility","Knowledge-Gap Detection identifies questions the knowledge base couldn't answer well and auto-drafts a KB article to close the gap"]}]},{icon:We,title:"Governance, Security & Compliance",tagline:"Enterprise buyers need proof, not promises — and the platform is designed to serve more than one tenant safely.",modules:[{name:"Compliance, GDPR & Audit Trail",highImpact:["Compliance Export generates auditor-ready PDF exports of conversation records and quality data on demand, without exposing the underlying database","GDPR Data Management provides dedicated tooling for data subject requests — locating, exporting and deleting a customer's data across the platform","Enterprise RBAC — granular, resource-level role-based access control with custom roles and full audit logging of who accessed or changed what","AI Safety Guardrails — a defense-in-depth content moderation stack (safety middleware, LLM-layer validation, a dedicated safety engine) detects prohibited content, jailbreaks and prompt injection before a response reaches a customer"],standard:["Outbound Trigger Governance runs automated outbound messages on a governed scheduler with delivery tracking, avoiding uncontrolled or duplicate outreach"]},{name:"Security & Multi-Tenancy",highImpact:["Full Workspace Isolation — every organization's agents, conversations, rules and files are isolated at the data layer; no cross-tenant visibility is possible by design"],standard:["Active Directory Integration automates AD operations (account unlock, password reset) driven by action-based business rules, with a full audit trail","Voice & Video Multimodal Support extends agents beyond text with speech-to-text, text-to-speech and computer-vision-based analysis","Secure Agent API Keys provide encrypted key issuance with per-key usage tracking for safe programmatic access"]},{name:"Observability & Enterprise Reliability",standard:["OpenTelemetry Instrumentation captures every agent execution, peer-to-peer call and message-processing step as a structured trace span","Live Observability Dashboard shows conversation volume, latency, token usage and cost, plus a color-coded per-agent performance table with a live span feed","First-Response-Time Tracking captures time-to-first-response per conversation automatically as a core SLA metric","External Tracing Integration offers one-flag integration with third-party tracing platforms such as LangSmith"]}]},{icon:nt,title:"Automation, Reporting & Extensibility",tagline:"Agents don't just chat — they drive multi-step business processes, close the knowledge loop, and connect to the rest of the stack.",modules:[{name:"Workflow Automation & Task Scheduling",standard:["Visual Workflow Canvas — an n8n-style, drag-and-drop builder with natural-language-driven execution for multi-step processes","Task Automation & Schedules handle recurring or batch work via event-driven triggers and a cron-based scheduler","Workflow Playground offers a safe environment to test workflow logic against sample data before publishing","Execution History gives a complete, searchable log of every agent and workflow execution for debugging and review"]},{name:"Proactive Intelligence & Knowledge Management",highImpact:["Knowledge Base — centralized, agent-attachable knowledge sources spanning documents, spreadsheets, images and structured data"],standard:["Automated Weekly Briefing delivers a fixed-schedule Monday summary so leadership always starts the week informed","Proactive Messaging reaches customers based on defined conditions rather than waiting for them to contact support","Staffing Recommendations forecast needs on demand using historical patterns plus external event data","Knowledge Gaps surface the questions the knowledge base is failing to answer, ranked by frequency and impact","Auto-Drafted KB Articles draft a knowledge-base article automatically once a gap is resolved through a real conversation"]},{name:"Simulation, Reporting & Integration Ecosystem",highImpact:["MCP Server Management connects any Model Context Protocol server — GitHub, Atlassian, ServiceNow or custom — with tools instantly callable by agents",'LLM Cost Tracking & Attribution turns "AI is a black-box cost" into a per-model, per-agent, per-conversation line-item budget'],standard:["Conversation Replay steps through any historical conversation to understand exactly why an agent responded the way it did","Routing Rule Simulation runs a candidate rule set against test conversations with the full ordered evaluation trail","Scheduled Reports and Shareable Report Links deliver PDF/CSV reports on a recurring schedule and let stakeholders view them without a platform login","Platform Integrations Hub, API Key Management and a standards-compl

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57icketing system, and a spreadsheet of dependencies to triage an incident."}),e.jsx("p",{children:"Every resource type — from a bare-metal server to a Snowflake warehouse to a WebSphere cluster — is represented in the same inventory, monitored through the same alerting engine, and connected through the same dependency graph. Sitting on top of that deterministic foundation is an AI-assisted intelligence layer that applies large language models to the operational questions that traditionally required a senior engineer's judgment: what is the root cause, when will this resource run out of headroom, what happens if this dependency fails, and where is money being wasted."})]}),e.jsxs(v.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{duration:.5,delay:.1},className:"lg:col-span-2 border border-primary/30 bg-gradient-to-br from-primary/10 via-primary/5 to-transparent rounded-2xl p-8",children:[e.jsx("div",{className:"text-xs font-semibold tracking-widest uppercase text-primary/70 mb-4 text-center",children:"Scope Note"}),e.jsx("p",{className:"text-sm text-muted-foreground leading-relaxed text-center",children:"This platform covers core observability, monitoring, and AI-assisted operations capabilities. AI-driven log analysis and log-based AIOps are a dedicated subsystem, documented separately."})]})]})})}),e.jsx("section",{className:"py-20 bg-muted/20 border-y border-border/40",children:e.jsxs("div",{className:"container mx-auto px-4 md:px-6",children:[e.jsxs(v.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{duration:.5},className:"max-w-2xl mb-12",children:[e.jsxs("div",{className:"inline-flex items-center gap-2 border border-primary/30 bg-primary/10 text-primary text-xs font-semibold tracking-widest uppercase px-3 py-1.5 rounded-full mb-4",children:[e.jsx(Fs,{className:"h-3 w-3"})," Eight Capability Areas"]}),e.jsx("h2",{className:"text-3xl md:text-4xl font-bold mb-4",children:"From Everyday Visibility to AI-Assisted Intelligence"}),e.jsx("p",{className:"text-muted-foreground",children:"Moving from the day-to-day visibility surfaces operators use first, through specialized monitoring domains, into alerting and incident response, and finally into the AI-assisted intelligence layer."})]}),e.jsx(v.div,{variants:kf,initial:"hidden",whileInView:"visible",viewport:{once:!0},className:"grid md:grid-cols-2 gap-5",children:pG.map(a=>e.jsxs(v.div,{variants:Sf,className:"border border-border/60 bg-card rounded-xl p-6 hover:border-primary/30 hover:shadow-md transition-all duration-300",children:[e.jsxs("div",{className:"flex items-start justify-between mb-4",children:[e.jsx("div",{className:"inline-flex items-center justify-center w-11 h-11 rounded-xl bg-primary/10 border border-primary/20 text-primary",children:e.jsx(a.icon,{className:"h-5 w-5"})}),e.jsxs("span",{className:"text-xs font-bold tracking-widest uppercase text-muted-foreground/60",children:["Ch. ",a.

