“Is this model good enough to put in front of the public?”
Whether you are buying an AI product or building one, a demo is not evidence. We test models against your own cases before procurement, before launch, and after, so the decision to go live rests on results.
What it looks like
Vendors demo well. Failures show up after procurement, in the cases the demo never covered.
The demo was perfect
Vendor demos use the vendor’s examples. Your edge cases, other languages, and messy records were never in them.
No definition of good
Nobody has written down what accuracy, fairness, or failure rate is acceptable, so nobody can say when it is ready.
Launch and hope
Once live, models drift as inputs change, and nothing is watching for it.
If any of that sounds familiar, here is how we take it apart.
How we solve it
Independent evaluation against test sets built from your own cases (accuracy, bias, and failure modes) and a clear go or no-go before you sign.
Define good
Agree the measures that matter (accuracy, harmful outputs, bias across groups, cost, latency) and the threshold each must pass.
Build your test set
Test cases drawn from your own records and scenarios, weighted towards the edge cases that matter most.
Test and red-team
Run the model or product against the set, probe for failure modes, and compare alternatives side by side.
Monitor in production
Keep evaluating after launch, so drift and new failure modes are caught early.
Data governance in this work
An evaluation is only credible if someone else could repeat it.
- Test sets versioned and documented, so every result can be reproduced.
- Findings mapped to the NIST AI Risk Management Framework.
- A written go or no-go with its evidence, suitable for procurement files and oversight bodies.
What you get
- Evaluation criteria and pass thresholds
- A test set built from your own cases
- An evaluation report with a go or no-go
- Red-team findings and mitigations
- A production monitoring plan
Knowing the approach is half of it. Here is how it gets into production.
Phase by phase, with a gate at each one
Each gate is agreed before its phase begins, so nothing moves forward on optimism.
Define
Measures and thresholds agreed with the people accountable for the decision.
Gate: Thresholds fixed before testingTest
Evaluation and red-teaming against your test set.
Gate: Results against every thresholdDecide
A documented go, no-go, or go-with-conditions.
Gate: Decision on fileMonitor
Ongoing evaluation in production.
Gate: Ongoing
How it runs
Test data stays in your environment, and the results are yours to share with vendors and oversight bodies.
Go deeper
Tell us which model you are about to trust.
A vendor product under review, or your own model before launch: we will help you decide what good enough means, then test it.