1import{r as m,j as e,L as o}from"./react-vendor-VptLKEvj.js";import{P as v,S as j,a as s,f as p,g as l,L as w,l as i,i as N,I as k,d as A,h as x,n as F}from"./index-BWlUU3al.js";import{A as C,a as E,b as R,c as I}from"./accordion-DJ25XJgX.js";import{C as M}from"./ComparisonTable-BDqMVjml.js";import{P as S,B as g,D,a as G}from"./ProductResources-zShTiYXY.js";import{H as T}from"./H1-Blj2XDht.js";import{S as P,a as O,D as $}from"./HowItWorksStageLegacy-C_mtPABN.js";import{F as W}from"./Squares2X2Icon-C4OOWwe4.js";import{F as L}from"./CubeTransparentIcon-DxBAQzyl.js";import{F as q}from"./ShieldCheckIcon-B7RAU7Bn.js";import{F as B}from"./FingerPrintIcon-I5T3DA9M.js";import{F as _}from"./DocumentDuplicateIcon-BCi6pYPg.js";import{F as H}from"./MagnifyingGlassCircleIcon-B7h2PawA.js";import{F as z}from"./MagnifyingGlassIcon-CZGyoxdZ.js";import{F as K}from"./LinkIcon-BNpWBco4.js";import{F as V}from"./SparklesIcon-CFPSWE7_.js";import{F as U}from"./ShareIcon-BErXV-C-.js";import{F as Q}from"./file-text-CKa6Yxo1.js";import{F as Z,C as X}from"./file-audio-tkHwci_z.js";import{V as Y}from"./video-Df22vb-_.js";import{I as J}from"./image-DHdcJxwT.js";import"./ui-vendor-BsZg97Zl.js";import"./download-DCL6Hyv1.js";import"./play-Bad5qd1t.js";function ee({title:t,titleId:a,...r},n){return m.createElement("svg",Object.assign({xmlns:"http://www.w3.org/2000/svg",viewBox:"0 0 20 20",fill:"currentColor","aria-hidden":"true","data-slot":"icon",ref:n,"aria-labelledby":a},r),t?m.createElement("title",{id:a},t):null,m.createElement("path",{fillRule:"evenodd",d:"M2.5 3A1.5 1.5 0 0 0 1 4.5v4A1.5 1.5 0 0 0 2.5 10h6A1.5 1.5 0 0 0 10 8.5v-4A1.5 1.5 0 0 0 8.5 3h-6Zm11 2A1.5 1.5 0 0 0 12 6.5v7a1.5 1.5 0 0 0 1.5 1.5h4a1.5 1.5 0 0 0 1.5-1.5v-7A1.5 1.5 0 0 0 17.5 5h-4Zm-10 7A1.5 1.5 0 0 0 2 13.5v2A1.5 1.5 0 0 0 3.5 17h6a1.5 1.5 0 0 0 1.5-1.5v-2A1.5 1.5 0 0 0 9.5 12h-6Z",clipRule:"evenodd"}))}const te=m.forwardRef(ee),re=["dow-jones","associated-press","dotdash-meredith","webmd-ignite","nsw-health"],b=["IDCâs Global DataSphere research projected that 80% of the worldâs data would be unstructured by 2025 â and in the enterprise, that knowledge is locked in PDFs, contracts, emails, transcripts, and media files. When an AI agent tries to use it, traditional RAG chunks content into fragments and retrieves whatever sounds mathematically similar.","Fluree CAM extracts the actual knowledge from unstructured content. Entities get unique identities, relationships get typed and linked, and facts map to your business vocabulary as structured, queryable knowledge in the graph."],ae=[{number:"01",eyebrow:"Ingest",title:"Plug into the content systems you already run.",body:"CAM ingests virtually any unstructured source â PDFs, audio, video, images, web â using the connectors content teams already trust. Adding a new source is configuration, not engineering.",icon:_,preview:e.jsx("div",{className:"grid gap-3 md:grid-cols-2",children:[["Document repos","SharePoint · OpenText · Box · Drive"],["CMS & web","Drupal · WordPress · HTML · XML feeds"],["Support & email","Zendesk · email threads · chat logs"],["Files & storage","SFTP · S3 · shared folders · MongoDB"],["Search engines","Solr · Elasticsearch"],["Custom","REST APIs · pipeline connectors"]].map(([t,a])=>e.jsxs("div",{className:"rounded-[1.25rem] border border-border/70 bg-card p-4 shadow-fluree-sm",children:[e.jsx("p",{className:"text-[10px] font-mono font-bold uppercase tracking-[0.25em] text-fluree-purple",children:t}),e.jsx("p",{className:"mt-2 text-sm leading-snug text-muted-foreground",children:a})]},t))})},{number:"02",eyebrow:"Convert",title:"Every format becomes a single normalized text layer.",body:"Documents decompose into chapters, sections, headings, and embedded tables; audio is transcribed with speakers and timestamps; video tracks are processed; images go through OCR and visual classification â all without bespoke preprocessing for every format.",icon:H,preview:e.jsx("div",{className:"grid gap-4 sm:grid-cols-2",children:[{Icon:Q,label:"PDF",note:"Sections · tables · annotations"},{Icon:Z,label:"Audio",note:"Transcript · speakers · timestamps"}
