# The data layer your AI actually understands.

An enterprise knowledge graph connects your data the way your business works — entities, relationships, and meaning — so agents traverse real connections instead of guessing at JOINs. Fluree ships one that is governed by default, verifiable to every fact, and AI-ready from day one.

[Read the Knowledge Graph Guide](/content/resources/so-you-think-you-re-ready-for-an-enterprise-knowledge-graph/index.html)

## What Is An Enterprise Knowledge Graph

### Relationships are first-class. Meaning lives in the data.

A knowledge graph represents business information as entities connected by typed relationships. Instead of implying connections through foreign keys and JOINs, it encodes them directly —  a single semantic layer over every system, in every format.

When an agent answers a question like _"Which customers have compliance risk exposure above $1M?"_ it doesn’t stitch tables together — it follows a declared path from **Customer** to **Account** to **Transaction** to **ComplianceRisk**.

That’s why the knowledge graph has become the bottleneck for enterprise AI — not the model. Fluree builds the graph that’s governed, verifiable, and ready to serve every agent downstream.

### Model — define your business vocabulary

Start in Fluree ITM with your ontology, taxonomies, and controlled vocabulary. Use AI-assisted discovery from existing schemas, begin with an upper ontology like GIST or FIBO, or model from scratch — no code required.

### Map — classify and link your data

Fluree Sense classifies structured data against the model; Fluree CAM extracts entities and relationships from documents, audio, and video. Entity resolution produces golden records with lineage.

### Connect — persist in Fluree Core

Everything lands in Fluree Core as RDF triples with typed relationships, embedded security, immutable provenance, and hybrid BM25 + HNSW search in a single engine.

### Activate — serve AI, analytics, and apps

Query via natural language, SPARQL, REST, or MCP. Power GraphRAG, conversational analytics, and governed agents with answers that trace back to the source.

## Why Fluree’s Knowledge Graph Is Different

### Six capabilities other graph databases bolt on. We build in.

### Built on open web standards, not a proprietary query language

Fluree stores data as RDF triples in JSON-LD, modeled with OWL and SKOS, queried with SPARQL. Your graph is portable, interoperable, and lock-in-free — it speaks the language the AI ecosystem already knows.

- RDF + JSON-LD data model
- SPARQL, REST, and natural language in one engine
- No proprietary query language to hire for

### Structured and unstructured data unified under one schema

Fluree Sense classifies structured data. Fluree CAM extracts entities and relationships from documents, audio, and video. Both land in the same governed graph — so one traversal moves from a customer record to a contract clause to a compliance obligation.

- Structured: databases, SaaS, warehouses, APIs
- Unstructured: PDFs, audio, video, contracts
- One semantic model over every source

### Security lives in the graph, not in an app-layer filter

Every node, relationship, and property can carry its own access policy. Authorization is evaluated at query time — each user or agent sees only the governed slice they’re allowed, no matter the retrieval path.

- Row, column, and relationship-level policies
- Zero-trust friendly architecture
- No sensitive data leaks into AI context

### Every fact carries its lineage — cryptographically

Changes create new versions, never overwrites. Ask what a risk score was last quarter, compare contract versions, or prove what the system knew and when — with a cryptographic audit trail instead of reconstructed logs.

- Time-travel queries across every entity
- Diffs between historical versions
- Full audit trail for regulators

### Graph, BM25, and vector search in one engine

Fluree Core serves graph traversal, full-text, and HNSW vector search from the same engine against the same schema. The graph is self-describing, so GraphRAG, natural language queries, and MCP-connected agents work without a stack of glue code.

- Native MCP server for agent workflows
- HNSW vectors + BM25 in the same query
- GraphRAG reaches 95%+ retrieval accuracy

### Stand up a working graph in weeks, not quarters

Fluree collapses the modeling timeline with AI-assisted ontology discovery, automated classification, entity resolution, and continuous sync from source systems. Most teams ship a governed knowledge graph in 4–8 weeks.

- AI-assisted ontology and entity resolution
- Continuous sync from source systems
- Golden records with lineage, not batch ETL

| Capability | Traditional<br>Graph databases | Fluree<br>Knowledge Graph |
| --- | --- | --- |
| Data model | Property graph or pure RDF | W3C RDF + JSON-LD, fully interoperable |
| Query languages | Cypher, Gremlin, or SPARQL | SPARQL + REST + natural language |
| Unstructured data | Separate pipeline or plugin | Native via Fluree CAM entity extraction |
| Security model | App layer or IAM only | Policy in the data, enforced at query time |
| Provenance | Not supported | Time-travel with cryptographic audit |
| Hybrid retrieval | Vector store bolted on | Graph + BM25 + HNSW in one engine |
| AI / MCP integration | Manual glue code | Native MCP server |
| Graph construction | Manual ETL + modeling | AI-assisted via Sense + CAM + ITM |
| GraphRAG readiness | Custom implementation required | Native — 95%+ accuracy |

### FAQ

### What is an enterprise knowledge graph?

### How is a knowledge graph different from a relational database?

### How is Fluree different from Neo4j and other graph databases?

### Do we need to replace our existing databases?

### How long does it take to stand up an enterprise knowledge graph?

### Can a knowledge graph handle both structured and unstructured data?

### How does a knowledge graph improve AI accuracy?

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