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300+ connectors bring structured systems and unstructured content into reach. Fluree Sense maps databases and SaaS; Fluree CAM extracts entities from documents and media.
Enterprise AI Search
Enterprise AI Search That Proves Its Answers
Most enterprise AI search tools find documents that look relevant. Fluree answers the question — across structured systems and unstructured content — with citations on every answer and permissions enforced at the data layer. Built for the enterprises where “probably right” isn’t good enough.
Before
hours, maybe days- 01Search the intranet. Get ten stale links.
- 02Search three more systems. Different answers.
- 03Ask a colleague who asks a colleague.
- 04Give up and rebuild the analysis from scratch.
Forrester finds analysts lose 12 hours a week searching siloed data.
Fluree
After
seconds↓ asked
“What did we commit to Acme in the 2024 renewal?”
- One answer across contracts, CRM, and email archives.
- Citations to every source record and passage.
- Only the data this user is allowed to see.
Same governed retrieval for people and AI agents — via MCP.
Definition
What is enterprise AI search?
Enterprise AI search lets your people — and your AI assistants — ask questions across every internal system and get direct, cited answers instead of ranked links. It combines natural language understanding, retrieval-augmented generation, and semantic search over the knowledge your organization already has.
The hard part isn’t the interface — it’s the retrieval. Keyword indexes don’t understand your business, vector stores lose the relationships between facts, and neither enforces who’s allowed to see what. That’s why so many enterprise search deployments end as a smarter-looking intranet box that still can’t answer a cross-system question.
Fluree makes the knowledge graph the retrieval layer: GraphRAG returns connected, permission-filtered context — so every answer is grounded, governed, and traceable to its source.
How does it work — from connection to cited answer?
Semantic understanding, NLP, RAG, and a knowledge graph as the retrieval layer — four stages that turn scattered systems and content into one governed answer surface.
Understand
Content resolves against your business vocabulary — entities, relationships, and meaning. Duplicate records merge; “client” and “customer” become one thing.
Retrieve
Each question runs graph traversal, full-text, and vector search in one governed pass — GraphRAG returning connected, permission-filtered context instead of lookalike chunks.
Answer
People and assistants get direct answers with citations — the sources, records, and relationships behind every response. Explore further in natural language.
Core CapabilitiesSix capabilities that separate answers
Six capabilities that separate answers
from search results.
Understands what you mean, not just what you typed
Keyword search matches strings. Fluree resolves each request against a semantic model of your business — entities, relationships, and vocabulary — so “our exposure to Acme” surfaces accounts, contracts, and subsidiaries the words never mentioned.
- Natural-language questions for every team, no syntax to learn
- Customer, account, and client resolve to one entity
- Ontology-driven context that search, assistants, and agents share
Security & Governance
How are security, privacy, and permissions enforced?
Permission enforcement has to happen at the data layer — inside retrieval — not as a filter applied after an index has already been built. Anything less eventually leaks: sensitive content lands in shared embeddings, per-source filters drift, and one misconfigured connector exposes what it shouldn’t.
In Fluree, policies live in the graph with the data. Every request — from a person, an assistant, or an AI agent — is evaluated against attribute-based rules on entities, relationships, and properties. Unauthorized information never enters the context window, because it never leaves the database in the first place. And every access is logged with role and timestamp.
That’s governance and trust in retrieved answers: cited results, policy on every query, and an audit trail that shows exactly who accessed what, when.
Where does governed search pay off first?
The pattern repeats across departments: knowledge that exists but stays buried. These are the use cases where cited, governed answers change how teams work.
Traditional enterprise search vs.
governed AI search on Fluree.
Keyword and intranet search made information findable. Governed, graph-native AI search makes it answerable.
Capability | Traditional Keyword & intranet search | Fluree AI Search on Fluree |
|---|---|---|
Matching basis | Keywords and lookalike passages | Semantic understanding + typed relationships |
Result format | Ranked links to maybe-relevant pages | Direct answers with citations |
Multi-part questions | Fails across silo boundaries | One traversal across connected systems |
Structured data | Invisible to document search | Unified with unstructured content |
Permissions | Per-index filters, easy to drift | Enforced at the data layer, every time |
Sensitive content | Leaks into shared indexes | Never enters unauthorized context |
Freshness | Stale until the next crawl | Live — the graph is the index |
Trust | User judges ten blue links | Every answer traceable to its source |
AI agents | Separate, ungoverned integrations | Same governed retrieval via MCP |
In Production
Global financial services leader
“Semantic tagging went from error-prone and manual to quality-controlled and AI-driven. User trust in the data portal came back.”
~500K
documents made findable
100%
automated tagging
Hundreds
analysts served daily
10×
knowledge base growth
A research data portal where hundreds of analysts find, explore, and trust institutional knowledge — powered by semantic search over a governed graph.
Read the full case studyWhat are the challenges of implementation?
Four failure modes sink most deployments. Each one is an architecture decision — and each is solved before it starts when retrieval runs on a governed graph.
Permission sprawl
Every connected system has its own access model, and per-index filters drift until someone finds a document they shouldn’t. Fluree enforces attribute-based policy at the data layer, so one governance model covers every source — and every query.
Stale indexes
Crawl-and-index architectures answer from yesterday’s snapshot. In Fluree, the graph is the index — when data changes, the answer changes, with no reindexing window.
Invisible unstructured content
Most enterprise knowledge sits in documents nobody tagged. Fluree CAM extracts entities and relationships from PDFs, contracts, audio, and video automatically — with provenance back to the source passage.
Answers nobody trusts
One confident wrong answer and adoption dies. Citations on every response — sources, records, relationships — let users verify instead of guess, which is how trust (and usage) compounds.
FAQ
The governed search playbook.
How graph-native retrieval works, why it beats vector-only search, and what AI-ready data looks like heading into 2026.
The Future of RAG — graph-native AI with Fluree and MCP
Live walkthrough of governed retrieval powering natural-language search and agents.
Watch replay
GraphRAG vs. Vector RAG: when knowledge graphs outperform semantic search
The evidence that explicit relationships beat embedding similarity on multi-hop, governed queries.
Read the articleGraphRAG & knowledge graphs: making your data AI-ready for 2026
The data foundations behind search and agents that actually work.
Read the articleRecognized by Gartner
Search that shows its work.
Give every team — and every AI tool — direct, cited answers over governed knowledge. Connected in weeks, not another six-month indexing project.