How Data Graphs Built a True Hybrid Graph RAG Platform

2026-04-22 · Source: Qdrant Blog on Qdrant - Vector Search Engine · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Advanced, medium

Summary

Data Graphs, a UK-based platform company co-founded by Paul Wilton over 10 years ago, has developed a "True Hybrid Graph RAG Platform" to overcome the limitations of vector-only retrieval. This platform integrates a proprietary high-performance graph database, full-text search, vector embeddings, workflow automation, and an Agentic AI layer, serving industries like sports media, publishing, and Agri-Tech. Data Graphs selected Qdrant as its vector search engine due to its Hybrid Cloud deployment capability within their AWS environment, robust payload filtering aligning with existing JSON-LD and OpenSearch patterns, and strong Terraform support for infrastructure automation. The architecture uses Qdrant and a proprietary GQL/Cypher graph engine, orchestrated by an Agentic AI pump. This system performs real-time embedding from the graph into Qdrant, initiates schema-first retrieval, and enables the AI agent to dynamically choose between graph queries for structured data and vector retrieval for semantic content. After 18 months in production, running on AWS Bedrock with Claude Opus 4.6, the platform reports zero significant issues.

Key takeaway

For AI Architects designing advanced RAG platforms, recognize that vector-only approaches limit precision for empirical data. Your systems should integrate graph databases for structured queries with vector search engines like Qdrant for semantic content. This hybrid approach, orchestrated by an agentic AI, delivers verifiable, domain-aware responses, significantly raising your AI's intelligence ceiling. Consider implementing schema-first retrieval to empower agent decisions.

Key insights

True hybrid RAG combines structured graph data with semantic vector search, dynamically orchestrated by an AI agent for superior results.

Principles

• Vector-only RAG struggles with empirical, precise data queries.
• Graph and vector retrieval are complementary, not competitive.
• A comprehensive schema guides agentic retrieval decisions.

Method

Embed graph properties into Qdrant with metadata. An AI agent retrieves the graph schema, then dynamically decides between graph queries, vector search, or both, blending results for the LLM.

In practice

• Employ Qdrant for efficient semantic search with payload filtering.
• Utilize graph databases for complex, empirical data retrieval.
• Develop agentic layers to orchestrate hybrid retrieval paths.

Topics

• Hybrid RAG
• Knowledge Graphs
• Qdrant
• Agentic AI
• Vector Search
• AWS Bedrock

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Qdrant Blog on Qdrant - Vector Search Engine.