The Unified
Intelligence
Platform

Fluree is where your data
becomes intelligence and
intelligence runs your business.

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Every dashboard is live

Every agent is governed

Every team shares one truth

Every answer is cited

Natural Language Analytics

Answers you can defend.

Every answer traces back to the exact source — citations, lineage, and policy included.

AI with Guardrails

Agents that stay in bounds.

Shared policy and ontology keep autonomous work safe, audited, and on scope.

Real-Time Applications

Dashboards, always current.

The same graph drives every dashboard and workflow — no extracts, no reconciliation.

Trusted by

Selected Case Studies

Real results across
industries we serve.

Browse all Case Studies

USE CASE Financial Services

10,000+

documents extracted

Driving Just-in-Time Decision Intelligence

A financial services leader used Fluree to automate tagging, improve discovery, and grow a data portal into a more trusted intelligence destination.

“ By integrating Fluree into its content supply chain, the client eliminated error-prone tagging, expanded its knowledge base tenfold, and regained the trust of portal users consuming the data.”

Source: financial services data portal case study

Read the Case Study

USE CASE Banking

330,000+

golden customer records

Turning Customer Records into Proactive Outreach

A community bank used Fluree Sense to unify 3–5 competing reporting systems, create golden records for 330,000+ customers, and enable proactive banker conversations powered by trustworthy household-level data.

“ By providing AI-driven workflows that can scale to millions of records, Fluree Sense provides crucial input for Master Data Management, leading to not only a satisfied customer base, but to a customer-centric, empowered tomorrow.”

Chief Data Officer

Read the Case Study

USE CASE Beauty & Cosmetics

core operating functions

Building a Data Agent Factory

A global beauty brand built a governed AI Agent Factory that turns fragmented marketing, product, and social knowledge into faster, more reliable decision support.

“ Rather than building isolated point solutions, the customer and Fluree designed a comprehensive AI Agent Factory — a repeatable operating model for creating, governing, and deploying Knowledge Agents at scale.”

Source: customer case study

Read the Case Study

Browse all Case Studies

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.

Why Fluree

Trusted AI where others
can only claim it.

Fluree unifies and governs data at the semantic layer — the foundation for GenAI that’s trustworthy and production-ready.

Zero Hallucinations

Deterministic answers, grounded in your graph.

Why we can

AI issues deterministic queries against a connected knowledge graph — it retrieves, never generates.
Every response is verifiable against the source data via GraphRAG, not model interpretation.

Why they can't

Traditional RAG feeds raw text chunks to the model and asks it to synthesize an answer. Without structure, semantics, or relationships, the model is forced to infer and fill gaps — leading to hallucinations.

Security & Governance

Policy, provenance, and lineage at the data layer.

Why we can

Policy-based access controls are embedded at the data layer, not the application layer.
Permissions travel with the data — every AI query enforces role-based rules automatically.

Why they can't

Most AI stacks treat security as an application-layer concern — filters and guardrails applied after the fact. Sensitive data leaks into embeddings, vector stores lack row-level controls, and governance becomes a fragile afterthought.

Speed & Quality

One traversal. No multi-hop. No stale vectors.

Why we can

Semantic graph resolves complex queries in a single pass — no data integration needed.
AI receives rich, pre-connected context instead of stitching fragments from siloed systems.

Why they can't

Siloed systems force AI into multi-hop retrieval: query one database, then another, then reconcile conflicts. Each hop adds latency, introduces inconsistency, and degrades output quality.

From Evaluation to Executive Buy-In.

Explore resources designed for executives evaluating AI data platforms.

The 1-minute version.

Everything Fluree does in 60 seconds — knowledge graphs, embedded security, and AI-ready data.

Watch video

The complete guide to retrieval, knowledge graphs & LLMs.

How GraphRAG, semantic layers, and grounded generation replace hallucination-prone RAG pipelines.

Download whitepaper

Named a Cool Vendor. Featured on the Hype Cycle.

Fluree is analyst-recognized for its unique approach to knowledge graphs and AI data governance.

See the research

For Business Leaders

From Evaluation to Executive Buy-In.

Explore resources designed for executives evaluating AI data platforms.