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w-4"})]})]})]})})})]})}const yG=[{icon:Ue,chapter:"01",title:"Universal Log Ingestion & Normalization",desc:"A single ingestion layer that speaks every log format your estate produces, normalized into one query-able schema before it reaches search or AI analysis.",bullets:["Native collectors for Syslog, Windows Event Log, journald, and container stdout/stderr","Cloud-native log sources — AWS CloudWatch Logs, Azure Monitor Logs, GCP Cloud Logging","Kubernetes pod, node, and control-plane log aggregation via DaemonSet collectors","Schema-on-write normalization across JSON, key-value, CSV, and unstructured free text"]},{icon:RN,chapter:"02",title:"Multi-Source Event Ingestion",desc:"Every alert your monitoring and ITSM stack produces — metrics, APM, network, synthetic checks, and tickets — lands in the same event stream as your logs, ready for AI correlation.",bullets:["Native connectors for APM & metrics tools — Prometheus/Alertmanager, Datadog, New Relic, Dynatrace","Cloud-native 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deduplicated, topology-aware, prioritized incidents.",bullets:["Real-time event deduplication and grouping across every connected monitoring tool","Topology- and dependency-aware correlation — alerts on related services merge into one incident","Alert-storm and flap detection with dynamic suppression during known incidents","Severity re-scoring that blends event confidence with business-service impact"]},{icon:Uu,chapter:"05",title:"Root Cause Correlation Engine",desc:"Correlates anomalous log patterns and deduplicated events across services, hosts, and time windows into a single causal narrative — not a wall of unrelated alerts.",bullets:["Cross-service correlation of logs, events, and alerts aligned to distributed trace and span IDs","Temporal correlation linking deploys, config changes, log spikes, and event bursts","Causal chain visualization from symptom back to originating event","Confidence-scored root cause candidates ranked by evidence strength across both signal types"]},{icon:Zg,chapter:"06",title:"Predictive AIOps & Auto-Remediation",desc:"Moves from explaining what already happened to forecasting what is about to happen, and — within approved guardrails — fixing it automatically.",bullets:["Predictive failure scoring from leading log-pattern and event-frequency indicators","AI-generated remediation playbooks mapped to recurring event and log signatures","Approval-gated auto-remediation with full execution and rollback history","Closed-loop learning — outcomes of past remediations refine future recommendations"]},{icon:Be,chapter:"07",title:"Governance, Retention & Compliance",desc:"Enterprise-grade controls for how long logs and events live, who can see them, and how the platform holds up under audit.",bullets:["Configurable hot / warm / cold retention tiers with automated lifecycle policies","Field-level PII redaction and masking at ingestion time","Framework-mapped audit trails — SOC 2, HIPAA, PCI-DSS, GDPR, and ISO 27001","Role-based 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57cture — Syslog, journald, Windows Event Log, and SNMP traps","Cloud-native — AWS CloudWatch Logs, Azure Monitor Logs, GCP Cloud Logging","Containers & orchestration — Kubernetes pod, node, and control-plane logs via DaemonSet collectors","Application — structured JSON, Log4j/Logback, Python logging, .NET diagnostics, and custom agents"]},{heading:"Ingestion Pipeline",items:["Schema-on-write parsing across JSON, key-value, CSV, and free-text formats","Configurable pattern library for legacy and proprietary log formats","Buffered, back-pressure-aware collectors that survive downstream outages","Real-time and batch ingestion modes, selectable per source"]},{heading:"Enrichment at Ingest",items:["Automatic host, service, and environment tagging from CMDB and cloud metadata","Trace and span ID extraction for cross-referencing with distributed tracing","GeoIP, user-agent, and severity-level enrichment for common log families"]}]},{icon:RN,title:"Multi-Source Event Ingestion",tagline:"One event bus for every alert-producing tool in the enterprise — metrics, APM, network, synthetic checks, and ITSM tickets — normalized before correlation ever runs.",groups:[{heading:"Monitoring & APM Sources",items:["Prometheus/Alertmanager, Datadog, New Relic, and Dynatrace alert webhooks","Cloud-provider native alerts — AWS CloudWatch Alarms, Azure Monitor Alerts, GCP Alerting","XpediOo AI Observability alerts ingested natively, with full topology context attached"]},{heading:"Network & Synthetic Sources",items:["SNMP traps and NetFlow-based anomaly alerts","Firewall, IDS/IPS, and VPN security event feeds","Synthetic transaction and uptime-check failures from external probes"]},{heading:"ITSM & Ticketing Sync",items:["Bi-directional integration with ServiceNow and Jira Service Management","PagerDuty and Opsgenie incident and on-call event ingestion","Generic webhook connector for any tool that can post a JSON payload"]}]},{icon:be,title:"AI-Powered Log Analysis & Pattern 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log attributes"]}]},{icon:Dl,title:"AIOps Event Correlation & Noise Reduction",tagline:"Machine learning turns a flood of alerts from a dozen disconnected tools into a small number of deduplicated, prioritized, and correctly-routed incidents.",groups:[{heading:"Deduplication & Grouping",items:["Real-time event deduplication across every connected monitoring and ITSM tool","Topology- and dependency-aware grouping — alerts on related services merge into one incident","Fingerprint-based matching for near-duplicate alerts with differing wording or IDs"]},{heading:"Storm & Flap Control",items:["Alert-storm detection with automatic suppression once a root incident is identified","Flap detection to prevent unstable signals from re-triggering the same alert repeatedly","Dynamic suppression during acknowledged incidents and scheduled maintenance windows"]},{heading:"Prioritization & Routing",items:["Severity re-scoring blending event confidence with business-service impact","Multi-channel delivery — Slack, Microsoft Teams, PagerDuty, ServiceNow, email, and SMS","On-call schedule and escalation-policy integration"]},{heading:"Noise Analytics",items:["Noise-reduction dashboard tracking raw-alert-to-incident compression ratio over time","Mean-time-to-acknowledge and mean-time-to-resolve trending by service","Alert-rule tuning recommendations for chronically noisy sources"]}]},{icon:Uu,title:"Root Cause Correlation Engine",tagline:"Links anomalous log signals and deduplicated events across services, hosts, deploys, and time into a single explainable narrative.",groups:[{heading:"Correlation Signals",items:["Distributed trace and span ID alignment across service boundaries","Deploy and configuration-change timeline overlay on log anomaly and event spikes","Topology-aware correlation using the live dependency graph from AI Observability, spanning both logs and events"]},{heading:"Causal Analysis",items:["Causal chain visualization from downstream symptom back to originating event","Confidence-scored root cause candidates ranked by supporting evidence","Multi-incident clustering when a single root cause produces several symptoms"]},{heading:"Investigation Workspace",items:["One-click drill-down from a correlated incident to raw underlying log lines and source events","AI-generated incident summary with a plain-language explanation of the chain","Exportable incident timelines for postmortems and audits"]}]},{icon:Zg,title:"Predictive AIOps & Auto-Remediation",tagline:"Moves from explaining incidents after the fact to forecasting them ahead of time, and resolving known patterns automatically under approval-gated guardrails.",groups:[{heading:"Predictive Signals",items:["Failure-likelihood scoring from leading log-pattern and event-frequency indicators","Capacity and saturation forecasting derived from log-volume and event-rate trends","Early-warning alerts before a known failure signature fully manifests"]},{heading:"Remediation Playbooks",items:["AI-generated remediation runbooks mapped to recurring incident signatures","Library of pre-built playbooks for c