1,{Icon:Y,label:"Video",note:"Audio extraction · scene context"},{Icon:J,label:"Image",note:"OCR · visual classification"}].map(({Icon:t,label:a,note:r})=>e.jsx("div",{className:"rounded-[1.25rem] border border-border/70 bg-card p-4 shadow-fluree-sm",children:e.jsxs("div",{className:"flex items-center gap-3",children:[e.jsx("div",{className:"rounded-[0.9rem] bg-accent/10 p-3 text-accent",children:e.jsx(t,{className:"h-5 w-5"})}),e.jsxs("div",{children:[e.jsx("p",{className:"text-sm font-semibold text-foreground",children:a}),e.jsx("p",{className:"mt-1 text-xs text-muted-foreground",children:r})]})]})},a))})},{number:"03",eyebrow:"Extract entities",title:"Every entity maps to a governed concept, not a string.",body:"NER and LLM models identify people, organizations, products, amounts, dates, and domain concepts â then map each to the right IRI in your ontology. Auto-tagging against ITM vocabularies happens in the same pass; uncertain mentions route to review before they hit the graph.",icon:z,preview:e.jsxs("div",{className:"grid gap-4 lg:grid-cols-2",children:[e.jsxs("div",{className:"rounded-[1.5rem] border border-border/70 bg-card p-5 shadow-fluree-sm",children:[e.jsx("p",{className:"text-xs font-mono uppercase tracking-[0.25em] text-muted-foreground",children:"Raw text"}),e.jsx("p",{className:"mt-4 text-sm leading-relaxed text-muted-foreground",children:"GoEuro raised $45 million in Berlin. Kevin joined as CFO after the financing round closed."})]}),e.jsxs("div",{className:"rounded-[1.5rem] border border-accent bg-secondary p-5 shadow-fluree-md",children:[e.jsx("p",{className:"text-xs font-mono uppercase tracking-[0.25em] text-fluree-purple",children:"Mapped entities"}),e.jsx("div",{className:"mt-4 space-y-3",children:[["Organization","GoEuro â iri:org/goeuro","98%"],["Money","$45 million â iri:money/45m","95%"],["Person","Kevin â iri:person/kevin","93%"],["Place","Berlin â iri:place/berlin","99%"]].map(([t,a,r])=>e.jsxs("div",{className:"rounded-xl border border-accent/20 bg-card px-4 py-3",children:[e.jsxs("div",{className:"flex items-center justify-between gap-3",children:[e.jsx("p",{className:"text-sm font-medium text-foreground",children:a}),e.jsx("span",{className:"rounded-full bg-secondary px-2 py-1 text-[10px] font-mono uppercase tracking-[0.18em] text-fluree-purple",children:r})]}),e.jsx("p",{className:"mt-1 text-xs text-muted-foreground",children:t})]},a))})]})]})},{number:"04",eyebrow:"Disambiguate",title:"Same word, right concept.",body:"Ambiguous mentions resolve against surrounding context and ontological relationships, so each connects to exactly the right concept â not a near-miss with a similar string.",icon:te,preview:e.jsx("div",{className:"space-y-3",children:[{mention:"âApolloâ",context:"in a contract about lunar logistics",iri:"iri:org/apollo-aerospace"},{mention:"âApolloâ",context:"in a publishing rights memo",iri:"iri:org/apollo-records"},{mention:"âApolloâ",context:"next to Greek mythology research",iri:"iri:concept/apollo-deity"}].map((t,a)=>e.jsxs("div",{className:"rounded-[1.25rem] border border-border/70 bg-card p-4 shadow-fluree-sm",children:[e.jsxs("div",{className:"flex items-baseline justify-between gap-3",children:[e.jsx("p",{className:"font-mono text-sm text-foreground",children:t.mention}),e.jsx("span",{className:"font-mono text-[11px] text-accent",children:t.iri})]}),e.jsx("p",{className:"mt-1 text-xs text-muted-foreground",children:t.context})]},a))})},{number:"05",eyebrow:"Extract relationships",title:"Documents become structurally connected, not just tagged.",body:"CAM identifies typed relationships between entities â constrained by your ontology â so the output is RDF triples instead of disconnected tags. Knowledge inside content becomes traceable and queryable.",icon:K,preview:e.jsxs("div",{className:"space-y-4",children:[e.jsxs("div",{className:"rounded-[1.5rem] border border-border/70 bg-card p-5 shadow-fluree-sm",children:[e.jsx("p",{className:"text-xs font-mono uppercase tracking-[0.25em] text-muted-foreground",children:"Passage"}),e.jsx("p",{className:"mt-4 text-sm leading-relaxed text-muted-foreground",children:"GoEuro re
