Ofer Mendelevitch

This talk introduces enterprise deep research as a powerful application for AI agents, focusing on its ability to conduct in-depth, multi-step investigations within private enterprise data. It highlights the challenges of factual accuracy in generative AI and presents an agent operating system designed to mitigate hallucinations and ensure robust information retrieval for enterprise-grade deployments.

This talk introduces open-rag-eval, an open-source project designed for scalable Retrieval-Augmented Generation (RAG) evaluation. It addresses the common challenge of RAG evaluation requiring "golden answers" or "golden chunks," which is often impractical and non-scalable. The project, developed in collaboration with the University of Waterloo, offers a research-backed approach to evaluate RAG pipelines without relying on pre-defined correct answers.