Model: define your business vocabulary
Start 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, with no code required.
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Solution · Enterprise knowledge graph
Enterprise knowledge graph platform for governed AI
Fluree is enterprise knowledge graph software that connects the customers, products, suppliers, and contracts your business runs on into one governed graph. It reads source systems where they live, resolves each entity across them, enforces policy on every fact, and keeps every version, so AI agents and people get answers they can trace to the source.
Fluree AI is the context layer for enterprise AI: one governed knowledge graph your people and agents can trust. Its engine, FlureeDB, is a knowledge graph database you can also run yourself.
Definition
What is an enterprise knowledge graph?
An enterprise knowledge graph is a connected model of a company’s data: the real-world entities it runs on, the typed relationships between them, and the vocabulary that defines both, built from every source and governed as one. AI and people follow real connections instead of guessing at joins.
Relational systems imply connections through foreign keys and JOINs. A knowledge graph stores them directly, as 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, not the model, has become the bottleneck for enterprise AI. Fluree builds the graph that’s governed, verifiable, and ready to serve every agent downstream. For the vendor-neutral basics, read what is a knowledge graph.
What does enterprise knowledge graph software do?
Enterprise knowledge graph software connects your sources, resolves entities, models meaning, governs access, and keeps history, then serves the result to AI agents, analytics, and applications. A graph database stores and queries the graph; a knowledge graph platform also does the work of building and governing it.
Connect sources where they live
Reads databases, warehouses, and lake tables in place. Lake tables copied into the graph refresh automatically.
Resolve entities
Matches the records for one customer, product, or supplier across systems into a golden record, with lineage to every source.
Model meaning
Holds the ontology, taxonomies, and controlled vocabularies that define each entity and relationship, in open standards your teams own.
Extract facts from documents
Turns contracts, reports, and policies into graph facts against the same ontology, each kept with the excerpt that supports it.
Govern every fact
Enforces who may see each fact at query time and keeps every version, so you can show what was true, and who could see it, on any past date.
Serve AI and applications
Answers agents over MCP and applications over SPARQL, openCypher, GraphQL, and JSON-LD, with graph, keyword, and vector search in one query.
How Fluree builds an enterprise knowledge graph
Four stages on one platform: model, map, connect, activate. Most teams start with one domain and have a first governed use case live in four to eight weeks. For the full method, read how to build a knowledge graph.
Map: classify and link your data
Fluree classifies structured data against the model and pulls entities and relationships from documents, and from audio and video on the Fluree Enterprise tier. Entity resolution produces golden records with lineage.
Connect: persist in FlureeDB
Everything lands in FlureeDB 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 in natural language, SPARQL, openCypher, or GraphQL, or connect agents over MCP. Power GraphRAG, conversational analytics, and governed agents with answers that trace back to the source.
Federated vs centralized knowledge graphs: which should you build?
A centralized knowledge graph copies data into one store under one model; a federated knowledge graph leaves data in its source systems and joins it at query time under a shared ontology and policy. Most enterprises need both: federate what changes often or can’t move, and copy in what needs speed, history, or enrichment.
Centralized knowledge graph
- One store and one model, so traversals are fast
- History and enrichment kept on every fact
- Copies drift unless they refresh
- Each new source becomes a migration project
Federated knowledge graph
- Data stays in the system that owns it
- Each domain governs its own subgraph
- A shared ontology joins them at query time
- Query speed depends on the sources behind it
In Fluree’s April 2024 study, GraphRAG for GenAI Accuracy, both graph designs started at 60–65% accuracy zero-shot. Once the data was enriched and joined, the centralized graph reached 80–90%, and the decentralized one, where independently governed subgraphs are joined at query time under access policy, reached 95% or better.
Fluree runs both patterns in one engine. It queries Iceberg, Parquet, and SQL sources in place through W3C R2RML mappings, joins remote SPARQL endpoints, and keeps native ledgers for the facts it holds, all in the same query. Fluree reads source systems where they live, and lake tables copied into Fluree refresh automatically.
