https://flur.ee/assets/ArchiveCataloguing-YNRFQ8Z5.js

1import{j as e,L as s}from"./react-vendor-VptLKEvj.js";import{P as v,S as f,a as o,f as i,g as n,L as w,d,i as j,l as c,v as A}from"./index-BWlUU3al.js";import{S as N}from"./SideBySideComparison-DuUSmizs.js";import{G as I}from"./GartnerBadges-BNT7q2Ys.js";import{H as k}from"./HowItWorksStage-CGTh73Lx.js";import{A as M,a as C,b as F,c as S}from"./accordion-DJ25XJgX.js";import{F as E}from"./film-DjEGEuaW.js";import{F as L}from"./TagIcon-BWY4wiEN.js";import{F as R}from"./SparklesIcon-CFPSWE7_.js";import{F as T}from"./RectangleStackIcon-Zw0KNm8T.js";import{F as q}from"./BookOpenIcon-CXsPwSG_.js";import{F as G}from"./ShieldCheckIcon-B7RAU7Bn.js";import{F as P}from"./CpuChipIcon-DCZCZcxB.js";import{D as O}from"./download-DCL6Hyv1.js";import"./ui-vendor-BsZg97Zl.js";import"./minus-C1e-xxuL.js";import"./check-DsdpN74k.js";const m="/platform/cam",D="/solutions/media/content-monetization",W="/resources/fluree-taxonomy-solutions",B="/blog/controlling-llms-with-enterprise-taxonomies",Y="/blog/taxonomies-versus-ontologies-a-short-guide",z=["cbc","npr","dow-jones","associated-press","dotdash-meredith","medscape"],V={sources:[{name:"Avid iNEWS",tag:"News MAM · OAuth",icon:"app"},{name:"Dalet MAM",tag:"Broadcast MAM",icon:"app"},{name:"Tape archive",tag:"Legacy · metadata sync",icon:"db"},{name:"DAM / photos",tag:"Image library",icon:"lake"}],nodes:["Clip","Person","Location","Topic","Rights"],stagedLabel:"4 systems · 500K assets staged",query:"Cleared Arctic climate footage, 2018–2023?",sparql:["SELECT ?clip ?date ?rights WHERE {","  ?clip a ebu:VideoClip ;",'        ebu:topic   "Arctic climate" ;',"        ebu:created ?date ;","        ebu:rights  ?rights .",'  FILTER (?rights = "cleared")',"}"],answerTitle:"12 cleared clips · 3 archives",answerIds:["CLP-2019-A","CLP-2021-B","CLP-2023-C"],kpi:"100K+ / 24 hrs",kpiLabel:"CLASSIFIED & TAGGED",citationLabel:"traced · 4 systems",steps:[{num:1,title:"Connect content systems.",body:"MAM, DAM, tape libraries, CMS — Fluree reads metadata and media in place. No migration, no duplication."},{num:2,title:"AI tags your vocabulary.",body:"Not generic labels. Your editorial taxonomy, managed in ITM. CAM processes video, audio, text, and images — every format, against your terms."},{num:3,title:"Search, discover, reuse.",body:"Plain-English search across every format and every decade. Every result carries its rights status and traces back to source."}],header:{kicker:"How it works",titleLead:"Three steps.",titleTail:"One living library.",dek:"From legacy tape libraries to AI-ready archive — with your editorial vocabulary preserved and governed.",id:"how-it-works-media"}},H=[{id:"your-vocabulary",label:"Your vocabulary, not generic labels",title:"Tagged against your editorial taxonomy, not “person” and “outdoor scene”",icon:L,body:"Fluree CAM tags content against YOUR controlled vocabulary, managed in ITM. A news clip isn’t “two people at a podium” — it’s tagged with the journalist’s name, the location from your geography taxonomy, the topic from your editorial taxonomy, and the series from your production database.",bullets:["Your terms, not Rekognition or Cloud Video AI defaults","Taxonomy team owns the vocabulary, AI a

1pplies it","Editorial standards enforced at archive-scale speed"]},{id:"legacy-enrichment",label:"Decades back to searchable",title:"100,000+ assets tagged in 24 hours — against your current taxonomy",icon:R,body:"Process millions of hours of legacy content through AI classification. Every asset gets entities extracted (people, places, organizations, topics), concepts mapped, and relationships linked. Content invisible for decades becomes searchable in days.",bullets:["National broadcaster case: ~500K assets, 10× knowledge growth","Works on content that was never properly catalogued","Tagged, governed metadata on every asset"]},{id:"multi-format",label:"Every format, one query",title:"Video, audio, text, images — one search, every result",icon:T,body:"Speech-to-text for audio, scene analysis for video, OCR for scripts and documents, visual recognition for images. Every format maps to the same controlled vocabulary in the same knowledge graph. One query returns clips, recordings, scripts, and photographs together.",bullets:["Video + audio + text + images + production documents","Same vocabulary across every format","Cross-format results in one view"]},{id:"vocabulary-modernization",label:"Legacy tags ↔ current terms",title:"Cross-vocabulary alignment keeps old archives searchable under new terms",icon:q,body:"When your vocabulary evolves — “Eastern Europe” becomes “Central and Eastern Europe,” “Peking” becomes “Beijing,” “Czechoslovakia” splits — ITM auto-aligns legacy tags to