Scientific AI Evaluation
A convincing result is not the same as a defensible conclusion.
We trace conclusions back to their evidence, expose competing explanations and identify the conditions under which the result breaks.
Where Does the Evidence Break?
A model may reproduce a physical system accurately and still lose the information that determines its conclusions. We examine the boundaries of that loss through state, dynamics, inference and decision fidelity.
01
Evidence
Your model passes its benchmarks. But do those results support the claim you intend to make? We examine the data, assumptions and evaluation conditions, separating what has been demonstrated from what remains unproven.
02
Alternatives
An impressive result may have more than one explanation. We test competing mechanisms, information advantages and evaluation choices to determine which explanations the evidence can actually exclude.
03
Boundaries
A conclusion may hold under one representation and fail under another. We identify where that happens, what information is lost and how far the original claim can be defended.
What you
get from an
Independent Audit?
The outcome is not another performance score.
It is a clearer account of what the evidence supports, where the claim is vulnerable, and which test should come next.
Independent Scientific Review
An impressive result is not sufficient evidence of a valid claim. We examine assumptions, benchmarks, alternative explanations and the limits of the supporting evidence.
Decision Stress Testing
A model's preferred action may change when a critical physical feature is lost or the evaluation criterion changes. We identify these reversals, measure their consequences and test possible corrections.
Before the conclusion becomes a commitment.
Before publishing a result, deploying a model or committing resources, bring us the claim and the evidence behind it. We identify what still needs to be tested and where an independent examination can make a difference.