# GraphRAG — Graph-Native Retrieval for Enterprise AI Vector RAG retrieves chunks that look similar. GraphRAG traverses typed relationships in your governed knowledge graph — returning verifiable context, not interpretation. The foundation for enterprise AI that stands up to audit. [Book a Demo](/content/contact/index.html) [Watch the Future of RAG](/content/resources/videos/future-of-rag-graph-native-ai-webinar/index.html) ## What Is GraphRAG ### Deterministic retrieval. Cited answers. No hallucinations. Traditional RAG feeds an LLM the text chunks that look most similar to a prompt and asks it to synthesize an answer. The model fills in what it can’t find — that’s the hallucination tax. GraphRAG replaces the lookup. Instead of retrieving by embedding similarity, it traverses explicit entity relationships in your knowledge graph. The path from question to answer is declared, not inferred — across structured systems, unstructured content, and world knowledge alike. Decentralized GraphRAG with Fluree pushes enterprise accuracy past 95%, with cited answers, row-level governance, and no vector index to keep fresh. ### Why GraphRAG Wins #### Six reasons graph traversal beats fuzzy similarity. ##### Traverse explicit paths, not guess at similarity GraphRAG follows typed relationships through your knowledge graph — the exact path from entity to entity to entity. Vector RAG retrieves chunks that look similar and asks the model to fill the gaps. - “What projects has Alice worked on with people who reported to Bob?” is one traversal - Accuracy stays stable as entity count grows; vector-only systems degrade - Deterministic paths → deterministic answers ##### Every answer traces back to the row it came from Responses are grounded in your governed graph, not the model’s interpretation of a snippet. Every answer carries the query, the data that produced it, and the policy that allowed it. - No more “the model said so” — answers are provable - Citations at the entity and property level - Auditable end-to-end for compliance and trust ##### One retrieval surface across every data type Vector systems stop at text embeddings. GraphRAG unifies databases, ERPs, SaaS apps, PDFs, audio, video, and contracts in one governed graph — so retrieval reaches across every source at once. - Fluree Sense classifies structured data against your ontology - Fluree CAM extracts entities from unstructured content - One vocabulary, one retrieval model, every format ##### Security in the graph, not bolted on Authorization is evaluated at the entity, relationship, and property level as part of retrieval itself — not as a filter applied after embeddings have already been computed. - Each user or agent sees only the governed slice they’re allowed - No sensitive data leaks into embeddings or vector stores - Row-level controls travel with every query ##### 95%+ accuracy on decentralized GraphRAG Decentralized GraphRAG adds cryptographic provenance and context-aware retrieval on top of the graph — pushing accuracy past centralized knowledge graphs and far past vector-only RAG. - Centralized relational DB RAG: 8–15% accuracy - Centralized knowledge graph RAG: 60–65% - Decentralized GraphRAG (Fluree): 95%+ ##### No stale vectors. No rebuild windows. Your graph is the retrieval index. When data changes, the answer changes — no embedding refresh job, no vector store reindexing, no drift between what’s true and what was true yesterday. - CDC updates flow directly into retrievable context - New sources join the graph without recomputing anything - Traversal is as fresh as the data itself ### Universal Connectivity Every system. One retrieval surface. #### Why we can - **Connects databases, SaaS, files, and content in place** — no rip-and-replace integration. - **One semantic view across every source** so retrieval reaches the whole business at once. #### Why they can't Vector pipelines see only the text they’ve been fed. When the answer lives across CRM, billing, product, and support systems, fragmented embeddings leave the model guessing at joins that were never expressed. ### 100% Verifiable Accuracy #### Why we can - **Every answer carries its source** — systems queried, records retrieved, relationships traversed, timestamps included. - **In finance, healthcare, legal, and ops,** explainability is not optional — it’s governance. #### Why they can't Vector systems return a relevance score, not a proof — no path from the answer back to its sources. Audit, compliance, and trust all break down. ### Embedded Security #### Why we can - **Every node carries its access rules** — the model never sees unauthorized data. - **Users and agents get a governed slice** instead of a dangerous all-access retrieval layer. #### Why they can't Vector stores treat security as a downstream filter. Sensitive content leaks into shared embeddings, row-level controls disappear, and governance becomes a brittle layer of patches over a fundamentally ungoverned index. | Capability | Traditional Vector RAG | Fluree GraphRAG | | --- | --- | --- | | Retrieval basis | Embedding similarity | Explicit typed relationships | | Context quality | Fragmented text chunks | Pre-connected entity context | | Multi-source queries | Siloed per index | Unified across systems | | Structured data | Bolt-on, limited | Unified with unstructured | | Answer grounding | Probabilistic, model-inferred | Cited, source-provable | | Explainability | Opaque similarity score | Full query path + citations | | Determinism | Non-deterministic | Deterministic traversal | | Hallucination risk | High — model fills gaps | Near-zero — retrieves, doesn’t generate | | Enterprise accuracy | ~60–70% with tuning | 95%+ on decentralized GraphRAG | Webinar replay Live walkthrough **The Future of RAG — Graph-Native AI with Fluree and MCP** GraphRAG and MCP, side by side — grounding agents in a governed knowledge graph. Watch replay The evidence that explicit relationships beat embedding similarity on multi-hop, governed queries. Read the article [Executive download **The complete guide to retrieval, knowledge graphs & LLMs.**](/content/resources/semantic-graphrag-overview/index.html) Download whitepaper What production-ready GraphRAG looks like — and the data foundations teams need to get there. Read the article ### FAQ #### What is GraphRAG? #### How is GraphRAG different from Vector RAG? #### Can we use GraphRAG alongside vector search? #### What kind of accuracy improvements can we expect? #### Does GraphRAG handle unstructured documents? #### How does GraphRAG handle access control? #### What standards does Fluree’s GraphRAG use? #### How long does it take to stand up GraphRAG? ### Recognized by Gartner [Book a Demo](/content/contact/index.html) [Watch the Webinar](/content/resources/videos/future-of-rag-graph-native-ai-webinar/index.html)