According to the 2024 IBM CEO Study, 71% of organizations see their GenAI pilots stall. What keeps these pilots from moving into production? Hallucinations, privacy concerns, and a lack of traceability.

However, you can ground LLMs to avoid hallucinations, make your GenAI results more accurate, and improve their explainability using GraphRAG. The GraphRAG architecture combines RAG with knowledge graphs to feed LLMs factual, contextual, and traceable data.  As a result, you can get past the piloting stage and build enterprise-grade GenAI applications.

On April 24, join us for a 45-minute overview of how to build accurate, explainable GenAI applications with GraphRAG. Find out the steps to take for a successful GenAI deployment.

You’ll learn how:

• To improve accuracy, governance, and explainability
• A major gaming company increased time to insight 10x and improved traceability
• Klarna built its internal AI assistant Kiki using an internal knowledge graph