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AI Model Assurance for High-Stakes Systems

Independent technical validation of an AI model before it drives a high-impact decision — robustness, calibration, bias, drift and operational limits, in one defensible evidence pack.

The problem

A model that looks strong in a demo can fail silently in production: calibration drifts, a subgroup is mishandled, performance collapses on data it never saw. AI Model Assurance stress-tests the model — not your company — so you find those failures before customers, capital or liability are on the line.

What we validate

  • Performance on held-out data
  • Calibration (reliability of probabilities)
  • Bias & fairness across subgroups
  • Drift and distribution shift
  • Robustness to noise and edge inputs
  • Fragility and overfitting
  • Failure modes
  • Model limitations

What you receive

  • A Proof Score 0–100
  • A technical verdict (READY / REVIEW / NOT READY)
  • A documented evidence pack
  • Prioritized, actionable recommendations

Who it's for

Method, in brief

One principle: before trusting a system, we try to break it — in a controlled, documented way. We work across context, data, performance, calibration, robustness, bias, drift, fragility, overfitting and operational risk, then summarize a Proof Score and recommendations.

Pricing

Starts with an AI Model Snapshot (€750); a full Model Assurance Report is from €2,500.

Related: Independent Model Validation · LLM Assurance · Methodology · Pricing · Sample report · Contact

FAQ

Frequently asked questions

What is included in an AI Model Snapshot?

A fast, independent validation pass on your model's outputs — discrimination, calibration, class balance and a sample-size confidence read — returning a Proof Score, a verdict and the top risks, in days.

What is the difference between a Snapshot and a full Model Assurance Report?

The Snapshot is a quick independent read; the full Report adds calibration, bias, drift, robustness and failure modes in an 8–12 page, committee-ready evidence pack.

Do you need access to source code?

Usually no. We typically work from model outputs, labels or ground truth where available and documentation. Deeper scopes can include more.

Can this support internal governance?

Yes. The evidence pack is built to support internal governance, risk committees and partner or due-diligence review. It is not a certification.