1ceived $45 million from Investor X to accelerate European expansion."})]}),e.jsxs("div",{className:"rounded-[1.5rem] border border-accent bg-secondary p-5 shadow-fluree-md",children:[e.jsx("p",{className:"text-xs font-mono uppercase tracking-[0.25em] text-fluree-purple",children:"Extracted triples"}),e.jsxs("div",{className:"mt-4 space-y-3 text-sm text-foreground",children:[e.jsx("div",{className:"rounded-xl border border-accent/20 bg-card px-4 py-3",children:"GoEuro â receivedFunding â $45M"}),e.jsx("div",{className:"rounded-xl border border-accent/20 bg-card px-4 py-3",children:"Investor X â investedIn â GoEuro"}),e.jsx("div",{className:"rounded-xl border border-accent/20 bg-card px-4 py-3",children:"FundingRound â supportsExpansion â Europe"})]})]})]})},{number:"06",eyebrow:"Generate embeddings",title:"Vector and graph retrieval, side by side.",body:"Embeddings get generated alongside extraction and link directly to the corresponding entity nodes. Keyword search, vector similarity, and graph traversal operate as one retrieval system instead of three siloed tools.",icon:V,preview:e.jsxs("div",{className:"rounded-[1.5rem] border border-border/70 bg-card p-5 shadow-fluree-sm",children:[e.jsx("div",{className:"flex items-center justify-center",children:e.jsxs("div",{className:"relative flex h-24 w-24 items-center justify-center rounded-full border border-accent bg-secondary text-accent shadow-fluree-md",children:[e.jsx(X,{className:"h-8 w-8"}),e.jsx("span",{className:"absolute -bottom-7 text-sm font-semibold text-foreground",children:"Kevin"})]})}),e.jsx("div",{className:"mt-12 grid gap-3 md:grid-cols-3",children:["Mention in earnings call","Mention in contract","Mention in email thread"].map(t=>e.jsx("div",{className:"rounded-xl border border-border/70 bg-background px-4 py-4 text-sm text-muted-foreground",children:t},t))})]})},{number:"07",eyebrow:"Transact to graph",title:"Extracted knowledge lands in Fluree Core as governed truth.",body:"Triples and embeddings are written into Core with full provenance back to the source passage and extraction event â ready for AI, GraphRAG, analytics, and applications the moment theyâre written.",icon:U}],ne=ae.map((t,a)=>{const r=t.icon;return{key:t.eyebrow.toLowerCase().replace(/[^a-z0-9]+/g,"-").replace(/^-|-$/g,""),menuLabel:t.eyebrow,menuNumeral:a+1,content:e.jsx(F,{icon:e.jsx(r,{className:"h-9 w-9"}),title:t.title,body:t.body,children:t.preview})}}),se=[["What gets stored","Text chunks as vectors","Entities, relationships, and embeddings as structured triples"],["How retrieval works","Semantic similarity to the query","Graph traversal along typed relationships with optional vector similarity"],["Entity identity","None â âAppleâ is just a string","Unique IRIs with semantic disambiguation"],["Relationships","Implicit in text â the model must guess","Explicit typed relationships in the graph"],["Cross-document connections","Each chunk is independent","Entities connect across all documents automatically"],["Provenance","Which chunk was retrieved","Document, entity, relationship, and extraction event preserved"],["Accuracy ceiling","~80%","95%+ with graph-grounded retrieval"]],oe=[{icon:W,title:"One pipeline for every format",body:"PDFs, audio, video, images, and web content all normalize through the same pipeline â transcription, OCR, and document decomposition included â with no bespoke preprocessing per format."