57ommon failure classes — disk, memory, connection-pool exhaustion","Custom playbook authoring with parameterized remediation steps"]},{heading:"Governed Automation",items:["Approval-gated execution with a mandatory human sign-off step by default","Full execution and rollback history for every automated action","Closed-loop learning — remediation outcomes feed back into future recommendations"]}]},{icon:Be,title:"Governance, Retention & Compliance",tagline:"Enterprise controls for how long logs and events live, who can see them, and how the platform holds up under a regulatory audit.",groups:[{heading:"Retention & Lifecycle",items:["Configurable hot / warm / cold retention tiers with automated tiering policies","Per-source retention overrides for regulatory or contractual requirements","Immutable archive export to long-term object storage"]},{heading:"Privacy & Access Control",items:["Field-level PII detection and redaction at ingestion time","Role-based access control scoped to log source, event source, service, and environment","Per-tenant log-and-event isolation for managed-service-provider and multi-business-unit deployments"]},{heading:"Audit & Compliance",items:["Framework-mapped audit trails — SOC 2, HIPAA, PCI-DSS, GDPR, and ISO 27001","Immutable access and query logs for every user action on sensitive log and event data","Scheduled compliance reports exportable for auditor review"]}]}],vG=[["Infrastructure & OS","Syslog, journald, Windows Event Log, and SNMP traps"],["Cloud-Native Logs","AWS CloudWatch Logs, Azure Monitor Logs, and GCP Cloud Logging"],["Containers & Orchestration","Kubernetes pod, node, and control-plane logs via DaemonSet collectors"],["Application Frameworks","Log4j/Logback, Python logging, .NET diagnostics, Node.js, and Go structured logging"],["Web & Network","NGINX, Apache, HAProxy, load balancer, and firewall logs"],["Databases","PostgreSQL, MySQL, MS SQL, Oracle, and MongoDB slow-query and error logs"],["Security","IDS/IPS, endpoint detection, VPN, and identity-provider audit logs"],["Message Queues & Streaming","Kafka, RabbitMQ, and other broker operational logs"]],wG=[["Monitoring & APM","Prometheus/Alertmanager, Datadog, New Relic, and Dynatrace alert webhooks"],["Cloud-Native Alerting","AWS CloudWatch Alarms, Azure Monitor Alerts, and GCP Alerting"],["Network & Security Events","SNMP traps, NetFlow anomalies, firewall/IDS/IPS, and VPN events"],["Synthetic & Uptime Checks","External probe and synthetic-transaction check failures"],["ITSM & Incident Tools","ServiceNow, Jira Service Management, PagerDuty, and Opsgenie"],["Internal Observability","Native ingestion of XpediOo AI Observability alerts, with full topology context"]],jG=[["Log Pattern Mining","What are the handful of recurring patterns hiding inside millions of log lines?"],["Anomaly Detection","Which log pattern just deviated from its historical baseline?"],["Event Correlation & Deduplication","Which of these alerts from five different monitoring tools describe the same underlying incident?"],["Alert-Storm Suppression","How do we stop ten thousand alerts from one root cause from flooding every on-call channel?"],["Root Cause Correlation","Which upstream event actually caused this cascade of symptoms?"],["Predictive Failure Scoring","How likely is this signature to escalate into an outage?"],["Auto-Remediation","Can this recurring failure be resolved automatically, within approved guardrails?"],["Natural-Language Search","What happened, described in plain English instead of a query language?"]],NG=[{icon:Ue,title:"Every Log And Event, One Pipeline",desc:"Infrastructure, cloud, container, application, network, and security logs — plus every alert fired by APM, network, synthetic, and ITSM tools — are ingested, normalized, and correlated in a single schema. No per-tool silos, no per-source noise-reduction logic."},{icon:Dl,title:"Noise Reduction Is the Core, Not a Feature",desc:"AIOps event correlation runs continuously in the background, collapsing alert storms from a dozen disconnected tools into a handful of deduplicated, topology-aware incidents before a human ever sees them."},{icon:Zg,title:"Automation With Guardrails",desc:"Auto-remediation runs inside approval gates with full rollback history, so automated response earns operator trust instead of demanding it upfront."},{icon:Uu,title:"Built to Work With AI Observability",desc:"Topology and dependency data — and native alert ingestion — from XpediOo AI Observability feed directly into event correlation, so root-cause analysis reasons over the same map your infrastru

57cture team already trusts."}],Ce="/aiops-screenshots",AG=[{title:"Anomaly Patterns",page:"AI Log Analysis",image:`${Ce}/foundational-log-anomaly-patterns.jpg`,desc:"The default, foundational view of AI Log Analysis: AI-detected anomaly patterns across every ingested log source, with severity, trend, and confidence for each one. Every high- and medium-impact Log Analysis capability in this document builds on the anomaly stream produced here."},{title:"Correlations",page:"AI Event Correlation (AIOps)",image:`${Ce}/foundational-event-correlations.jpg`,desc:"The default, foundational view of Event Correlation: multi-signal grouping across anomalies, Kubernetes events, remediations, deployments, and external alerts into a single correlated incident view. Every high- and medium-impact AIOps capability in this document builds on the correlation groups produced here."}],kG=[{title:"Cross-Service Causality Analysis",image:`${Ce}/log-high-cross-service-causality.jpg`,desc:"Uses AI to trace how a failure in one service propagates into failures in dependent services, reconstructing the full chain from root cause to symptoms.",impact:"Collapses hours of manual cross-team log correlation into an automatically generated causal chain, dramatically shortening root-cause analysis during multi-service incidents."},{title:"AI Query (Conversational Log Search)",image:`${Ce}/log-high-ai-query.jpg`,desc:"A conversational interface for querying log data in plain English, with the AI translating questions into the underlying filters and returning direct answers grounded in the matching log entries.",impact:"Makes log data accessible to engineers who aren't fluent in query syntax and speeds up ad-hoc investigation for everyone else."},{title:"False-Positive Learning",image:`${Ce}/log-high-false-positive-learning.jpg`,desc:"Learns from dismissal feedback which alert patterns are consistently false positives and automatically suppresses future recurrences of them.",impact:"Directly reduces alert fatigue — the single biggest driver of ignored or delayed responses to real incidents — by keeping the noise-to-signal ratio in check over time."},{title:"Semantic Anomaly Deduplication (Smart Dedup)",image:`${Ce}/log-high-semantic-dedup.jpg`,desc:"Uses semantic similarity — not just exact string matching — to cluster anomalies that are really the same underlying root cause reported multiple times, surfacing one canonical entry per cluster.",impact:"Cuts through duplicate noise in incident lists and reporting, so headline metrics reflect the true number of distinct problems rather than an inflated raw event count."},{title:"AI Shift Handoff & Digest Report",image:`${Ce}/log-high-shift-handoff.jpg`,desc:"Generates an automatic AI-written summary of anomalies, resolutions, and outstanding risks over a chosen period — 8-hour shift, day, or week.",impact:"Standardizes shift handoffs and management reporting, ensuring nothing falls through the cracks when responsibility changes hands, and gives leadership a digestible operational pulse."},{title:"Predictive Anomaly Forecasting",image:`${Ce}/log-high-predictive-forecasting.jpg`,desc:"AI analyzes the historical cadence of recurring issues and predicts when the same pattern is statistically likely to reoccur, with a confidence score and a 24-hour forecast window.",impact:"Shifts operations from purely reactive firefighting to proactive prevention, letting teams schedule fixes or capacity changes ahead of a predicted recurrence."},{title:"Log Baseline & Drift Detection",image:`${Ce}/log-high-baseline-drift.jpg`,desc:"Establishes a statistical baseline of normal log behavior per source and continuously checks for gradual deviation in error rate, warning rate, or volume.",impact:"Catches slow-building degradations — a memory leak, a creeping error rate — that single-anomaly detection would miss because no individual event looks alarming on its own."},{title:"Intelligent Alert Noise Correlation",image:`${Ce}/log-high-alert-noise-correlation.jpg`,desc:"Groups alert firings that stem from the same underlying incident into a single correlated notification instead of firing one alert per rule per event.",impact:"Prevents an on-call engineer's phone from lighting up with twenty pages for one incident, reducing response fatigue and making the true blast radius obvious immediately."},{title:"SLA Tracking",image:`${Ce}/log-high-sla-tracking.jpg`,desc:"Tracks the time from anomaly detection to resolution against configured response-time targets per severity, flagging breaches as they approach or occur.",impact:"Gives engineering leadership objective, real-time visibility into whether operational commitments — internal or contractual — are being met, before a customer has to ask."},{title:"PII Masking & Data Redaction",image:`${Ce}/log-high-pii-masking.jpg`,desc:"Automatically detects and masks sensitive personal data — emails, card numbers, SSNs, tokens — in log content before it is stored, displayed, or sent to an LLM.",impact:"Reduces regulatory exposure (GDPR, PCI-DSS, HIPAA) by ensuring sensitive data never lands unmasked in a system engineers routinely have broad access to."}],SG=[{title:"AI Insights (Pattern Intelligence)",image:`${Ce}/log-med-ai-insights.jpg`,desc:"Every detected anomaly gets an AI write-up explaining what happened, why it's risky, and what's likely to break next — no need to parse raw stack traces or correlate logs by hand.",impact:"On-call engineers and less-technical stakeholders (support, leadership) reach a shared understanding of incident severity in seconds, speeding up triage and cross-team communication."},{title:"Live Stream",image:`${Ce}/log-med-live-stream.jpg`,desc:"A live, continuously updating feed of log entries as they are emitted by your infrastru

