Case Study: Lettria achieves 20% higher accuracy with Qdrant and Neo4j

Lettria boosts accuracy by 20% with Qdrant

Lettria, a leader in document intelligence platforms, faced a challenge with traditional RAG systems, which provided insufficient accuracy and explainability for clients in highly regulated industries like pharmaceuticals and legal compliance. To overcome the limitations of vector-only search, Lettria sought a solution that could integrate with a knowledge graph and chose Qdrant as its vector database for its performance, hybrid search capabilities, and simple deployment.

By integrating Qdrant with Neo4j, Lettria built a hybrid GraphRAG system. Qdrant provided the high-performance vector search that, when combined with the contextual knowledge graph, delivered a 20-25% accuracy improvement. This solution from Qdrant enabled Lettria to offer its clients audit-grade accuracy and robust performance, handling over 100 million vectors with low latency and helping secure high-value contracts.

Lettria