Solutions · Applied AI
Know whether your AI still works after every change
Tooling for measuring the accuracy of your AI features. It uses your own cases and expected results, scores them automatically and with human review, checks every release, and regularly tests for changes in behaviour. We adapt it to your use case, so quality becomes a figure your team can act on.
Use cases
Is this you?
Teams launching AI features tend to be confident, right up until a customer shows them an answer that is clearly wrong.
Prompts changed on instinct
Changes are made after trying a few examples, with no way of knowing what else changed as a result.
The provider changed the model
The AI started behaving differently even though nothing changed on your side, and nobody noticed for weeks.
Choosing between models
A cheaper or faster model is available, but there is no fair way to compare it on your own tasks.
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 release pipeline
Your team owns it, and it is useful from the very first release it checks.
- Test sets built from your real cases
- Automatic scoring where possible, and human review where not
- A check in your pipeline that stops a release if quality drops
- Side-by-side comparison of prompts and models
- Regular checks for changes in behaviour, against a fixed set of cases
- Reports showing quality over time
Evaluation harness
Cases
Real inputs with agreed good results
Grading
Automatic scoring and human review
Release gate
Blocks a release below the agreed level
Comparison
Prompts and models, side by side
Drift checks
Regular re-runs against the fixed set
Quality reporting
Quality over time, for each feature
What we do around it
Only as good as the test cases inside it
Building those cases with your people provides most of the value, and it is something a tool cannot do on its own.
- Criteria
- We agree what a correct answer looks like with the people responsible for the outcome, because the tooling cannot score what nobody has defined.
- Cases
- We build test sets from real usage, including the difficult cases that matter most.
- Set-up
- We add the release check and the regular re-tests to your pipeline and schedules.
- Model choice
- We compare possible models on your cases, showing cost and response time alongside accuracy.
- Upkeep
- We refresh the cases as usage changes, so the tooling 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 according to your size, number of users and the support you need. There are no tiers.