57cture, filterable by source and severity level in real time.",impact:"Teams can watch a deployment or incident fix take effect live instead of waiting on periodic refreshes, shortening validation time after a change."},{title:"Log Search",image:`${Ce}/log-med-log-search.jpg`,desc:"Full-text and structured search across the entire retained log history, with filters for time range, source, severity, and tags, plus an AI-assisted search mode.",impact:"Root-cause investigations that used to mean grepping through server files now take seconds, materially reducing incident resolution time and audit turnaround."},{title:"Log Sources",image:`${Ce}/log-med-log-sources.jpg`,desc:"A managed inventory of every system, service, and environment feeding logs into the platform, with health status for each connection.",impact:"Gaps in log coverage — a silent agent, a misconfigured shipper — are visible immediately, preventing blind spots that would otherwise hide real incidents."},{title:"Schedules",image:`${Ce}/log-med-schedules.jpg`,desc:"Configures anomaly scans to run automatically on a recurring cadence — hourly, daily, or custom — instead of requiring someone to trigger them manually.",impact:"Detection coverage no longer depends on an engineer remembering to run a scan, which is critical for 24/7 operations and off-hours incident coverage."},{title:"Alert Rules",image:`${Ce}/log-med-alert-rules.jpg`,desc:"Defines precise trigger conditions — thresholds, patterns, or combinations — that fire an alert the moment matching log activity occurs.",impact:"Turns passive log storage into an active early-warning system, so responders are notified proactively instead of discovering problems reactively."},{title:"Volume & Trends",image:`${Ce}/log-med-volume-trends.jpg`,desc:"Time-series visualization of log throughput and severity mix across sources, surfacing volume spikes, drop-offs, and long-term trend shifts.",impact:"Capacity planning and anomaly detection both benefit — sudden volume changes are often the first visible sign of an outage, security event, or silent failure."},{title:"Run History",image:`${Ce}/log-med-run-history.jpg`,desc:"A full audit trail of every anomaly-detection run — when it executed, what configuration was used, and what it found, with run-to-run comparison.",impact:"Gives compliance and engineering leadership a verifiable record that monitoring is actually happening consistently, and lets teams compare results run-over-run."},{title:"Field Analytics",image:`${Ce}/log-med-field-analytics.jpg`,desc:"Extracts structured fields — IP addresses, endpoints, response times, user IDs — out of unstructured log text using built-in or AI-suggested patterns, then aggregates them like a spreadsheet.",impact:'Turns free-text logs into queryable business and operational metrics — e.g. "top IPs causing 500 errors" — without needing a data engineering project.'},{title:"Retention",image:`${Ce}/log-med-retention.jpg`,desc:"Defines automated policies for how long log data of each type or source is retained before deletion, enforced without manual cleanup.",impact:"Balances storage cost against compliance requirements (e.g. HIPAA, SOC 2 retention windows) automatically, removing a common source of audit findings and storage overruns."},{title:"Export & Audit",image:`${Ce}/log-med-export-audit.jpg`,desc:"Exports raw or filtered logs for external analysis, and reviews a compliance audit log of every retention and export action taken on the data.",impact:"Satisfies auditor and regulator requests for evidence of data handling controls, and supports e-discovery or forensic investigations without engineering involvement."}],IG=[{title:"Cross-Service Causality Analysis",image:`${Ce}/event-high-cross-service-causality.jpg`,desc:"Signals from anomalies, Kubernetes events, remediations, deployments, and external alerts within a 30-minute window across two or more resources are sent to the LLM for causal analysis, which identifies the root cause, how it propagated, and the downstream symptoms.",impact:"Collapses hours of manual, cross-team correlation work into an automatically generated causal chain — chains below 60% confidence are discarded, and repeat triggers are suppressed for an hour to avoid noise."},{title:"AI Query (Conversational Event Search)",image:`${Ce}/event-high-ai-query.jpg`,desc:"A conversational interface for asking plain-English questions about correlated events, grounded in the full real-time signal feed plus every AI-correlated group.",impact:'Gives on-call engineers immediate answers to the questions that come up in almost every incident review — "why did payment-service start alerting?" — with no query to write.'},{title:"False-Positive Learning",image:`${Ce}/event-high-false-positive-learning.jpg`,desc:'Every dismissal of a correlated group is a signal. Root-cause patterns dismissed five or more times with a high fal

57se-positive rate are automatically suppressed, so future groups are created as "Suppressed" instead of noisy "Active" alerts.',impact:"Keeps the correlation engine self-tuning — the patterns that page on-call for nothing get quieter over time without anyone editing a rule."},{title:"Semantic Correlation Deduplication (Smart Dedup)",image:`${Ce}/event-high-semantic-dedup.jpg`,desc:'Clusters alert-correlation groups by semantic similarity of their root cause — turning "DB latency spike on db-server-01" and "db-server-01 experiencing high query latency" from different analysis runs into one canonical recurring root cause with a merged signal count.',impact:"Headline noise-reduction metrics reflect the true number of distinct operational problems, not an inflated count of re-detected duplicates across runs."},{title:"AI Shift Handoff & Digest Report",image:`${Ce}/event-high-shift-handoff.jpg`,desc:"Generates a plain-English operational briefing covering stability, correlated-group trends, resource health, and recommended actions — ready for shift handoffs, executive reviews, or SOC 2 operational audits.",impact:"Gives incoming shifts and leadership a consistent, digestible operational summary without anyone having to manually assemble one from raw alert history."},{title:"Correlation Settings (Weighted Scoring)",image:`${Ce}/event-high-correlation-settings.jpg`,desc:"Tunable composite scoring across three strategies — Topology (service dependency graph), Timing (co-incidence window), and Wording (message similarity) — that determines how aggressively signals are grouped together.",impact:"Lets operators tune correlation behavior to their environment: raise Topology weight where the dependency map is well-maintained, or raise Wording weight in noisy, repetitive-alert environments."},{title:"Event Correlation Baseline & Drift Detection",image:`${Ce}/event-high-baseline-drift.jpg`,desc:"Continuously learns normal signal behavior per source type per hour-of-day and alerts when volume or critical-signal rate deviates significantly from that learned baseline.",impact:"Surfaces slow-building shifts in alert behavior — a source quietly getting noisier or a critical-rate creep — that a single correlation run would never flag on its own."},{title:"PII Masking on Event Correlation",image:`${Ce}/event-high-pii-masking.jpg`,desc:"Applies the same shared masking rule set used by Log Analysis to every signal message before it reaches the correlation AI, with a live tester for event-style content.",impact:"Guarantees consistent data-handling policy across both AIOps surfaces — one set of redaction rules, enforced everywhere signals reach an LLM, with no separate configuration to maintain."},{title:"SLA Tracking",image:`${Ce}/event-high-sla-tracking.jpg`,desc:"Tracks acknowledgement and resolution windows for correlated event groups by severity, with an automatic breach checker and escalation tracking.",impact:"Gives leadership the same objective, real-time SLA visibility for cross-system incidents that Log Analysis provides for individual anomalies."},{title:"Event Sources",image:`${Ce}/event-high-event-sources.jpg`,desc:"A live registry of every signal type feeding the correlation engine — anomalies, Kubernetes events, remediation actions, change/deployment events, and external webhook or polled integrations — each with a 7-day event count and health indicator.",impact:"Gives operators one place to confirm every upstream signal pipeline is actually delivering before they trust the correlation results built on top of it."}],CG=[{title:"Anomaly Patterns (Recurring Signal Shapes)",image:`${Ce}/event-med-anomaly-patterns.jpg`,desc:"Surfaces recurring signal shapes across the last 7 days — the same Kubernetes warning, anomaly type, or change repeatedly hitting the same resource — not just a single run's grouping.",impact:"Highlights the chronic, slow-burning issues that a one-off correlation run would miss because no individual occurrence looks urgent."},{title:"AI Insights",image:`${Ce}/event-med-ai-insights.jpg`,desc:"An AI-generated pattern intelligence summary drawn from recurring signal patterns across every correlation source, each with a severity tag and a plain-English explanation.",impact:"Gives responders a fast, prioritized reading list of what actually matters across the noise, without opening every individual correlation group."},{title:"Live Stream",image:`${Ce}/event-med-live-stream.jpg`,desc:"A live, continuously updating feed of every signal type — anomalies, Kubernetes events, remediations, changes, external alerts, app-server alerts — filterable by source as they occur.",impact:"Lets an operator watch a live incident or a change rollout unfold 

