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. 

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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.

Hear directly from Lettria, Qdrant, and Neo4j. 

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About Lettria
Lettria is a no-code Natural Language Understanding (NLU) platform that turns raw text into structured knowledge using a hybrid AI approach. By combining symbolic and statistical methods, Lettria enables precise, explainable text analysis at scale.
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About Qdrant
Qdrant is the open-source vector search engine that powers the next generation of AI applications. Built for performance and scalability, Qdrant enables real-time, high-dimensional similarity search at scale, making it ideal for production-ready semantic search, recommendation systems, and RAG.
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About Neo4j
Neo4j is the world’s leading graph database and analytics platform, helping organizations uncover relationships and patterns in connected data. It is trusted by Fortune 500 companies and developers alike to power intelligent, real-time applications.