Quick Overview · 1 min

The 1-minute version.

Everything Fluree does in 60 seconds — knowledge graphs, embedded security, and AI-ready data.

Executive Download

The complete guide to retrieval, knowledge graphs & LLMs.

How GraphRAG, semantic layers, and grounded generation replace hallucination-prone RAG pipelines.

Download whitepaper

For Developers

Ship Trusted AI.

A local-first knowledge graph, a native MCP server, and the primitives that plug into the stack you already have.

MCP Server

Give your agent a graph-native brain.

Drop Fluree into Claude, Cursor, or any MCP client. Answers cite your governed graph — not a vector cache.

mcp-config.json

{
  "mcpServers": {
    "fluree-memory": {
      "type": "stdio",
      "command": "/path/to/fluree",
      "args": ["mcp", "serve", "--transport", "stdio"],
      "env": {
        "FLUREE_HOME": "${workspaceFolder}/.fluree"
      }
    }
  }
}

Read the MCP quickstart

Webinar Replay

The Future of RAG — Graph-Native AI with Fluree and MCP

GraphRAG and MCP, side by side. A live walkthrough of grounding agents in a governed graph.

Watch replay

New

Introducing Fluree Memory

Persistent, policy-bound memory for long-running agents. Every recall is sourced and revocable per tenant.

memory.recall("onboarding_prefs", tenant: acme)

Explore Fluree Memory

Documentation

Platform docs, API reference, quickstarts — deep-linked so Claude can cite them directly.

Open docs

2026 Benchmark Results

Fluree is the fastest read-write knowledge graph across the SPARQLoscope benchmark suite.

Read the report

GitHub

Core, MCP server, and CLI — all in the open.

Join us on Discord

Chat with the team and the community.

Sphere and tile 3D graphic representing connected data

The Foundation

We think
in graphs.

Enterprise answers live in relationships — connect every source, model it as a graph, power your apps and agents, and derive grounded intelligence from a single governed platform.

The Foundation

We think
in graphs.

Enterprise answers live in the relationships between your data. Fluree connects every source into a living knowledge graph, then serves it as grounded, citable intelligence to the humans, agents, and apps that need it.

Explore connectors

◆Iceberg

▦Enterprise Apps

≡CSV

⟐APIs

▤PDFs

▥File Systems

▶Video

◉Audio

◈IoT

Customer

Order

Product

Revenue

NATURAL LANGUAGE

"Which customers drove revenue on our top product last quarter?▊"

STRUCTURED QUERY · SPARQL

SELECT ?customer (SUM(?amount) AS ?revenue)
WHERE {
  ?order  a :Order ;
          :hasCustomer ?customer ;
          :hasProduct  :TopProduct ;
          :hasRevenue  ?amount ;
          :placedOn    ?date .
  FILTER (?date >= "2026-01-01" && ?date < "2026-04-01")
}
GROUP BY ?customer
ORDER BY DESC(?revenue)

Grounded answer

4 citations · verified

Acme Logistics, Northwind, and Contoso drove 62% of revenue on Atlas Pro last quarter.

Order#9912Customer:AcmeProduct:AtlashasRevenue

From the Blog

Latest Thinking

Perspectives on knowledge graphs, AI grounding, and the future of enterprise data from the Fluree team.

View all posts

Thought LeadershipMar 17, 2026

How to Build a Semantic Layer for Enterprise AI

Only 7% of enterprises have AI-ready data. A knowledge graph semantic layer breaks the 80% accuracy ceiling — and here is the architecture to build one.

Kevin DoubledayRead

Thought LeadershipFeb 26, 2026

Introducing Business Intelligence Without the BI Software

For all of its advanced capabilities and $30 billion market size, business intelligence (BI) still doesn’t get used much by companies with less than 5,000…

Kevin DoubledayRead

Thought LeadershipFeb 10, 2026

GraphRAG vs. Vector RAG: When Knowledge Graphs Outperform Semantic Search

Vector RAG fails when answers live in connections, not content. Seven scenarios where GraphRAG traversal decisively outperforms semantic similarity search.

Kevin DoubledayRead

View all posts

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