current taxonomy. Assets tagged with the old term become discoverable under the new one without re-tagging.",bullets:["Auto-alignment — no re-tagging projects","Multilingual mappings included","Vocabulary evolves continuously, not as one-time migrations"]},{id:"rights-provenance",label:"Clearance as query",title:"Rights status visible alongside every asset, every search",icon:G,body:"Rights metadata connects to content in the graph. Every asset links to its rights holder, license terms, territory restrictions, and talent agreements. When someone searches for content, clearance status is visible alongside the asset itself — not in a separate spreadsheet.",bullets:["Owner, restrictions, territory, expiration in one view","Licensing decisions in minutes, not days","Immutable audit trail of every access and reuse"]},{id:"ai-ready",label:"Archive as intelligence platform",title:"Your library, queryable by AI agents natively",icon:P,body:"The enriched archive IS a knowledge graph — entities, relationships, and governed metadata ready for GraphRAG, MCP-connected agents, and conversational research. Build recommendation engines, automated highlight reels, content-aware ad insertion, and copilots that query your governed metadata.",bullets:["Native MCP + GraphRAG retrieval","Agents query with full provenance and rights","Archive becomes a revenue engine, not a cost center"]}],$=H.map(a=>{const r=a.icon;return{key:a.id,menuLabel:a.label,content:e.jsx(A,{icon:e.jsx(r,{className:"h-9 w-9"}),title:a.title,body:a.body,bullets:a.bullets})}}),_=[{cap:"Speed",traditional:"Months of manual, or noisy generic-AI batches",fluree:"100,000+ assets in 24 hours"},{cap:"Vocabulary alignment",traditional:"Generic labels or inconsistent manual tagging",fluree:"Your controlled taxonomy, governed in ITM"},{cap:"Multi-format coverage",traditional:"One format at a time, one system each",fluree:"Video + audio + text + images + documents"},{cap:"Entity resolution",traditional:"Not supported",fluree:"Golden records — same person across every asset"},{cap:"Relationship extraction",traditional:"Not supported",fluree:"People, places, topics, series — all connected"},{cap:"Legacy-term alignment",traditional:"Manual, or not at all",fluree:"Automatic — legacy tags ↔ current taxonomy"},{cap:"Rights integration",traditional:"Separate spreadsheets or systems",fluree:"Connected in the graph alongside content"},{cap:"Search experience",traditional:"Requires exact metadata queries",fluree:"Plain English across every format"},{cap:"AI / GraphRAG ready",traditional:"Not supported",fluree:"Native — archive queryable by AI agents"}],u=[{q:"Do we have to replace our existing MAM or archive system?",a:"No. Fluree creates an intelligence layer over your existing systems. Your MAM, DAM, tape management, and digital archive stay in place;

1 content doesn’t move. Fluree connects and enriches it with AI-powered metadata."},{q:"How is this different from generic AI tagging services?",a:"Generic AI (Rekognition, Cloud Video AI) produces inconsistent labels — “person” instead of the journalist’s name, “building” instead of your location taxonomy term. Fluree tags against YOUR controlled vocabulary, managed by your taxonomy team in ITM. Every tag aligns with your editorial standards."},{q:"How fast can you process our archive?",a:"100,000+ assets in 24 hours. Most organizations have enriched, searchable content within days. Full archive processing depends on volume, but the pipeline scales horizontally."},{q:"What about content tagged with outdated vocabulary?",a:"ITM’s cross-vocabulary alignment automatically maps legacy terms to your current taxonomy. Content tagged “Eastern Europe” in 1995 becomes discoverable under “Central and Eastern Europe” today — without re-tagging individual assets."},{q:"What formats do you support?",a:"Video (all major formats), audio (WAV, MP3, broadcast formats), text (scripts, transcripts, documents), images (photographs, stills, production art), PDFs, and born-digital content. All formats process through the same AI pipeline and tag against the same vocabulary."},{q:"Can we search across all formats at once?",a:"Yes. One query spans video, audio, text, and images — all in plain English. “Footage of [journalist] at [location] in 2019” returns clips, recordings, scripts, and photographs from a single search."},{q:"How does this help with rights and licensing?",a:"Rights metadata connects to content in the knowledge graph. When someone searches for content, they see clearance status alongside the asset — owner, restrictions, territory, expiration. Licensing decisions become queries, not research projects."