57signal-by-signal in real time, across every source type at once."},{title:"Event Search",image:`${Ce}/event-med-event-search.jpg`,desc:"Full-text and structured search across every correlated signal, with filters for source type and severity and a breakdown of matches by category.",impact:'Turns ad-hoc "what happened with this resource last week" questions into a few-second search instead of a manual sweep across multiple source pages.'},{title:"Alert Rules",image:`${Ce}/event-med-alert-rules.jpg`,desc:"Rules evaluated against correlated event groups every time analysis runs, matching on correlation type, severity, resource, or signal source rather than raw log text.",impact:"Lets teams route notifications for specific correlation shapes — e.g. any critical-severity group touching payment-api — without waiting on a human to notice it."},{title:"Volume & Trends",image:`${Ce}/event-med-volume-trends.jpg`,desc:"Real event throughput over time across all signal sources, with hourly bucketing and breakdowns by severity.",impact:"Gives capacity and on-call planning teams the same trend visibility over correlated events that Log Analysis provides over raw log volume."},{title:"Run History",image:`${Ce}/event-med-run-history.jpg`,desc:'Every "Correlate Alerts" analysis run over the last 30 days, with signal counts, new groups, flapping, suppression, and noise-reduction percentage — plus the ability to diff two runs.',impact:"Gives engineering leadership a verifiable, comparable record that correlation is running consistently and actually reducing noise run over run."},{title:"Field Analytics",image:`${Ce}/event-med-field-analytics.jpg`,desc:"Ready-made, one-click analyses over real correlated signals — noisiest resources, critical-only hotspots, top signal source types, most frequent event types, Kubernetes warning hotspots, and external alert hotspots.",impact:"Gives on-call engineers immediate answers to the questions that come up in almost every incident review, with no query to write."},{title:"Maintenance Windows",image:`${Ce}/event-med-maintenance-windows.jpg`,desc:"Signals whose resource and severity fall inside an active, scheduled maintenance window are suppressed before correlation runs.",impact:"Known-safe changes — a scheduled database maintenance, a planned patch window — don't spam the team with new alert groups."},{title:"Noise Trends",image:`${Ce}/event-med-noise-trends.jpg`,desc:'Tracks noise-reduction percentage and mean time to acknowledge/resolve (MTTA/MTTR) for correlated groups over time, across every historical "Correlate Alerts" run rather than just the latest snapshot.',impact:"Lets leadership see whether the correlation engine's effectiveness is actually improving over time, not just what it looks like today."},{title:"Retention",image:`${Ce}/event-med-retention.jpg`,desc:"Automated purge policies for correlated event groups past a configured compliance retention window, with a full retention audit trail.",impact:"Extends the same storage-cost and compliance balancing that Log Analysis retention provides to the correlation domain's own data."},{title:"Export & Audit",image:`${Ce}/event-med-export-audit.jpg`,desc:"Exports filtered correlated event groups as CSV, JSON, or a formatted PDF report, and maintains a compliance audit trail of every correlation run and export with PII-masking counts.",impact:"Gives auditors and compliance teams the same SOC2/GDPR-ready evidence trail for cross-system incident correlation that exists for raw log handling."},{title:"Topology Aliases",image:`${Ce}/event-med-topology-aliases.jpg`,desc:"Maps a signal's resource identifier to its dependency-map application name when automatic name normalization isn't enough — for example, a Kubernetes pod name versus a dependency-map service name that differ structurally.",impact:"Keeps topology-weighted correlation accurate even when naming conventions differ across the systems feeding signals into the engine, instead of silently under-correlating them."}],MG=[{icon:ra,title:"Faster Resolution",desc:"Causality chains, conversational AI query, and semantic deduplication collapse hours of manual cross-team correlation into seconds, directly cutting mean-time-to-resolution on multi-service incidents."},{icon:PM,title:"Less Noise, Less Fatigue",desc:"False-positive learning, alert-noise correlation, and drift detection mean the system gets quieter and more accurate over time instead of paging the same person for the same non-issue every week."},{icon:Be,title:"Audit-Ready by Default",desc:"PII masking, retention policies, SLA tracking, and compliance export are built into both engines natively — not bolted on — so security and compliance reviews are a formality rather than a project."}],TG=[{icon:wE,title:"For Operators",desc:"Fewer pages for the same incident, faster root-cause attribution, and a conversational way to ask questions instead of writing queries."},{icon:Ug,title:"For Engineering 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57p-10 items-start",children:[e.jsxs(v.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{duration:.5},className:"lg:col-span-3 space-y-5 text-muted-foreground leading-relaxed",children:[e.jsx("h2",{className:"text-3xl md:text-4xl font-bold text-foreground mb-2",children:"From Alert Storms and Log Noise to One Root Cause"}),e.jsx("p",{children:"Enterprise environments don't just bury teams in log lines — they bury them in alerts. A single upstream failure can trigger hundreds of near-simultaneous notifications across APM tools, network monitors, synthetic checks, and ticketing systems, each one looking like its own incident. On-call engineers routinely spend the most critical minutes of an outage untangling which alerts are symptoms and which one is the cause, before log analysis has even started."}),e.jsx("p",{children:"XpediOo AIOps & AI Log Management solves both problems in one platform. Every log is ingested and normalized, and machine learning mines it for the handful of patterns that matter. In parallel, every event fired by connected monitoring, network, and ITSM tools is deduplicated, grouped by topology, and correlated in real time — this is the AIOps core, purpose-built to turn a flood of alerts into a small number of actionable incidents. Both streams feed the same root-cause engine, so the platform explains a single incident with the full picture: the alerts that fired, how they relate, and the log lines that explain why — with predictive models and approval-gated automation ready to resolve the recurring ones on their own."})]}),e.jsxs(v.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{duration:.5,delay:.1},className:"lg:col-span-2 border border-primary/30 bg-gradient-to-br from-primary/10 via-primary/5 to-transparent rounded-2xl p-8",children:[e.jsx("div",{className:"text-xs font-semibold tracking-widest uppercase text-primary/70 mb-4 text-center",children:"Scope Note"}),e.jsx("p",{className:"text-sm text-muted-foreground leading-relaxed text-center",children:"This platform is the dedicated AIOps and log-intelligence layer that complements XpediOo AI Observability — natively ingesting its alerts (plus events from any other connected monitoring or ITSM tool), correlating them against log data and live topology, and focusing purely on noise reduction, root cause, and automated remediation."})]})]})})}),e.jsx("section",{className:"py-20 bg-muted/20 border-y border-border/40",children:e.jsxs("div",{className:"container mx-auto px-4 md:px-6",children:[e.jsxs(v.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{duration:.5},className:"max-w-2xl mb-12",children:[e.jsxs("div",{className:"inline-flex items-center gap-2 border border-primary/30 bg-primary/10 text-primary text-xs font-semibold tracking-widest uppercase px-3 py-1.5 rounded-full mb-4",children:[e.jsx(Fs,{className:"h-3 w-3"})," Seven Capability Areas"]}),e.jsx("h2",{className:"text-3xl md:text-4xl font-bold mb-4",children:"From Raw Logs and Alert Storms to Automated Resolution"}),e.jsx("p",{className:"text-muted-foreground",children:"Moving from universal log and event ingestion, through AI-driven pattern detection and AIOps noise reduction, into root cause correlation, predictive automation, and the governance controls that keep it all audit-ready."})]}),e.jsx(v.div,{variants:Bi,initial:"hidden",whileInView:"visible",viewport:{once:!0},className:"grid md:grid-cols-2 gap-5",children:yG.map(a=>e.jsxs(v.div,{variants:Ps,className:"border border-border/60 bg-card rounded-xl p-6 hover:border-primary/30 hover:shadow-md transition-all duration-300",children:[e.jsxs("div",{className:"flex items-start justify-between mb-4",children:[e.jsx("div",{className:"inline-flex items-center justify-center w-11 h-11 rounded-xl bg-primary/10 border border-primary/20 text-primary",children:e.jsx(a.icon,{className:"h-5 w-5"})}),e.jsxs("span",{className:"text-xs font-bold tracking-widest uppercase text-muted-foreground/60",children:["Ch. ",a.

