Make your archive searchable again.

Decades of video, audio, and text — minimally catalogued, buried in legacy systems, invisible to the teams who need it. Fluree’s AI catalogues your entire archive against your controlled vocabulary at scale: 100,000+ assets tagged in 24 hours. Every asset findable, every tag governed, every rights status attached.

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Trusted by publishing & media leaders

What Is Archival & Cataloguing

AI that tags like your best archivist — at the speed of your entire library.

Your archive holds decades of premium content — news footage, documentaries, interviews, raw b-roll, audio recordings, photographs. Most of it was catalogued with outdated vocabulary, minimal metadata, or not catalogued at all. You own it. You can’t find it.

Generic cloud AI (Rekognition, Cloud Video AI) labels content fast — but the tags are inconsistent and don’t match your editorial taxonomy. "Person" instead of the journalist’s name. "Outdoor scene" instead of your location taxonomy.

Fluree CAM tags video, audio, text, and images against YOUR controlled vocabulary, governed in ITM — so every asset in every format becomes findable, connected, and ready for the AI era.

How it works

Three steps.
One platform.

From a raw source to a governed knowledge graph — with answers that trace back to the row they came from.

Connect any source.

CSV, API, Postgres, Snowflake, Salesforce — Fluree ingests it as-is. No schema migration. No pipelines to maintain.

The graph builds itself.

Entities resolve, duplicates merge, and relationships infer in place — no modeling marathon, no manual ontology.

Answers, with receipts.

Ask in plain language. Every answer traces back to the exact row it came from — for humans, agents, and apps alike.

What Fluree Unlocks For Your Archive

Six capabilities no MAM, DAM, or generic-AI service
gives you on its own.

Tagged against your editorial taxonomy, not “person” and “outdoor scene”

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.

Your terms, not Rekognition or Cloud Video AI defaults
Taxonomy team owns the vocabulary, AI applies it
Editorial standards enforced at archive-scale speed

100,000+ assets tagged in 24 hours — against your current taxonomy

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.

National broadcaster case: ~500K assets, 10× knowledge growth
Works on content that was never properly catalogued
Tagged, governed metadata on every asset

Video, audio, text, images — one search, every result

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.

Video + audio + text + images + production documents
Same vocabulary across every format
Cross-format results in one view

Cross-vocabulary alignment keeps old archives searchable under new terms

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.

Auto-alignment — no re-tagging projects
Multilingual mappings included
Vocabulary evolves continuously, not as one-time migrations

Rights status visible alongside every asset, every search

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.

Owner, restrictions, territory, expiration in one view
Licensing decisions in minutes, not days
Immutable audit trail of every access and reuse

Your library, queryable by AI agents natively

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.

Native MCP + GraphRAG retrieval
Agents query with full provenance and rights
Archive becomes a revenue engine, not a cost center

Side by Side

Manual & generic-AI cataloguing vs.
Fluree.

See how governed AI cataloguing stacks up against manual and generic-AI approaches across every capability that matters for media archives.

Capability	Traditional Manual & generic-AI	Fluree CAM + ITM
Speed	Months of manual, or noisy generic-AI batches	100,000+ assets in 24 hours
Vocabulary alignment	Generic labels or inconsistent manual tagging	Your controlled taxonomy, governed in ITM
Multi-format coverage	One format at a time, one system each	Video + audio + text + images + documents
Entity resolution	Not supported	Golden records — same person across every asset
Relationship extraction	Not supported	People, places, topics, series — all connected
Legacy-term alignment	Manual, or not at all	Automatic — legacy tags ↔ current taxonomy
Rights integration	Separate spreadsheets or systems	Connected in the graph alongside content
Search experience	Requires exact metadata queries	Plain English across every format
AI / GraphRAG ready	Not supported	Native — archive queryable by AI agents

Go Deeper

The content intelligence playbook.

Solution overviews and practitioner guides on taxonomy governance, AI tagging, and making your archive AI-ready.

Why it matters

Controlling LLMs With Enterprise Taxonomies

How governed vocabularies turn taxonomies into the grounding layer for enterprise AI.

Read the article

Solution overview

Tag and organize digital content with Fluree.

How taxonomy governance and auto-content tagging work together for editorial, marketing, and media teams.

Download overview

Short guide

Taxonomies vs. Ontologies — What’s the Difference?

A practitioner’s guide to the models that power content discovery — and when to use each.

Read the article

FAQ

No. Fluree creates an intelligence layer over your existing systems. Your MAM, DAM, tape management, and digital archive stay in place; content doesn’t move. Fluree connects and enriches it with AI-powered metadata.

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.

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.

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.

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.

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.

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.

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.

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