RAG lacks structured context, traceability, and reliability. In agentic systems, these weaknesses compound — errors cascade, behavior becomes unpredictable, and fragmented data make multi-hop reasoning nearly impossible. Basic RAG wasn't built for the complexity of real-world production.

Without a clear path forward, your team stalls in prototype mode. Reaching production requires retrieval that’s contextual, accurate, and explainable — and that starts with how you structure your data.

Knowledge graphs provide structured context that addresses the core limitations of RAG.

By modeling relationships explicitly, knowledge graphs enable the traceability and reasoning depth that unstructured retrieval simply can't deliver. GraphRAG puts this into practice — combining the semantic power of vector search with graph-aware retrieval to unlock multi-hop reasoning and richer context assembly.

Join us on April 23 to learn how to build graph-powered RAG systems that move from prototype to production. We'll cover the architectural patterns behind GraphRAG and show you how to design retrieval pipelines that are accurate, explainable, and ready for the real world. Register now.