Entity-resolved and governed knowledge graphs
An entity-resolved knowledge graph keeps one node for each real-world customer, product, or supplier, however many systems describe it. A governed knowledge graph enforces who may see each fact and records where it came from and when, so AI agents get answers that are both correct and allowed.
What is an entity-resolved knowledge graph?
It is a graph in which records that describe the same thing are matched and merged into one golden record before anyone queries them.
- The same customer in CRM, ERP, and billing resolves to one record, with provenance to every source.
- Records are classified against your ontology. Confident matches promote; ambiguous ones queue for review.
- Agents stop double-counting, and a count of customers means real customers.
More on entity resolution and golden records.
What makes a knowledge graph governed?
Policy, provenance, and history live in the graph as data, enforced by the database rather than by each application.
- Attribute-based, role-based, and relationship policies, evaluated on every fact at query time.
- Signed transactions record who changed what.
- Any past state can be queried by transaction, timestamp, or commit.
- SHACL shapes check data against the model.
More on governing AI agents at the data.
How does a knowledge graph create an enterprise 360 view?
A knowledge graph creates a 360 view by resolving every record about a customer, product, or supplier to one entity and linking it to everything around it: accounts, orders, contracts, tickets, and people. Unlike a 360 dashboard, the view is queryable by agents, governed per fact, and kept in history.
Customer 360
Households, accounts, products held, and service history, joined to one customer. A community bank unified 3–5 reporting systems into golden records for 330,000+ customers.
Product 360
Attributes, sources, channels, and pricing, joined to one product. A Global 500 consumer packaged goods company unified five product data sources, found a 48% duplicate rate, and reached 97.5% accuracy in three weeks.
Supplier 360
Suppliers, the parts they ship, the plants that use them, and the contracts that cover them, in one graph. A late shipment traces to the orders and customers it puts at risk. See knowledge graphs for supply chain.
Why Fluree’s Knowledge Graph Is DifferentSix capabilities other graph databases
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, openCypher, GraphQL, and natural language on one engine
- No proprietary query language to hire for
How do you choose an enterprise knowledge graph platform?
Judge a knowledge graph platform on how data gets in, how it resolves and governs that data, and how AI reaches it, not only on query speed. Score each vendor against the criteria below, using the first use case you need in production.
The “typical graph database” column describes common patterns, not any one vendor. Check each vendor’s documentation. Updated October 2026.
Comparing named knowledge graph vendors? Our enterprise knowledge graph buyer’s guide profiles Neo4j, Memgraph, Stardog, Graphwise, eccenca, Amazon Neptune, and Fluree against the same criteria. Moving off a property graph? See Fluree vs. Neo4j and Neo4j alternatives compared.
Enterprise knowledge graph examples by industry
Enterprise knowledge graphs pay off wherever answers depend on connections across systems: customers and households in banking, research and regulatory data in pharma, product catalogs in consumer goods and retail, supplier networks in supply chain and manufacturing, and decades of records in government.
Financial services
Customer and household records, KYC, and risk exposure traced across core banking, CRM, and reporting systems. See knowledge graphs for financial services.
Pharma and life sciences
Research, clinical, and regulatory data under one model. A $2B+ pharmaceutical company consolidated more than 10 warehouses and cut new analytics use cases from nine months to three weeks.
Consumer goods and retail
Product catalogs, attributes, and channel data resolved into golden product records, the base for pricing analytics and AI-ready consumer insights. See knowledge graphs for CPG.
Supply chain and manufacturing
An industrial knowledge graph links parts, bills of materials, plants, suppliers, and maintenance documents, so a disruption traces to the products and customers it affects. See supply chain solutions.
Government
A government department brought 6M+ species records and scanned field reports into one governed knowledge graph, with sensitive-species access controlled by policy.
Media and publishing
Archives and new content tagged against one taxonomy, so editors, search, and licensing teams find the same things. See knowledge graphs for media.
The knowledge graph playbook.
Webinars, whitepapers, and practitioner guides for building a governed knowledge graph in production. New to the topic? Start with what a knowledge graph is and how to build one.
Webinar replay
Live walkthrough
The Future of RAG: Graph-Native AI with Fluree and MCP
The knowledge graph layer that grounds agents in governed, verifiable context, end to end.
