Artificial intelligence · Artificial Intelligence
AI quality evaluation and control
We measure whether your artificial intelligence answers well, how much each answer costs and whether it degrades when the model changes.
- Method
- Golden case suite
- Measures
- Quality, cost, latency and regressions
- Duration
- 3 to 6 weeks
- Applies to
- On your own or third-party solutions
The problem
Most AI implementations are never measured: they are launched and trusted. Later the provider updates the model, quality drops and nobody notices until a client complains.
What it includes
Golden case suite
We build a set of cases with their correct answer, including the hard ones and the ones it must never answer.
Automatic judge
An evaluator model with an explicit rubric scores every answer, with deterministic guards for the critical cases.
Regression detection
Every change of model, prompt or data is compared against the baseline before reaching production.
Cost and latency
Measured per answer, to know what operating really costs and where it pays to optimise.
Restricted topics and crises
Verification that the solution never answers what it must not and correctly refers sensitive cases.
How we work
- 01DefinitionWhat a good answer means in your specific case.
- 02DatasetBuilding the suite with real and synthetic cases.
- 03MeasurementBaseline of quality, cost and latency.
- 04IntegrationThe evaluation runs on every change, automatically.
Deliverables
| Document | Length |
|---|---|
| Evaluation suite Yours, reusable and versioned. | included |
| Baseline report Initial quality, cost and latency. | included |
| Approval threshold An explicit criterion for publishing changes. | included |
It also works to audit another provider. If you already have an AI solution under contract and do not know whether it works well, this evaluation answers that with data instead of impressions.