Does your AI generate convincing analyses too?
But what are those analyses actually based on? How do we know they’re not relying on widely accepted assumptions that may be wrong or outdated, speculation, or hallucinations? Does the output also provide insight into relevant empirical data, the key drivers, and the uncertainties?
With the rise of AI, this question is becoming increasingly relevant.
That is why we built an automated validation engine that helps organizations test relevant AI claims against empirical data and shows how much confidence they actually deserve.
AI Validation & the EU AI Act
The EU AI Act increases the need for AI outputs to be transparent, testable, and well-governed. Empirical validation helps make the evidence, key drivers, and uncertainties behind AI claims transparent.
Read more about the EU AI Act →
Broadly applicable
From land development risk and early-stage infrastructure estimates to macroeconomics, interest rate risk, and investments, our validation engine can be applied across a broad range of capital decisions.
AI generates hypotheses. We provide the empirical validation to support your decision.
