The AI application your team can actually stand behind

Neo4j describes a GraphRAG architecture intended to make enterprise AI applications more accurate, auditable, and trustworthy by grounding LLM responses in a structured knowledge graph and continuously evaluating results. Built on Neo4j Aura, the approach combines schema-based graph ingestion for structured data with an unstructured-data pipeline that chunks text, extracts and resolves entities, creates embeddings, validates outputs, and stores both semantic vectors and connected facts. Its GraphRAG Python package, Needle StarterKit interface, and DeepEval-based evaluation framework support modular development, graph and vector retrieval, and ongoing quality measurement. After an initial vector-RAG chatbot lacked rich graph context and automated testing, Neo4j rebuilt it with a strict domain schema, an agent that selects either vector-plus-graph retrieval or Text2Cypher queries, and an 80-question expert-curated test suite measuring correctness, relevance, faithfulness, and tool selection. Neo4j reports that the revised system improved pass rates from 68.75% to 100%, correctness from 64.75% to 97.88%, and relevance from 86% to 97.34%, while maintaining 100% faithfulness and handling 1,500 messages in its first week without manual-review escalations.

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