GraphRAG (Graph Retrieval-Augmented Generation) is an advanced approach to grounding AI in enterprise data. Instead of relying solely on vector similarity matching like traditional RAG, GraphRAG uses knowledge graphs to provide LLMs with semantically connected, contextually rich information. This enables AI to understand relationships between entities—customers, products, transactions, policies—delivering more accurate, explainable answers.

Traditional RAG converts documents into vectors and retrieves content based on mathematical similarity—but similarity isn’t accuracy. The LLM receives isolated text chunks without understanding how data relates. GraphRAG retrieves connected entities with their relationships, giving the AI full business context. The result: traditional RAG typically hits an ~80% accuracy ceiling, while GraphRAG can achieve 95%+ accuracy with complete explainability.

GraphRAG is ideal when your AI needs to answer complex questions that span multiple data sources, require understanding of relationships between entities, or demand complete auditability. If you’re experiencing hallucinations with traditional RAG, need to enforce data governance at the AI layer, or want to trace every answer back to its source, GraphRAG provides the accuracy and explainability that enterprise use cases require.

Model Context Protocol (MCP) is an open standard that provides a universal interface for AI agents to connect to external data sources—think of it as a “USB-C port for AI.” Instead of building custom integrations for every LLM, MCP provides a standardized way for any AI model to query your knowledge graph. Fluree’s MCP Server lets developers connect Claude, GPT, or any MCP-compatible model directly to enterprise data with built-in governance.

GraphRAG improves accuracy by providing the LLM with semantically connected context rather than isolated text fragments. When you ask a question, GraphRAG traverses relationships in your knowledge graph to retrieve not just relevant documents, but the actual entities, their attributes, and how they connect to each other. This structured context eliminates the guesswork that causes hallucinations, resulting in up to 4x accuracy improvement over traditional vector-only approaches.

Fluree embeds security policies directly at the data layer—not as middleware bolted on after the fact. Access controls are enforced at query time, meaning sensitive data is filtered before it ever reaches the LLM. This ensures that AI responses only contain information the user is authorized to see, and that proprietary data never leaks to unauthorized users or external AI models. Complete audit trails track every query for compliance.

Start by auditing your current data landscape: identify the key data sources your AI needs to access, map the relationships between entities (customers, products, transactions), and assess data quality. From there, evaluate whether you need to build a knowledge graph from scratch or can leverage existing ontologies in your industry.

Most organizations begin with a focused pilot—a single use case where traditional RAG is falling short—rather than attempting enterprise-wide deployment. This lets you validate accuracy improvements and build internal expertise before scaling. Whether you build in-house or work with a vendor, the foundational work of defining your semantic model and data relationships is the same.