GraphRAG: How Lettria Unlocked 20% Accuracy Gains with Qdrant and Neo4j

Lettria, a leader in document intelligence, achieved a 20-25% accuracy improvement in regulated industries like finance, aerospace, and pharmaceuticals by integrating Qdrant's vector search capabilities with Neo4j's graph-based semantic understanding. Traditional Retrieval-Augmented Generation (RAG) systems fell short in high-stakes environments requiring precise and auditable outputs. Lettria's innovative solution involved building a robust document parsing engine, automatic ontology builder, and a dual ingestion pipeline for vectors and graph enrichment. The system maintained consistent data alignment between Qdrant and Neo4j through a custom transaction mechanism, ensuring atomic updates and conflict resolution in concurrent environments. By flattening payloads and managing over 100 million vectors with low latency, Lettria created a scalable and accurate GraphRAG platform that enhanced explainability and transparency for clients. This approach not only improved performance but also secured high-value contracts by delivering reliable, audit-grade results in complex document intelligence applications.

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