},{icon:L,title:"Knowledge, not chunks",body:"The output is entities with unique IRIs, typed relationships, and linked embeddings â structured triples your AI can traverse and cite, not text fragments it has to interpret."},{icon:q,title:"Governed by your vocabulary",body:"Every extraction maps to concepts in your ontology and is auto-tagged against ITM-managed vocabularies â and uncertain mentions route to human review before they reach the graph."},{icon:B,title:"Provenance on every fact",body:"Each triple traces back to the source document, passage, and extraction event â so prepared knowledge stands up to audit, not just retrieval."}],f=[{q:"What content formats can Fluree CAM prepare?",a:"CAM ingests PDFs and documents, audio, video, images, and web content, plus support threads, email, and chat logs. Documents decompose into sections and tables; audio is transcribed with speakers and timestamps; video tracks are processed; images go through OCR and visual classification."},{q:"How is CAM different from a chunking pipeline?",a:"Chunking pipelines split text into fragments and store them as vectors â entity identity, relationships, and cross-document connections are lost. CAM extracts entities with unique IRIs, typed relationships constrained by your ontology, and embeddings linked to entity n
1odes, so knowledge stays connected and traceable."},{q:"How does CAM keep AI-extracted data accurate?",a:"Every entity maps to a governed concept in your ontology rather than a raw string, ambiguous mentions resolve against surrounding context and ontological relationships, and uncertain extractions route to human review before theyâre written to the graph."},{q:"Where does the prepared data land?",a:"Extracted triples and embeddings are written into Fluree Core with full provenance back to the source passage and extraction event â ready for AI, GraphRAG, analytics, and applications the moment theyâre written."}],c="#7DD3FC",ie={...$,sources:[{name:"Q3-earnings-call.mp3",tag:"Audio · 47 min",icon:"audio",swatch:c},{name:"GoEuro-contract.pdf",tag:"PDF · 132 pages",icon:"doc",swatch:c},{name:"press-release.html",tag:"Web · scraped",icon:"web",swatch:c},{name:"product-demo.mp4",tag:"Video · 8 min",icon:"video",swatch:c}],nodes:["Organization","Person","Money","Contract","Place"],stagedLabel:"1.4M tokens extracted"},u=[{x:60,y:170,glyph:"âª",label:"earnings.mp3",detail:"audio · 47m"},{x:40,y:240,glyph:"¶",label:"contract.pdf",detail:"pdf · 132p"},{x:50,y:310,glyph:"â",label:"press.html",detail:"web · scraped"},{x:70,y:380,glyph:"â¶",label:"demo.mp4",detail:"video · 8m"}],h=[{x:480,y:80,label:"GoEuro",type:"ex:org/goeuro",hue:"cyan"},{x:500,y:200,label:"$45M",type:"ex:money/45m",hue:"cyan"},{x:500,y:320,label:"receivedFunding",type:"typed triple",hue:"purple"},{x:480,y:440,label:"vec[768d]",type:"linked to entity",hue:"purple"}],de=()=>e.jsxs("div",{className:"relative",children:[e.jsx("div",{"aria-hidden":!0,className:"absolute inset-[-6%] pointer-events-none",style:{background:"radial-gradient(closest-side at 50% 50%, rgba(19,198,255,0.16), transparent 65%), radial-gradient(closest-side at 70% 30%, rgba(172,110,216,0.12), transparent 60%)",filter:"blur(36px)"}}),e.jsxs("div",{className:"relative aspect-[540/520]","aria-hidden":"true",children:[e.jsxs("svg",{viewBox:"0 0 540 520",preserveAspectRatio:"xMidYMid meet",className:"absolute inset-0 h-full w-full",children:[e.jsx("defs",{children:e.jsxs("radialGradient",{id:"hero-cam-glow",children:[e.jsx("stop",{offset:"0%",stopColor:"#13C6FF",stopOpacity:"0.07"}),e.jsx("stop",{offset:"50%",stopColor:"#B775D6",stopOpacity:"0.03"}),e.jsx("stop",{offset:"100%",stopColor:"#13C6FF",stopOpacity:"0"})]})}),e.jsx("circle",{cx:"270",cy:"260",r:"210",fill:"none",stroke:"#CFF3FF",strokeWidth:"1"}),e.jsx("circle",{cx:"270",cy:"260",r:"140",fill:"none",stroke:"#C9D3EC",strokeWidth:"0.7",strokeDasharray:"4 