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Learn how to integrate Large Language Models (LLMs) into production-level data analytics workflows through this technical conference talk from Numbers Station's Chief Scientist. Discover the Numbers Station platform's approach to automating data analytics using LLMs, Retrieval Augmented Generation (RAG), and multi-agent architecture. Explore practical applications like dashboard search, query generation, and automated analysis summaries for presentations. Follow a detailed case study on building a SQL agent from scratch while understanding key architectural considerations including dynamic control flows and business context integration through a Knowledge Layer. Gain deep technical insights from Stanford Ph.D. graduate Ines Chami, who brings expertise in graph representation learning and data-intensive automation, as she demonstrates how to transform theoretical AI frameworks into scalable, production-ready solutions for advancing organizational data capabilities.