57chapter]})]}),e.jsx("h3",{className:"font-bold text-foreground text-lg mb-2",children:a.title}),e.jsx("p",{className:"text-muted-foreground text-sm leading-relaxed mb-4",children:a.desc}),e.jsx("div",{className:"space-y-2 pt-3 border-t border-border/40",children:a.bullets.map(r=>e.jsxs("div",{className:"flex items-start gap-2 text-sm text-muted-foreground",children:[e.jsx("span",{className:"h-1 w-1 rounded-full bg-primary mt-2 flex-shrink-0"}),e.jsx("span",{children:r})]},r))})]},a.chapter))})]})}),e.jsx("section",{className:"py-20",children:e.jsxs("div",{className:"container mx-auto px-4 md:px-6",children:[e.jsxs(v.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{duration:.5},className:"max-w-2xl mb-12",children:[e.jsxs("div",{className:"inline-flex items-center gap-2 border border-primary/30 bg-primary/10 text-primary text-xs font-semibold tracking-widest uppercase px-3 py-1.5 rounded-full mb-4",children:[e.jsx(Hl,{className:"h-3 w-3"})," Technical Deep-Dive"]}),e.jsx("h2",{className:"text-3xl md:text-4xl font-bold mb-4",children:"Ingestion, Noise Reduction & Remediation — In Depth"}),e.jsx("p",{className:"text-muted-foreground",children:"The seven areas below expand into full technical detail — every source, model, and workflow behind the platform, covering both AI-driven log analysis and the AIOps event-correlation core. Select a section to expand it."})]}),e.jsx(v.div,{variants:Bi,initial:"hidden",whileInView:"visible",viewport:{once:!0},className:"space-y-4",children:bG.map(a=>e.jsxs(v.details,{variants:Ps,className:"group border border-border/60 bg-card rounded-xl overflow-hidden [&_summary::-webkit-details-marker]:hidden",children:[e.jsxs("summary",{className:"cursor-pointer list-none flex items-center justify-between gap-4 p-6 hover:bg-muted/20 transition-colors",children:[e.jsxs("div",{className:"flex items-center gap-4",children:[e.jsx("div",{className:"inline-flex items-center justify-center w-11 h-11 rounded-xl bg-primary/10 border border-primary/20 text-primary flex-shrink-0",children:e.jsx(a.icon,{className:"h-5 w-5"})}),e.jsxs("div",{children:[e.jsx("h3",{className:"font-bold text-foreground text-lg",children:a.title}),e.jsx("p",{className:"text-muted-foreground text-sm leading-relaxed mt-1",children:a.tagline})]})]}),e.jsx(na,{className:"h-5 w-5 text-muted-foreground flex-shrink-0 transition-transform duration-300 group-open:rotate-180"})]}),e.jsx("div",{className:"px-6 pb-6 pt-2 border-t border-border/40 grid sm:grid-cols-2 lg:grid-cols-3 gap-6",children:a.groups.map(r=>e.jsxs("div",{children:[e.jsx("h4",{className:"text-sm font-bold text-foreground mb-2",children:r.heading}),e.jsx("ul",{className:"space-y-1.5",children:r.items.map(s=>e.jsxs("li",{className:"flex items-start gap-2 text-sm text-muted-foreground leading-relaxed",children:[e.jsx("span",{className:"h-1 w-1 rounded-full bg-primary mt-2 flex-shrink-0"}),e.jsx("span",{children:s})]},s))})]},r.heading))})]},a.title))})]})}),e.jsx("section",{className:"py-20 bg-muted/20 border-y border-border/40",children:e.jsxs("div",{className:"container mx-auto px-4 md:px-6",children:[e.jsxs(v.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{duration:.5},className:"max-w-3xl mb-14",children:[e.jsxs("div",{className:"inline-flex items-center gap-2 border border-primary/30 bg-primary/10 text-primary text-xs font-semibold tracking-widest uppercase px-3 py-1.5 rounded-full mb-4",children:[e.jsx(de,{className:"h-3 w-3"})," Complete Capability Reference"]}),e.jsx("h2",{className:"text-3xl md:text-4xl font-bold mb-4",children:"Two Engines, One AIOps Story"}),e.jsx("p",{className:"text-muted-foreground leading-relaxed",children:"AI Log Analysis and AI Event Correlation are XpediOo's two AI-native surfaces — one reasoning over raw log text, the other reasoning over the signals every other part of the platform produces. Together they form this platform's AIOps layer: the part of the system that doesn't just show data, but explains it, groups it, predicts it, and tells operators what to do next. Below is every high- and medium-impact capability on both surfaces — 44 in total — each with a live product screenshot, what it does, and the business outcome it drives."}),e.jsx("div",{className:"flex flex-wrap gap-4 mt-6",children:[{label:"44 Capabilities Documented",icon:Fs},{label:"20 High-Impact",icon:de},{label:"24 Medium-Impact",icon:Dl}].map(a=>e.jsxs("div",{className:"flex items-center gap-2 border border-border/60 bg-card px-4 py-2 rounded-full text-sm text-muted-foreground",children:[e.jsx(a.icon,{className:"h-3.5 w-3.5 text-primary"}),a.label]},a.label))})]}

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57ategory, and affected service without human intervention","Intelligent routing — incidents are matched to the right team or individual based on skills, current load, and historical resolution patterns","AI-powered diagnosis — the platform correlates the incident with CMDB data, recent changes, and similar past incidents to surface probable root cause immediately","Auto-escalation with SLA awareness — escalation rules fire automatically when breach thresholds approach, notifying the right stakeholders before the clock runs out","War-room summaries — for major incidents, AI generates live status briefs for executive comms without your engineers having to stop and write updates"]},{id:"service-request",num:"02",icon:Ll,title:"Service Request",badge:"Self-Service at Scale",desc:"Service requests are the highest-volume, lowest-complexity work your service desk handles — yet they consume a disproportionate share of agent time. XpediOo automates fulfilment end-to-end while giving end-users a consumer-grade self-service experience that reduces walk-ups and email chains to zero.",points:["Conversational request intake — users describe what they need in plain language; AI maps it to the right service and pre-populates the form","Automated approval workflows — multi-stage approvals are routed dynamically based on request type, cost threshold, and organisational hierarchy","Fulfilment automation — standard requests (software installs, access provisioning, hardware orders) are fulfilled automatically via integrations with your provisioning and IAM tools","Real-time status visibility — requesters see live progress updates without having to chase the service desk","Continuous deflection — the AI suggests self-service alternatives and knowledge articles before the request is submitted, reducing catalogue volume over time"]},{id:"catalogue",num:"03",icon:qM,title:"Service Catalogue",badge:"Single Source of Truth",desc:"A service catalogue is only as useful as it is current and navigable. XpediOo replaces the static spreadsheet-style catalogue with a living, AI-enriched library that employees can search in plain language and that administrators can maintain with minimal manual effort.",points:["AI-assisted catalogue curation — the platform identifies gaps, duplicates, and stale entries and suggests corrections automatically","Natural language search — users find services by describing what they need, not by knowing the exact catalogue name","Rich service records — each service includes SLA commitments, ownership, dependencies, cost, and linked knowledge articles in one place","Subscription and notification model — users subscribe to services they depen

57d on and receive proactive updates when availability or terms change","Business service mapping — each IT service is linked to the business capabilities it supports, giving IT leaders a clear line of sight to business impact"]},{id:"cmdb",num:"04",icon:Qt,title:"CMDB — Configuration Management Database",badge:"Always-Accurate CI Data",desc:"A CMDB that nobody trusts is worse than no CMDB at all — it generates noise, slows incident diagnosis, and undermines every downstream process that depends on it. XpediOo's AI-driven CMDB keeps configuration item (CI) data accurate automatically by continuously reconciling discovery data against what's actually running in your environment.",points:["Auto-discovery and reconciliation — agents continuously scan your infrastructure and update CI records without manual intervention","Relationship mapping — dependencies between CIs (servers, applications, services, databases) are detected and visualised automatically","Change impact analysis — before any change is approved, the CMDB shows every downstream CI and service that could be affected","Drift detection — when the actual state of infrastructure diverges from the recorded state, the platform raises an alert and proposes a correction","Federated data model — pulls CI data from cloud providers, virtualisation layers, network scanners, and agent-based tools into a single normalised record"]},{id:"problem",num:"05",icon:We,title:"Problem Management",badge:"Eliminate Recurring Failures",desc:"Incident management puts out fires; problem management stops them from starting. XpediOo's AI-powered Problem Management proactively identifies patterns across your incident history, surfaces hidden root causes, and drives permanent fixes before users are impacted again.",points:["Trend analysis — AI continuously analyses incident data to identify recurring failures that indicate an underlying problem","Automatic problem record creation — when a pattern meets defined thresholds, a problem record 