6"}),e.jsx("circle",{cx:"270",cy:"260",r:"72",fill:"none",stroke:"#13C6FF",strokeOpacity:"0.15",strokeWidth:"0.6"}),e.jsx("circle",{cx:"270",cy:"260",r:"85",fill:"url(#hero-cam-glow)"}),u.map((t,a)=>e.jsx("line",{x1:t.x,y1:t.y,x2:"270",y2:"260",stroke:"#13C6FF",strokeOpacity:"0.32",strokeWidth:"1",strokeDasharray:"5 5"},`sl-${a}`)),h.map((t,a)=>e.jsx("line",{x1:"270",y1:"260",x2:t.x,y2:t.y,stroke:"#13C6FF",strokeOpacity:"0.55",strokeWidth:"1.6"},`kl-${a}`)),[[480,80,500,200],[500,200,500,320],[500,320,480,440]].map(([t,a,r,n],d)=>e.jsx("line",{x1:t,y1:a,x2:r,y2:n,stroke:"#13C6FF",strokeOpacity:"0.1",strokeWidth:"0.5"},`xc-${d}`)),[[480,80,500,320],[500,200,480,440]].map(([t,a,r,n],d)=>e.jsx("line",{x1:t,y1:a,x2:r,y2:n,stroke:"#B775D6",strokeOpacity:"0.08",strokeWidth:"0.5",strokeDasharray:"3 4"},`xp-${d}`)),u.map((t,a)=>e.jsxs("g",{children:[e.jsx("circle",{cx:t.x,cy:t.y,r:"5",fill:"#C9D3EC",fillOpacity:"0.3"}),e.jsx("circle",{cx:t.x,cy:t.y,r:"2.5",fill:"#4B57A5",fillOpacity:"0.35"})]},`sd-${a}`)),h.map((t,a)=>{const r=t.hue==="cyan"?"#13C6FF":"#B775D6";return e.jsxs("g",{children:[e.jsx("circle",{cx:t.x,cy:t.y,r:"6",fill:r,fillOpacity:"0.08"}),e.jsx("circle",{cx:t.x,cy:t.y,r:"3",fill:r,fillOpacity:"0.3"})]},`kd-${a}`)})]}),e.jsx("img",{loading:"lazy",src:"/platform/fluree-ai-mascot.webp",alt:"","aria-hidden":!0,className:"absolute left-1/2 top-1/2 h-[26%] w-auto -translate-x-1/2 -translate-y-1/2 object-contain",style:{filter:"drop-shadow(0 8px 24px rgba(19,198,255,0.15))"}}),u.map(t=>e.jsxs("div",{className:"absolute -translate-x-1/2 -translate-y-1/2 whitespace-nowrap rounded-[0.9rem] bg-background/90 px-3 py-1.5 shadow-fluree-sm ring-1 ring-border/60 opacity-60 transition-all duration-200 hover:scale-[1.04] hover:opacity-100 hover:ring-foreground
1/15",style:{left:`${t.x/540*100}%`,top:`${t.y/520*100}%`},children:[e.jsxs("div",{className:"flex items-center gap-2",children:[e.jsx("span",{className:"inline-flex h-6 w-6 items-center justify-center rounded-md bg-secondary text-[12px] text-[#00A0D1]",children:t.glyph}),e.jsx("span",{className:"text-[13px] font-semibold leading-tight text-foreground",children:t.label})]}),e.jsx("p",{className:"mt-0.5 font-mono text-[10px] leading-tight tracking-[0.04em] text-[#004E79]",children:t.detail})]},t.label)),h.map(t=>e.jsxs("div",{className:x("absolute -translate-x-1/2 -translate-y-1/2 whitespace-nowrap rounded-[0.9rem] bg-background px-3 py-1.5 shadow-fluree-sm ring-1 transition-transform duration-200 hover:scale-[1.04]",t.hue==="cyan"?"ring-accent/15 hover:ring-accent/30":"ring-[hsl(275,58%,64%)]/15 hover:ring-[hsl(275,58%,64%)]/30"),style:{left:`${t.x/540*100}%`,top:`${t.y/520*100}%`},children:[e.jsx("p",{className:"text-[13px] font-semibold leading-tight text-foreground",children:t.label}),e.jsx("p",{className:x("mt-0.5 font-mono text-[10px] leading-tight tracking-[0.04em]",t.hue==="cyan"?"text-accent":"text-[hsl(275,58%,64%)]"),children:t.type})]},t.label))]})]}),De=()=>{const t={"@context":"https://schema.org","@type":"SoftwareApplication",name:"Fluree CAM",applicationCategory:"BusinessApplication",operatingSystem:"Cloud",description:"Content AI that extracts entities, relationships, and meaning from documents, media, and archives into a governed enterprise knowledge graph.",url:"https://flur.ee/platform/cam",publisher:{"@type":"Organization",name:"Fluree",url:"https://flur.ee"}},a={"@context":"https://schema.org","@type":"FAQPage",mainEntity:f.map(r=>({"@type":"Question",name:r.q,acceptedAnswer:{"@type":"Answer",text:r.a}}))};return e.jsxs(v,{showEnterpriseSubnav:!0,children:[e.jsx(j,{title:"Fluree CAM â Turn Documents Into Structured Knowledge",description:"Fluree CAM turns unstructured content â documents, audio, video, and images â into entities, relationships, and embeddings in a governed knowledge graph.",jsonLd:[t,a]}),e.jsx("section",{className:"pt-10 md:pt-14",children:e.jsx("div",{className:"container mx-auto px-6 pb-16 md:pb-20",children:e.jsxs("div",{className:"grid gap-12 lg:grid-cols-[minmax(0,0.86fr)_minmax(0,1.14fr)] lg:items-center lg:gap-14 xl:gap-16",children:[e.jsx(s,{children:e.jsxs("div",{className:"max-w-2xl",children:[e.jsx(T,{children:"Unlock the knowledge trapped in your documents."