Large language models are often treated as the center of modern AI systems, but in enterprise deployments the real performance ceiling is set much earlier — at the data modeling layer. In this talk, Max Helzberg draws on experience building operational AI systems in complex environments to show why domain-specific ontologies and semantic data models are critical for reliable LLM and agent performance. When models interact with messy operational data, poorly structured schemas lead to hallucinations, brittle reasoning, and systems that fail in production. The session explores how purpose-built ontologies differ from traditional data warehouses, and how those differences influence downstream model behavior. Topics include entity resolution at scale, semantic relationships between operational objects, and how structured action hierarchies enable reliable agent workflows. Through real-world examples from sectors such as energy, finance, and national security, the talk highlights common failure modes in enterprise AI systems and shows how carefully designed semantic layers dramatically improve reasoning quality, traceability, and auditability. Attendees will leave with practical insights into how data architecture choices influence the success of LLM applications — and why the most important design decisions for AI systems often happen long before model training begins.
Max Helzberg is an Applied AI practitioner with more than a decade of experience deploying data and machine learning systems in complex enterprise environments. He previously worked as a Forward Deployed Engineer at Palantir Technologies, where he built operational AI systems across sectors including energy, finance, and government. His work focuses on bridging the gap between cutting-edge AI capabilities and real-world production systems, with particular interest in data architecture, semantic modeling, and large-scale operational AI deployments.
