Operationalizing GraphRAG: Lettria’s Scalable Architecture with Neo4j and Qdrant
Learn how Lettria designed a production-scale GraphRAG system by integrating Qdrant’s high-performance vector engine with Neo4j’s graph-based knowledge representation.
Join the livestream at 11 am ET on July 23rd
GraphRAG is expanding what's possible with Retrieval-Augmented Generation, enabling systems to combine the ability to capture the semantics of vector search with the structure of knowledge graphs for greater context and control and better understanding of the relationships in your data.
In this session, you’ll learn how Lettria designed a production-scale GraphRAG system by integrating Qdrant’s high-performance vector engine with Neo4j’s graph-based knowledge representation.
The result: a platform capable of handling over 100 million embeddings with sub-200ms latency and delivering 20–25% higher accuracy than traditional RAG in real-world applications.