}),e.jsx("p",{className:"mt-6 max-w-2xl text-xl leading-relaxed md:text-[24px] md:leading-[32px] text-muted-foreground",children:"Fluree CAM extracts entities and relationships from your unstructured content â PDFs, contracts, audio, video, images, and web. Everything maps to your business vocabulary and lands as structured knowledge graph triples."}),e.jsx("div",{className:"mt-8",children:e.jsx(p,{asChild:!0,variant:"primary",theme:"purple",size:"md",iconRight:e.jsx(l,{}),children:e.jsx(o,{to:"/contact",children:"Book a Demo"})})})]})}),e.jsx(s,{delay:80,children:e.jsx(de,{})})]})})}),e.jsx(w,{slugs:re}),e.jsx(i,{label:"The Problem",labelVariant:"vibrant",heading:"80% of your enterprise data is unstructured. None of it is queryable.",headingClassName:"max-w-[16ch]",body:b[0],contentSpacing:"mt-6",children:e.jsx("p",{className:"text-lg md:text-xl leading-relaxed text-muted-foreground max-w-3xl",children:b[1]})}),e.jsx(N,{sectionLabel:"The CAM Pipeline",heading:e.jsx(e.Fragment,{children:"From unstructured content to connected knowledge in seven stages."}),items:ne}),e.jsxs(i,{label:"The Outcome",labelVariant:"vibrant",heading:"Imagine your knowledge as a graph.",headingClassName:"max-w-[16ch]",body:"Documents stop being silos and start being a connected, governed knowledge layer your AI can actually reason over.",contentSpacing:"mt-10",children:[e.jsx("div",{className:"grid gap-8 lg:grid-cols-2 lg:items-start",children:[{num:1,title:"Ingest any content.",body:"Audio, PDFs, web, video â CAM ingests unstructured content as-is. No chunking strategy to design. No format-specific pipelines to maintain.",Scene:P},{num:2,title:"The graph builds itself.",body:"Entities resolve, relationships type themselves, and embeddings link to nodes â all against your business vocabulary, all governed.",Scene:O}].map(({num:r,title:n,body:d,Scene:y})=>e.jsx(s,{delay:r===2?120:0,children:e.jsxs("div",{className:"flex flex-col",children:[e.jsxs("div",{className:"relative overflow-hidden rounded-[1.5rem] border border-border bg-card shadow-fluree-sm",style:{height:"clamp(360px, 32vw, 460px)"},children:[e.jsx(y,{playKey:0,active:!1,theme:ie}),e.jsx("div",{className:"absolute left-[18px] right-[18px] bottom-[
114px] h-[2px] rounded-full bg-[rgba(9,17,51,0.06)] overflow-hidden",children:e.jsx("span",{className:"block h-full w-full rounded-full bg-gradient-to-r from-[#13C6FF] to-[#7A5BFF]"})})]}),e.jsxs("div",{className:"mt-5 px-1",children:[e.jsxs("div",{className:"flex items-center gap-3 mb-1",children:[e.jsx("img",{loading:"lazy",src:`/fluree-custom-numerals/${String(r).padStart(2,"0")}-vibrant-fill-white_custom.svg`,alt:`Step ${r}`,className:"h-6 w-6 shrink-0",style:{filter:"drop-shadow(0 2px 6px rgba(9,17,51,0.22))"}}),e.jsx("span",{className:"text-[16px] font-bold leading-[1.3] tracking-[-0.01em] text-foreground",children:n})]}),e.jsx("p",{className:"mt-1 text-[14px] leading-[1.6] text-muted-foreground",children:d})]})]})},r))}),e.jsx(s,{delay:180,children:e.jsxs("div",{className:"mt-14 md:mt-16 rounded-[1.75rem] border border-border/70 bg-card p-6 md:p-8 shadow-fluree-sm",children:[e.jsxs("div",{className:"hidden sm:grid sm:grid-cols-[auto_1fr_auto_1fr] items-baseline gap-x-6 mb-5 pb-4 border-b border-border/60",children:[e.jsx("span",{"aria-hidden":!0,className:"h-1.5 w-1.5 rounded-full bg-muted-foreground/40"}),e.jsx("p",{className:"text-[10px] font-mono font-bold uppercase tracking-[0.22em] text-muted-foreground",children:"Traditional document RAG"}),e.jsx("span",{"aria-hidden":!0,className:"h-1.5 