As enterprises rapidly adopt LLMs for decision-making, they face a critical challenge: How do we evaluate and control AI-driven outcomes? Traditional AI monitoring tools only catch failures after they happen, but businesses need a way to trace, validate, and align LLM decisions before they cause financial or compliance risks.

This talk introduces graph-based AI evaluation—a method for mapping LLM decision pathways using Neo4j and Retrieval-Augmented Generation (RAG) to track data influence, improve model reliability, and ensure alignment with business goals. We will cover:

Why LLM decision failures happen—and why enterprises struggle to detect them early.
How graph-based AI evaluation helps businesses visualize AI decision logic, detect biases, and prevent costly mistakes.
Real-world applications of Graph AI in LLM deployments, including data-driven decision tracing and compliance monitoring.
LLMs are transforming business processes, but AI evaluation remains an unsolved challenge. This talk equips technical and business leaders with a practical framework for tracing AI decision-making, improving trust, and reducing risk.