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57st for call background noise","Acoustic echo cancellation and comfort noise injection for natural-sounding pauses","Incremental response generation — the TTS engine starts synthesizing the first sentence of a response before the LLM has finished generating the full answer"]},{name:"Context-Aware Conversational Reasoning",desc:"The agent maintains a structured call graph, not just a raw transcript window.",highImpact:["Entity State Tracking — every name, account number, date, product, and commitment mentioned during the call is extracted and updated in a typed entity store that survives topic switches","Intent History with Priority — the call graph records a stack of intents (primary goal, secondary request, expressed concerns) so the agent can return to interrupted threads without the caller having to repeat","Confidence Gating — when the agent's response confidence falls below threshold, it generates a clarifying question rather than guessing, with automatic escalation if confidence remains low after two retries"],standard:['Coreference resolution handles "it", "that", and "they" across turns without losing the referent','Temporal reasoning parses relative dates ("next Tuesday", "two weeks ago") against the call timestamp',"Structured output schema validation with self-correction retries ensures extracted data is always well-formed before being written to downstream systems"]}]},{icon:ge,title:"Autonomous Action & Enterprise Integration",tagline:"The agent doesn't just talk — it reads, writes, and orchestrates across your enterprise stack mid-call, without the caller having to wait on hold.",modules:[{name:"Mid-Call Parallel System Execution",desc:"Actions fire while the caller is speaking — not after.",highImpact:["Speculative Prefetch — when the agent detects an account-lookup intent, it begins the CRM query during the caller's utterance; by the time the ASR finalizes the transcript, results are already in cache","Write-Ahead Action Buffer — update and create operations (ticket creation, order status change) are composed during reasoning and submitted with a single call at the point the agent is ready to confirm verbally","ITSM First-Contact Resolution — the agent can create, update, escalate, and close service tickets, search the knowledge base for known issues, and read CMDB records — all mid-conversation without putting the caller on hold"],standard:["Optimistic locking and idempotency keys on all write operations prevent duplicate records from retry on telephony timeouts","Credential vaulting with per-integration health monitoring — the agent surfaces a graceful fallback when a downstream system is unavailable rather than failing silently","50+ pre-built enterprise connectors span CRM (Salesforce, HubSpot), ITSM (ServiceNow, Jira), ERP (SAP, Oracle), HRIS (Workday, SuccessFactors), and identity (Active Directory)"]},{name:"Telephony Platform Coverage",desc:"Deploy on the infrastructure your enterprise already runs — no forklift upgrade required.",highImpact:["SIP Trunk Integration — the voice agent connects to any RFC 3261-compliant SIP provider; PSTN calls route directly to the agent cluster without a cloud telephony intermediary","Twilio Programmable Voice — full Media Streams support for real-time audio, with automatic reconnection and stream continuity on dropped WebSocket frames","Enterprise PBX — native connectors for Cisco Unified Communications Manager, Avaya Aura Session Manager, and Genesys Cloud with CTI event passthrough"],standard:["DTMF capture for callers who prefer touch-tone input alongside speech (compliance verification, PIN entry, menu fallback)","Call recording controls — agent-managed start/stop with PCI-DSS pause-resume for card capture segments and automatic PHI redaction","Number management — provision, map, and rotate DID numbers per campaign, department, or language queue from a central console"]}]},{icon:yr,title:"Sentiment, Escalation & Human Handoff",tagline:"Autonomous handling is the goal — but when a human is the right answer, the handoff is instant, contextual, and invisible to the caller.",modules:[{name:"Real-Time Sentiment & Intent Scoring",desc:"A parallel analysis pipeline runs alongside the main reasoning engine without adding latency.",highImpact:["Multi-Dimensional Sentiment — frustration, urgency, satisfaction, and churn risk are scored independently per turn using a fine-tuned telephony sentiment model, not a generic NLP classifier","Escalation Trigger Hierarchy — predefined escalation rules fire in order: explicit caller request → frustration threshold crossed → consecutive failure loops → confidence below floor → time-in-call limit","Pre-Escalation Intervention — before transferring, the agent attempts one de-escalation response 

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61,animate:{opacity:1,y:0},transition:{duration:.6},children:[e.jsx("span",{className:"inline-block rounded-full border border-primary/30 bg-primary/10 px-4 py-1.5 text-xs font-medium text-primary mb-6",children:"Legal"}),e.jsx("h1",{className:"text-4xl md:text-5xl font-bold mb-4",children:"Privacy Policy"}),e.jsxs("p",{className:"text-sm text-muted-foreground mb-12",children:["Last updated: ",NX]}),e.jsxs("div",{className:"prose prose-sm dark:prose-invert max-w-none space-y-10 text-muted-foreground",children:[e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"1. Introduction"}),e.jsxs("p",{children:[oj,' ("',e.jsx("strong",{children:"XpediOo"}),'", "',e.jsx("strong",{children:"we"}),'", "',e.jsx("strong",{children:"us"}),'", or "',e.jsx("strong",{children:"our"}),'") operates the XpediOo enterprise AI platform and the website located at ',e.jsx("a",{href:"https://xpedioo.ai",className:"text-primary hover:underline",children:"https://xpedioo.ai"}),' (collectively, the "',e.jsx("strong",{children:"Service"}),'"). This Privacy Policy explains how we collect, use, disclose, and safeguard information about you when you visit our website, request a demonstration, or use any component of the XpediOo platform.']}),e.jsx("p",{className:"mt-3",children:"Please read this Privacy Policy carefully. By accessing or using the Service, you acknowledge that you have read, understood, and agree to be bound by all the terms of this Privacy Policy. If you do not agree with the terms of this Privacy Policy, please do not access the Service."})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"2. Information We Collect"}),e.jsx("h3",{className:"text-base font-semibold text-foreground mb-2",children:"2.1 Information You Provide to Us"}),e.jsx("p",{children:"We collect information you voluntarily provide when you:"}),e.jsxs("ul",{className:"list-disc pl-6 mt-2 space-y-1",children:[e.jsx("li",{children:"Fill in a contact or demo request form on our website (name, work email address, company name, job title, and message content)."}),e.jsx("li",{children:"Register as a partner through our Partner Registration programme (company details, business information, contact details)."}),e.jsx("li",{children:"Sign in to the XpediOo platform using credentials issued by your organisation."}),e.jsx("li",{children:"Communicate with us by email, telephone, or other means."}),e.jsx("li",{children:"Subscribe to newsletters or marketing communications (email address)."})]}),e.jsx("h3",{className:"text-base font-semibold text-foreground mb-2 mt-6",children:"2.2 Information Collected Automatically"}),e.jsx("p",{children:"When you visit our website, we may automatically collect certain technical information including:"}),e.jsxs("ul",{className:"list-disc pl-6 mt-2 space-y-1",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Log data:"})," IP address, browser type and version, operating system, referring URLs, pages viewed, and the date and time of your visit."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Usage data:"})," Information about how you navigate and interact with our website (page views, click paths, session duration)."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Device information:"})," Device type, unique device identifiers, and mobile network information."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Cookies and similar tracking technologies:"})," See Section 7 for details."]})]}),e.jsx("h3",{className:"text-base font-semibold text-foreground mb-2 mt-6",children:"2.3 Information from Third Parties"}),e.jsx("p",{children:"We may receive information about you from third-party sources such as business data providers, analytics vendors, and social media platforms, which we combine with information we already hold about you."})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"3. How We Use Your Information"}),e.jsx("p",{children:"We use the information we collect for the following purposes:"}),e.jsxs("ul",{className:"list-disc pl-6 mt-2 space-y-1",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Providing the Service:"})," To operate, maintain, and improve the XpediOo platform and our website."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Communications:"})," To respond to inquiries, send demo confirmations, and provide platform updates and support."]}

