Geospatial Diagnostics
Geospatial Diagnostics
Geospatial Diagnostics
We help organizations diagnose why predictive systems fail when deployed.
We help organizations diagnose why predictive systems fail when deployed.
We distinguish problems caused by how metrics are defined and measured, by the underlying data, and by model evaluation, from problems caused by the prediction models themselves.
Fragmented, incomplete, 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 assess local geographic, climate, and 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 valid, interpretable, and decision-ready, while also meeting standards related to risk and compliance.
Contact: varun@geospatialdiagnostics.com
We distinguish problems caused by how metrics are defined and measured, by the underlying data, and by model evaluation, from problems caused by the prediction models themselves.
Fragmented, incomplete, 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 assess local geographic, climate, and 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 valid, interpretable, and decision-ready, while also meeting standards related to risk and compliance.
Contact: varun@geospatialdiagnostics.com
We distinguish problems caused by how metrics are defined and measured, by the underlying data, and by model evaluation, from problems caused by the prediction models themselves.
Fragmented, incomplete, 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 assess local geographic, climate, and 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 valid, interpretable, and decision-ready, while also meeting standards related to risk and compliance.
Contact: varun@geospatialdiagnostics.com