Solutions · Applied AI
Know whether your AI still works after every change
A harness for measuring the accuracy of AI features: your cases and expected outputs, automatic and human grading, a gate in the pipeline, and scheduled checks for drift. Adapted to your use case, so quality becomes a number the team can act on.
Use cases
Is this you?
Teams shipping AI features tend to be confident right up until a customer shows them an answer that is plainly wrong.
Prompts changed on instinct
Edits are made after trying a few examples, with no way of telling what else changed as a result.
The provider changed the model
Behaviour shifted with nothing changed on your side, and nobody noticed for weeks.
Choosing between models
A cheaper or faster model is available, and there is no fair way to compare it on your own task.
Leadership wants proof
The board is asking how you know the AI feature is accurate, and there is no measured answer.
What it delivers
Built to run in your pipeline
Owned by your team, and useful from the first release it guards.
- Evaluation sets built from your real cases
- Automatic grading where the task allows it, human review where it does not
- A check in your pipeline that stops a release when quality drops
- Side by side comparison of prompts and models
- Scheduled drift checks against a fixed set
- Reports showing quality over time
Evaluation harness
Cases
Real inputs with agreed good outputs
Grading
Automatic scoring and human review
Release gate
Blocks a release below threshold
Comparison
Prompts and models, side by side
Drift checks
Scheduled rerun against the fixed set
Quality reporting
Quality over time, per feature
What we do around it
Only as good as the cases inside it
Building those cases with your people is most of the value, and it is the part a tool cannot do on its own.
- Criteria
- The standard for a correct answer set with the people who own the outcome, since the harness cannot grade what nobody has defined.
- Cases
- Evaluation sets drawn from real traffic, including the awkward cases that matter most.
- Wiring
- The gate and drift checks added to your pipeline and schedules.
- Model choice
- Candidate models compared on your cases, with cost and latency shown beside accuracy.
- Upkeep
- Cases refreshed as usage changes, so the harness keeps measuring what matters.
Delivered with
Pricing and demonstrations
Tell us which AI feature worries you most
We will show you how we would measure it, using a handful of your own cases.
Priced by your size, users and the support you need. No tiers.