61),e.jsxs("li",{children:[e.jsx("strong",{children:"Marketing:"})," To send promotional content, product updates, and event invitations where you have consented or where we have a legitimate interest. You may opt out at any time."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Analytics:"})," To understand how visitors interact with our website and to improve the user experience."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Security:"})," To detect, prevent, and address fraud, abuse, and other harmful activities."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Legal compliance:"})," To comply with applicable laws, regulations, legal processes, and governmental requests."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Business operations:"})," To fulfil partner agreements, process transactions, and manage our business relationships."]})]})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"4. Legal Bases for Processing (EEA / UK Users)"}),e.jsx("p",{children:"If you are located in the European Economic Area or the United Kingdom, we process your personal data under the following legal bases:"}),e.jsxs("ul",{className:"list-disc pl-6 mt-2 space-y-1",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Contractual necessity:"})," Processing required to perform a contract with you or take pre-contractual steps at your request."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Legitimate interests:"})," Processing for our legitimate business interests (e.g., analytics, marketing to business prospects), provided those interests are not overridden by your rights."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Consent:"})," Where we have asked for and you have provided consent (e.g., marketing emails). You may withdraw consent at any time."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Legal obligation:"})," Processing required to comply with applicable law."]})]})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"5. How We Share Your Information"}),e.jsx("p",{children:"We do not sell your personal data. We may share information with:"}),e.jsxs("ul",{className:"list-disc pl-6 mt-2 space-y-1",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Service providers:"})," Third-party vendors that process data on our behalf under data processing agreements (e.g., cloud hosting, email delivery, analytics platforms)."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Business partners:"})," Authorised XpediOo channel partners, where necessary to fulfil a transaction or provide a service you have requested."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Legal authorities:"})," Law enforcement, regulatory bodies, or courts when required by applicable law or to protect our legal rights."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Business transfers:"})," In connection with a merger, acquisition, asset sale, or similar corporate transaction, subject to confidentiality obligations."]})]})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"6. Data Retention"}),e.jsx("p",{children:"We retain personal data for as long as necessary to fulfil the purposes described in this Privacy Policy, unless a longer retention period is required by law. When data is no longer needed, we securely delete or anonymise it. Platform customer data is retained in accordance with the terms of the applicable enterprise agreement."})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"7. Cookies"}),e.jsx("p",{children:"Our website uses cookies and similar tracking technologies to enhance your experience. We use:"}),e.jsxs("ul",{className:"list-disc pl-6 mt-2 space-y-1",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Strictly necessary cookies:"})," Required for the website to function (e.g., session management, security). These cannot be disabled."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Analytics cookies:"})," Used to understand how visitors interact with our website. These may be set by us or third-party analytics providers."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Preference cookies:"})," Used to remember your settings (e.g., theme preference)."]})]}),e.jsx("p",{className:"mt-3",children:"You can control cookies through your browser settings. Disabling certain cookies may affect the functionality of the website."})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"8. Data Security"}),e.jsx("p",{children:"We implement appropriate technical and organisational security measures to protect your personal data against unauthorised access, disclosure, alteration, or destruction. 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61in our Privacy Policy and applicable enterprise data processing agreement.']}),e.jsx("h3",{className:"text-base font-semibold text-foreground mb-2 mt-4",children:"4.3 Feedback"}),e.jsx("p",{children:"If you provide feedback, suggestions, or ideas regarding the Service, you grant us an irrevocable, royalty-free, worldwide licence to use, copy, modify, and incorporate such feedback into the Service without any obligation to you."})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"5. Privacy and Data Processing"}),e.jsxs("p",{children:["Your use of the Service is also governed by our ",e.jsx("a",{href:"/privacy",className:"text-primary hover:underline",children:"Privacy Policy"}),", which is incorporated into these Terms by reference. Enterprise customers who process personal data through the Service should contact us at ",e.jsx("a",{href:"mailto:[email protected]",className:"text-primary hover:underline",children:"[email protected]"})," to execute a Data Processing Agreement as required by applicable data protection laws (including GDPR and UK GDPR)."]})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"6. Subscriptions and Payment"}),e.jsx("p",{children:"XpediOo is offered under enterprise subscription agreements negotiated separately with each customer organisation. Payment terms, subscription tiers, renewal periods, and refund policies are as specified in your applicable order form or master services agreement. In the event of conflict between these Terms and an executed enterprise agreement, the enterprise agreement will prevail."})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"7. Confidentiality"}),e.jsxs("p",{children:[`Each party agrees to maintain the confidentiality of the other party's non-public business, technical, or financial information disclosed in connection with the Service ("`,e.jsx("strong",{children:"Confidential Information"}),'"), using at least the same degree of care it uses to protect its own confidential information (but no less than reasonable care). Confidential Information does not include information that is or becomes publicly known through no fault of the receiving party, is independently developed by the receiving party, or is required to be disclosed by law or court order.']})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"8. Disclaimers"}),e.jsx("p",{children:'THE SERVICE IS PROVIDED "AS IS" AND "AS AVAILABLE" WITHOUT WARRANTIES OF ANY KIND, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHERWISE. TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, XPEDIOO DISCLAIMS ALL WARRANTIES, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, AND NON-INFRINGEMENT.'}),e.jsx("p",{className:"mt-3",children:"We do not warrant that the Service will be uninterrupted, error-free, or free of viruses or other harmful components. AI-generated outputs and recommendations are provided for informational purposes and should be reviewed by qualified personnel before being relied upon for business-critical decisions."})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"9. Limitation of Liability"}),e.jsx("p",{children:"TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL XPEDIOO BE LIABLE FOR ANY INDIRECT, INCIDENTAL, SPECIAL, CONSEQUENTIAL, PUNITIVE, OR EXEMPLARY DAMAGES — INCLUDING LOSS OF PROFITS, REVENUE, DATA, BUSINESS, OR GOODWILL — ARISING OUT OF OR IN CONNECTION WITH THESE TERMS OR THE SERVICE, EVEN IF XPEDIOO HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES."}),e.jsx("p",{className:"mt-3",children:"OUR TOTAL CUMULATIVE LIABILITY ARISING OUT OF OR IN CONNECTION WITH THESE TERMS OR THE SERVICE WILL NOT EXCEED THE GREATER OF (A) THE AMOUNT PAID BY YOU TO US IN THE TWELVE MONTHS PRECEDING THE CLAIM, OR (B) ONE HUNDRED US DOLLARS (USD $100)."}),e.jsx("p",{className:"mt-3",children:"Some jurisdictions do not allow the exclusion or limitation of certain liabilities. In such jurisdictions, our liability is limited to the minimum extent permitted by law."})]}),e.jsxs("section",{children:[e.jsx("h2",{className:"text-xl font-semibold text-foreground mb-4",children:"10. Indemnification"}),e.jsx("p",{children:"You agree to indemnify, defend, and hold harmless XpediOo, its affiliates, officers, directors, employees, and agents from and against any claims, liabilities, damages, losses, and expenses (including reasonable legal fees) arising out of or in any way connected with: (a) your access to or use of the Service; (b) your violation of these Terms;

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