We distinguish prediction problems caused by metric definitions and measurement error from the prediction algorithms themselves.

Fragmented and imperfectly measured information can create false confidence. Models, especially those built by AI, may appear convincing in tests and fail when deployed. We build and validate data-driven decision systems that quantify local economic risks and opportunities before those systems are automated and scaled.

Our approach combines economic modeling, machine learning, geospatial analysis, and contextual knowledge from clients. It draws on two decades of work with the World Bank, NASA, and Booz Allen, where it has informed risk assessments, investment decisions, and infrastructure planning. Our focus has remained on ensuring that the underlying evidence is interpretable and decision-ready, while also meeting standards related to safeguards.