Unlock the knowledge trapped in your documents

Fluree CAM scans your unstructured content — PDFs, contracts, audio, video, images, and web — extracts entities and relationships, maps them to your business vocabulary, and converts everything into structured knowledge graph triples.

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The Problem

80% of your enterprise data is unstructured. None of it is queryable.

Your 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.

The CAM Pipeline

From unstructured content to connected knowledge in seven stages.

Plug into the content systems you already run.

CAM ingests virtually any unstructured source — PDFs, audio, video, images, web — using the connectors content teams already trust. Adding a new source is configuration, not engineering.

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

Every format becomes a single normalized text layer.

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 — all without bespoke preprocessing for every format.

PDF

Sections · tables · annotations

Audio

Transcript · speakers · timestamps

Video

Audio extraction · scene context

Image

OCR · visual classification

Every entity maps to a governed concept, not a string.

NER and LLM models identify people, organizations, products, amounts, dates, and domain concepts — 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.

Raw text

GoEuro raised $45 million in Berlin. Kevin joined as CFO after the financing round closed.

Mapped entities

GoEuro → iri:org/goeuro

98%

Organization

$45 million → iri:money/45m

95%

Money

Kevin → iri:person/kevin

93%

Person

Berlin → iri:place/berlin

99%

Place

Documents become structurally connected, not just tagged.

CAM identifies typed relationships between entities — constrained by your ontology — so the output is RDF triples instead of disconnected tags. Knowledge inside content becomes traceable and queryable.

Passage

GoEuro received $45 million from Investor X to accelerate European expansion.

Extracted triples

GoEuro → receivedFunding → $45M

Investor X → investedIn → GoEuro

FundingRound → supportsExpansion → Europe

The Outcome

Imagine your knowledge as a graph.

Documents stop being silos and start being a connected, governed knowledge layer your AI can actually reason over.

Ingest any content.

Audio, PDFs, web, video — CAM ingests unstructured content as-is. No chunking strategy to design. No format-specific pipelines to maintain.

The graph builds itself.

Entities resolve, relationships type themselves, and embeddings link to nodes — all against your business vocabulary, all governed.

Traditional document RAG

With Fluree CAM

BeforeWith CAM

Text chunks as vectorsEntities, relationships, and embeddings as triples
Text chunks as vectorsEntities, relationships, and embeddings as triples
Entity identity: none — "Apple" is a stringUnique IRIs with semantic disambiguation
Entity identity: none — "Apple" is a stringUnique IRIs with semantic disambiguation
Relationships implicit — model must guessExplicit typed relationships in the graph
Relationships implicit — model must guessExplicit typed relationships in the graph
Each chunk independentEntities connect across all documents
Each chunk independentEntities connect across all documents
Provenance: which chunk was retrievedDocument, entity, relationship, and extraction event
Provenance: which chunk was retrievedDocument, entity, relationship, and extraction event
Accuracy ceiling ~80%95%+ with graph-grounded retrieval
Accuracy ceiling ~80%95%+ with graph-grounded retrieval

How It Compares

A different output model than document RAG.

Traditional RAG retrieves text fragments. CAM produces governed semantic knowledge — entities, typed relationships, and embeddings all linked back to source.

Capability	Traditional Document RAG	Fluree CAM
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 — "Apple" is just a string	Unique IRIs with semantic disambiguation
Relationships	Implicit in text — 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

[ CAM in the Fluree platform ]

CAM is the unstructured data on-ramp.

CAM brings documents, audio, video, and web content into the same governed semantic model that Sense, Core, and ITM share.

Fluree ITM
defines the language.
Sense
maps structured data into that model.
CAM
extracts knowledge from unstructured content against the same vocabulary.
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Core
persists the resulting graph as governed enterprise truth.
Fluree AI
turns that connected knowledge into grounded assistants, retrieval experiences, and agent workflows.