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Voltaire Yap, Global Events Manager, Oracle Corp.
Most enterprise LLM failures are not technical, they are trust failures. Models hallucinate, lose alignment with source truth, or generate answers with no traceable provenance, which is unacceptable in regulated industries. This session introduces GraphRAG, an emerging approach that combines knowledge graphs (Neo4j) with retrieval-augmented generation (RAG) to deliver transparent, explainable, and auditable AI outputs.
Attendees will learn how to design, evaluate, and deploy GraphRAG architectures aligned with the EU AI Act, NIST AI Risk Management Framework, and other enterprise governance standards. Through practical examples, the session will show how GraphRAG links entities, relationships, and source documents to model responses, enabling evidence-based reasoning, compliance, and confidence.
What You Will Learn
How GraphRAG extends traditional RAG with Neo4j graphs for explainable AI
Methods for tracing model responses back to data sources and relationships
Design and evaluation patterns that align with regulatory frameworks (EU AI Act, NIST AI RMF)
Who Should Attend
AI architects, data scientists, compliance officers, and enterprise engineers focused on building transparent, traceable, and regulation-ready AI systems.
