Building AI Systems You Can Trust

Following a fifteen-year career optimizing traditional machine learning and leading AI divisions at Intel, the speaker identified that enterprise adoption of generative AI is hindered by a critical trust gap rather than a lack of raw performance. This insight drove the founding of Distributional, a platform that replaces marginal optimization with distributional testing and holistic monitoring to detect behavioral shifts in non-deterministic, agentic systems. By shifting focus from atomic metrics to continuous validation of complex workflows, the company enables organizations to centralize operations, mitigate operational risks like hallucination, and scale reliable AI applications.