Case Study: Data Graphs achieves true hybrid graph RAG with Qdrant

Data Graphs runs Qdrant in production for 18 months to power true hybrid graph RAG

Data Graphs, a UK-based knowledge graph-as-a-service platform provider, faced the challenge of moving beyond basic vector retrieval to build a true hybrid Graph RAG system. They needed to combine structured, empirical data retrieval from their graph database with semantic similarity retrieval from unstructured content, a capability that standard vector-only systems lacked. To solve this, they turned to vendor Qdrant for its vector search engine.

By implementing Qdrant with its Hybrid Cloud deployment and advanced payload filtering, Data Graphs built a solution where an AI agent dynamically chooses the best retrieval path for each prompt. The results after 18 months in production were highly positive, with the team reporting zero significant issues and praising the stability and developer experience. Qdrant's integration allowed them to create a platform that delivers accurate, cited, and domain-aware AI responses, moving towards a future where agentic interactions are the dominant access pattern.

Data Graphs