Watch replay
Architecture
Executive downloadMaking Knowledge Graphs Operational
How to move from a whiteboard ontology to a governed graph that runs production workloads.
Read the articleSo you think you’re ready for an enterprise knowledge graph?
Download whitepaper
Practitioner guide
How to Create a Knowledge Graph for Analytics
A step-by-step playbook for building a governed graph that serves cross-system analytics, and the AI that follows.
Read the articleEnterprise knowledge graph FAQ
Short answers to the questions buyers ask most when they compare knowledge graph platforms.
What is the difference between a knowledge graph database, engine, and platform?
A knowledge graph database, or engine, stores and queries the graph. A knowledge graph platform, sometimes called a knowledge graph management system, adds the work around it: modeling the ontology, mapping and resolving source data, extracting facts from documents, and governing access. FlureeDB is Fluree’s knowledge graph engine; Fluree AI is the platform built on it.
Which companies sell enterprise knowledge graph software?
Knowledge graph vendors fall into a few groups: property graph databases such as Neo4j; semantic and RDF platforms such as Stardog and Graphwise; cloud graph services such as Amazon Neptune; and governed knowledge graph platforms such as Fluree. Our enterprise knowledge graph buyer’s guide compares the leading vendors on the same criteria.
How is a knowledge graph different from a relational database?
Relational databases imply relationships through foreign keys and JOINs. Knowledge graphs make relationships first-class: explicitly typed and directly traversable. That is the difference between an LLM guessing at a JOIN and an LLM following a declared path from entity to entity.
How is Fluree different from Neo4j and other graph databases?
Neo4j is a property graph database built around Cypher. Fluree is a knowledge graph built on W3C standards (RDF, JSON-LD, SPARQL, OWL, SHACL) that also answers openCypher and GraphQL. It enforces access policy on every fact inside the database, keeps a signed history you can query as of any past moment, turns documents into facts in the same graph, and runs graph, keyword, and vector search in one engine.
Do we need to replace our existing databases?
No. Fluree reads source systems where they live, and lake tables copied into Fluree refresh automatically. It never writes back to your sources. Your systems of record stay as they are; Fluree adds the governed graph that connects them for AI, analytics, and search.
How long does it take to stand up an enterprise knowledge graph?
Most teams have a first governed use case live in four to eight weeks. Start with one domain and one question no current system can answer, prove the value, then expand.
Can a knowledge graph handle both structured and unstructured data?
With Fluree, yes. Tables are mapped into the graph, and PDFs, Office files, HTML, and scans become graph facts, plus audio and video on the Fluree Enterprise tier. Each fact from a document is stored with the excerpt that supports it, under the same ontology and policy as data from tables.
How does an enterprise knowledge graph improve AI accuracy?
It gives the model typed relationships instead of tables it has to join by guesswork. In Fluree’s April 2024 study, GraphRAG for GenAI Accuracy, answers over raw relational tables scored 8–15% zero-shot, against 60–65% over a knowledge graph before any tuning. With the data enriched and joined across independently governed graphs, accuracy reached 95% or better.
What does an enterprise knowledge graph do for AI agents?
It grounds AI agents, copilots, and GraphRAG applications in governed facts. Agents query entities and relationships over MCP or SPARQL, see only what policy allows, and can cite the source of every answer. The same graph serves enterprise search, analytics, and applications.
Can we run Fluree’s knowledge graph engine ourselves?
Yes. FlureeDB, the engine under Fluree AI, runs as a single binary, in Docker, or in your own cloud. It is source-available under the Business Source License 1.1: free in production, including inside your own product, with a commercial license needed only to offer it as a hosted database service. Each release converts to Apache 2.0 on its fourth anniversary.
Recognized by Gartner
Gartner, “Cool Vendors in Data Management: GenAI Disrupts Traditional Technologies,” 30 May 2024. GARTNER and COOL VENDORS are registered trademarks of Gartner, Inc. and/or its affiliates and are used herein with permission. Gartner does not endorse any vendor, product or service depicted in its research publications.
The question isn’t whether you need one.
It’s whether yours is governed, verifiable, and AI-ready. Fluree’s is all three, from day one.
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