w-1.5 rounded-full bg-accent"}),e.jsx("p",{className:"text-[10px] font-mono font-bold uppercase tracking-[0.22em] text-fluree-purple",children:"With Fluree CAM"})]}),e.jsxs("div",{className:"flex gap-4 sm:hidden mb-4 pb-4 border-b border-border/60",children:[e.jsx("span",{className:"text-[10px] font-mono font-bold uppercase tracking-[0.22em] text-muted-foreground flex-1",children:"Before"}),e.jsx("span",{className:"text-[10px] font-mono font-bold uppercase tracking-[0.22em] text-fluree-purple flex-1",children:"With CAM"})]}),e.jsx("ul",{className:"divide-y divide-border/40",children:[["Text chunks as vectors","Entities, relationships, and embeddings as triples"],["Entity identity: none â âAppleâ is a string","Unique IRIs with semantic disambiguation"],["Relationships implicit â model must guess","Explicit typed relationships in the graph"],["Each chunk independent","Entities connect across all documents"],["Provenance: which chunk was retrieved","Document, entity, relationship, and extraction event"],["Accuracy ceiling ~80%","95%+ with graph-grounded retrieval"]].map(([r,n])=>e.jsxs("li",{className:"py-3",children:[e.jsxs("div",{className:"hidden sm:grid sm:grid-cols-[auto_1fr_auto_1fr] items-center gap-x-6",children:[e.jsx("span",{"aria-hidden":!0,className:"h-1 w-1 rounded-full bg-muted-foreground/30"}),e.jsx("span",{className:"text-sm leading-snug text-muted-foreground line-through decoration-muted-foreground/30",children:r}),e.jsx(l,{className:"h-3.5 w-3.5 text-accent"}),e.jsx("span",{className:"text-sm leading-snug font-medium text-foreground",children:n})]}),e.jsxs("div",{className:"flex gap-4 sm:hidden",children:[e.jsx("span",{className:"flex-1 text-sm leading-snug text-muted-foreground line-through decoration-muted-foreground/30",children:r}),e.jsx("span",{className:"flex-1 text-sm leading-snug font-medium text-foreground",children:n})]})]},r))}),e.jsxs("p",{className:"mt-4 border-t border-border/40 pt-4 text-xs leading-relaxed text-muted-foreground",children:["Accuracy figures are from Flureeâs April 2024 study,"," ",e.jsx(o,{to:"/resources/graphrag-genai-accuracy-report",className:"underline decoration-accent/60 underline-offset-4 hover:decoration-accent",children:e.jsx("em",{children:"GraphRAG for GenAI Accuracy"})}),"."]})]})})]}),e.jsx(i,{label:"Key Benefits",labelVariant:"vibrant",heading:"Key benefits of AI-
1prepared unstructured content.",body:"When content becomes governed knowledge instead of indexed text, everything downstream gets easier â retrieval, analytics, and agents included.",contentSpacing:"mt-10",children:e.jsx(s,{children:e.jsx("div",{className:"grid gap-5 md:grid-cols-2",children:oe.map(r=>{const n=r.icon;return e.jsxs("div",{className:"rounded-2xl border border-border/60 bg-card p-7 shadow-fluree-sm",children:[e.jsx(k,{size:"sm",children:e.jsx(n,{className:"h-5 w-5"})}),e.jsx("h3",{className:"mt-4 text-xl font-extrabold tracking-tight text-foreground",children:r.title}),e.jsx("p",{className:"mt-2 text-sm leading-relaxed text-muted-foreground md:text-base",children:r.body})]},r.title)})})})}),e.jsxs(i,{label:"How It Compares",labelVariant:"vibrant",heading:"How CAM differs from traditional, manual prep.",headingClassName:"max-w-[16ch]",body:"Manual preparation means bespoke parsers, hand-tagging, and per-format chunking heuristics â and traditional RAG retrieves the text fragments that process leaves behind. CAM produces governed semantic knowledge: entities, typed relationships, and embeddings all linked back to source.",contentSpacing:"mt-8",children:[e.jsxs("p",{className:"max-w-[65ch] text-lg leading-relaxed text-muted-foreground md:text-xl",children:["In Flureeâs April 2024 study,"," ",e.jsx(o,{to:"/resources/graphrag-genai-accuracy-report",className:"text-foreground underline decoration-accent/60 underline-offset-4 hover:decoration-accent",children:e.jsx("em",{children:"GraphRAG for GenAI Accuracy"})}),", retrieval over similarity-chunked documents plateaued near 80% accuracy, while graph-grounded retrieval reached 95%+. Closing that gap is what preparation is for â the unstructured half of an"," ",e.jsx(o,{to:"/solutions/ai-ready-data",className:"text-foreground underline decoration-accent/60 underline-offset-4 hover:decoration-accent",children:"AI-ready data"})," ","strategy."]