Over the last year, building DrunR forced us to confront a question that many AI teams will face sooner than they realize:

What happens when AI moves beyond answering questions and starts helping people make real-world decisions?

In healthcare, the consequences of a recommendation can impact a person's health, treatment success, and quality of life. We quickly learned that large language models alone were not enough. We needed structured knowledge, grounded data, explainable scoring systems, feedback loops, and a way to connect AI recommendations to real-world outcomes.

In this session, I'll share lessons learned while building an AI-powered platform for metabolic health and GLP-1 care. We'll explore the difference between information systems and decision systems, common mistakes startups make when integrating AI into high-stakes environments, and why the next generation of AI products will need to move beyond chat interfaces.

Whether you're building a startup, AI product, or enterprise platform, you'll leave with practical insights on designing AI systems people can trust