},{q:"Can AI agents query our archive?",a:"Yes. The enriched archive is a knowledge graph ready for GraphRAG and MCP-connected AI agents. Build recommendation engines, automated highlight reels, content-aware ad insertion, and conversational research tools — all querying governed, trusted metadata."}],h="group/tile relative flex flex-col rounded-2xl border border-border/60 bg-card overflow-hidden transition-all duration-300 hover:border-border hover:-translate-y-0.5 hover:shadow-fluree-md",p=({children:a})=>e.jsxs("span",{className:"inline-flex items-center gap-2 text-[10px] font-mono font-semibold uppercase tracking-[0.22em] text-muted-foreground mb-3",children:[e.jsx("span",{className:"w-1.5 h-1.5 rounded-full bg-accent"}),a]}),K=({children:a})=>e.jsxs("span",{className:"mt-auto pt-5 inline-flex items-center gap-1.5 text-[13px] font-medium text-fluree-purple",children:[a,e.jsx(n,{className:"w-3.5 h-3.5 transition-transform group-hover/tile:translate-x-1"})]}),g=({href:a,kicker:r,title:t,body:l,span:x,tint:y="cyan"})=>{const b=y==="cyan"?"radial-gradient(120% 90% at 20% 20%, rgba(19,198,255,0.18), transparent 58%), radial-gradient(100% 90% at 100% 100%, rgba(19,198,255,0.08), transparent 55%)":"radial-gradient(120% 90% at 80% 20%, rgba(172,110,216,0.22), transparent 58%), radial-gradient(100% 90% at 0% 100%, rgba(122,91,255,0.10), transparent 55%)";return e.jsxs(s,{to:a,className:`${h} ${x}`,children:[e.jsxs("div",{className:"relative aspect-[16/9] overflow-hidden bg-white",children:[e.jsx("img",{loading:"lazy",src:"/semantic-layer/tiles-backdrop.webp",alt:"","aria-hidden":!0,className:"absolute inset-0 h-full w-full object-cover transition-transform duration-500 group-hover/tile:scale-[1.03]"}),e.jsx("div",{"aria-hidden":!0,className:"absolute inset-0",style:{background:b}})]}),e.jsxs("div",{className:"p-6 flex-1 flex flex-col",children:[e.jsx(p,{children:r}),e.jsx("h3",{className:"text-lg font-extrabold tracking-tight leading-snug text-foreground mb-2 group-hover/tile:text-fluree-purple transition-colors",children:t}),e.jsx("p",{className:"text-sm leading-relaxed text-muted-foreground",children:l}),e.jsx(K,{children:"Read the article"})]})]})},U=()=>e.jsxs(s,{to:W,className:`${h} p-8 md:p-10`,style:{background:"radial-gradient(80% 100% at 0% 0%, color-mix(in oklab, hsl(var(--accent)) 12%, transparent), transparent 55%), hsl(var(--card))"}

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1s."})]}),e.jsxs("span",{className:"relative z-[1] mt-auto pt-12 inline-flex items-center gap-1.5 text-[13px] font-medium text-accent",children:["Download overview",e.jsx(O,{className:"h-3.5 w-3.5"})]})]}),ge=()=>{const a={"@context":"https://schema.org","@type":"FAQPage",mainEntity:u.map(t=>({"@type":"Question",name:t.q,acceptedAnswer:{"@type":"Answer",text:t.a}}))},r={"@context":"https://schema.org","@type":"BreadcrumbList",itemListElement:[{"@type":"ListItem",position:1,name:"Home",item:"https://flur.ee/"},{"@type":"ListItem",position:2,name:"Publishing & Media",item:"https://flur.ee/solutions/media"},{"@type":"ListItem",position:3,name:"Archival & Cataloguing",item:"https://flur.ee/solutions/media/archival-cataloguing"}]};return e.jsxs(v,{solutionsSubnavVariant:"industries",children:[e.jsx(f,{title:"Archival & Cataloguing — Make Your Archive Searchable Again",description:"Fluree catalogues decades of video, audio, text, and images against your editorial vocabulary — 100,000+ assets in 24 hours. 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Every asset findable, every tag governed, every rights status attached."}),e.jsxs("div",{className:"mt-8 flex flex-col gap-3 sm:flex-row",children:[e.jsx(i,{asChild:!0,variant:"primary",theme:"purple",size:"md",children:e.jsx(s,{to:"/contact",children:"Book a Demo"})}),e.jsx(i,{asChild:!0,variant:"outline",size:"md",iconRight:e.jsx(n,{}),children:e.jsx(s,{to:m,children:"See Content Intelligence in Action"})})]})]})})})}),e.jsx(w,{slugs:z,label:"Trusted by publishing & media leaders"}),e.jsx("section",{className:"py-16",children:e.jsx("div",{className:"container mx-auto px-6",children:e.jsxs("div",{className:"grid gap-10 lg:grid-cols-[0.85fr_1.15fr] lg:items-start",children:[e.jsxs(o,{children:[e.jsx(d,{label:"What Is Archival & Cataloguing",variant:"vibrant"}),e.jsx("h2",{className:"mt-3 text-3xl font-extrabold tracking-tight text-foreground md:text-4xl",children:"AI that tags like your best archivist — at the speed of your entire library."})]}),e.jsx(o,{delay:120,children:e.jsxs("div",{className:"space-y-4 rounded-2xl border border-border/60 bg-card p-7 text-base leading-relaxed text-muted-foreground md:p-8 md:text-lg",children:[e.jsx("p",{children:"Your archive holds decades of premium content — news footage, documentaries, interviews, raw b-roll, audio recordings, photographs. 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