}),e.jsx(s,{children:e.jsx("div",{className:"mt-8",children:e.jsx(M,{headers:["Capability","Traditional Document RAG","Fluree CAM"],rows:se})})})]}),e.jsx(i,{label:"Go Deeper",labelVariant:"vibrant",heading:"Resources for evaluating CAM.",headingClassName:"max-w-[16ch]",body:"Whitepapers, webinars, and articles to help you evaluate CAM and understand knowledge extraction.",contentSpacing:"mt-8",children:e.jsx(s,{children:e.jsxs(S,{children:[e.jsx(g,{href:"/blog/understanding-intelligent-document-extraction-with-fluree-part-1-llms",kicker:"Article · Document extraction",title:"Understanding intelligent document extraction with Fluree",body:"How Fluree pairs knowledge-graph governance with top LLMs to turn unruly PDFs into traceable, reusable knowledge assets â provenance included.",span:"md:col-span-7",tint:"cyan",cta:"Read the article"}),e.jsx(D,{span:"md:col-span-5",title:"See CAM on your content.",body:"Walk through your document corpus with a Fluree solutions architect.",children:e.jsx(p,{asChild:!0,variant:"primary",theme:"purple",size:"md",iconRight:e.jsx(l,{}),children:e.jsx(o,{to:"/contact",children:"Book a Demo"})})}),e.jsx(g,{href:"/blog/controlling-llms-with-enterprise-taxonomies",kicker:"Article · Taxonomies",title:"Controlling LLMs with enterprise taxonomies",body:"Why LLM reliability hinges on the data itâs grounded in â and how SKOS taxonomies, version control, and real-time APIs do the unglamorous work.",span:"md:col-span-5",tint:"purple",cta:"Read the article"}),e.jsx(G,{href:"/resources/videos/future-of-rag-graph-native-ai-webinar",cover:"/landing-cards/future-of-rag-cover.webp",kicker:"Webinar replay",title:"The Future of RAG â Graph-Native AI with Fluree",body:"A live walkthrough of grounding agents in extracted, governed content â built on entities, relationships, and lineage instead of opaque chunks.",span:"md:col-span-7"})]})})}),e.jsx("section",{className:"py-16",children:e.jsx("div",{className:"container mx-auto px-6",children:e.jsxs("div",{className:"max-w-4xl",children:[e.jsx(s,{children:e.jsx(A,{label:"FAQ",variant:"vibrant"})}),e.jsx(s,{children:e.jsx("div",{className:"mt-3 rounded-2xl border border-border/60 bg-card px-6 py-2 md:px-8",children:e.jsx(C,{type:"single",collapsible:!0,className:"w-full",children:f.map((r,n)=>e.jsxs(E,{value:`item-${n}`,children:[e.jsx(R,{className:"py-5 text-left text-base font-semibold text-foreground hover:no-underline md:text-lg",children:r.q}),e.jsx(I,{className:"pb-5 text-sm leading-relaxed text-muted-foreground md:text-base",children:r.a})]},r.q))})})}),e.jsx(s,{children:e.jsxs("p",{className:"mt-6 text-sm leading-relaxed text-muted-foreground",children:["New to AI data preparation?"," ",e.jsx(o,{to:"/solutions/ai-data-preparation",className:"text-foreground underline decoration-accent/60 underline-offset-4 hover:
1decoration-accent",children:"Read the complete guide"}),"."]})})]})})}),e.jsx(i,{label:"Get Started",labelVariant:"plum",heading:"Your documents know more than your database.",headingClassName:"max-w-[16ch] mx-auto",body:"Stop chunking. Start extracting. Turn unstructured content into a governed, queryable layer of your knowledge graph.",align:"center",children:e.jsx("div",{className:"flex justify-center",children:e.jsx(p,{asChild:!0,variant:"primary",theme:"purple",size:"md",iconRight:e.jsx(l,{}),children:e.jsx(o,{to:"/contact",children:"Book a Demo"})})})})]})};export{De as default};
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.