See CAM on your content.
Walk through your document corpus with a Fluree solutions architect.
Unlock the knowledge trapped in your documents.
Fluree CAM extracts entities and relationships from your unstructured content — PDFs, contracts, audio, video, images, and web. Everything maps to your business vocabulary and lands as structured knowledge graph triples.
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80% of your enterprise data is unstructured. None of it is queryable.
IDC’s Global DataSphere research projected that 80% of the world’s data would be unstructured by 2025 — 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.
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
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 triplesText chunks as vectorsEntities, relationships, and embeddings as triples
- Entity identity: none — “Apple” is a stringUnique IRIs with semantic disambiguationEntity identity: none — “Apple” is a stringUnique IRIs with semantic disambiguation
- Relationships implicit — model must guessExplicit typed relationships in the graphRelationships implicit — model must guessExplicit typed relationships in the graph
- Each chunk independentEntities connect across all documentsEach chunk independentEntities connect across all documents
- Provenance: which chunk was retrievedDocument, entity, relationship, and extraction eventProvenance: which chunk was retrievedDocument, entity, relationship, and extraction event
- Accuracy ceiling ~80%95%+ with graph-grounded retrievalAccuracy ceiling ~80%95%+ with graph-grounded retrieval
Accuracy figures are from Fluree’s April 2024 study, GraphRAG for GenAI Accuracy.
Key benefits of AI-prepared unstructured content.
When content becomes governed knowledge instead of indexed text, everything downstream gets easier — retrieval, analytics, and agents included.
One pipeline for every format
PDFs, audio, video, images, and web content all normalize through the same pipeline — transcription, OCR, and document decomposition included — with no bespoke preprocessing per format.
Knowledge, not chunks
The output is entities with unique IRIs, typed relationships, and linked embeddings — structured triples your AI can traverse and cite, not text fragments it has to interpret.
Governed by your vocabulary
Every extraction maps to concepts in your ontology and is auto-tagged against ITM-managed vocabularies — and uncertain mentions route to human review before they reach the graph.
Provenance on every fact
Each triple traces back to the source document, passage, and extraction event — so prepared knowledge stands up to audit, not just retrieval.
How CAM differs from traditional, manual prep.
Manual preparation means bespoke parsers, hand-tagging, and per-format chunking heuristics — 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.
In Fluree’s April 2024 study, 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 — the unstructured half of an AI-ready data strategy.
Resources for evaluating CAM.
Whitepapers, webinars, and articles to help you evaluate CAM and understand knowledge extraction.
Article · Document extraction
Understanding intelligent document extraction with Fluree
How Fluree pairs knowledge-graph governance with top LLMs to turn unruly PDFs into traceable, reusable knowledge assets — provenance included.
Read the articleArticle · Taxonomies
Controlling LLMs with enterprise taxonomies
Why LLM reliability hinges on the data it’s grounded in — and how SKOS taxonomies, version control, and real-time APIs do the unglamorous work.
Read the article
Webinar replay
Webinar replay
The Future of RAG — Graph-Native AI with Fluree
A live walkthrough of grounding agents in extracted, governed content — built on entities, relationships, and lineage instead of opaque chunks.
Watch replayFAQ
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.
Chunking pipelines split text into fragments and store them as vectors — 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 nodes, so knowledge stays connected and traceable.
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 they’re written to the graph.
Extracted triples and embeddings are written into Fluree Core with full provenance back to the source passage and extraction event — ready for AI, GraphRAG, analytics, and applications the moment they’re written.
New to AI data preparation? Read the complete guide.
Your documents know more than your database.
Stop chunking. Start extracting. Turn unstructured content into a governed, queryable layer of your knowledge graph.