Test sets that reflect reality
Cases drawn from your own history, including the difficult ones, with agreed scoring and a clear pass mark before release.
The measurement layer that proves an AI system works, keeps it working and stops the bill running away.
Best for
What it is
A demo can look convincing on the one example someone chose. Evaluation is how you know whether a system is right often enough to trust with real work, and how you notice when a model update changes its behaviour.
Cost control sits alongside it. Token spend, retries and long context windows add up quietly, and the fix is usually design rather than a cheaper model.
What we do
Each engagement covers the same four areas, weighted to where your risk and value actually sit.
Cases drawn from your own history, including the difficult ones, with agreed scoring and a clear pass mark before release.
Evaluation runs on every prompt, model or data change, with results recorded so quality is a trend rather than a memory.
Cost tracked per use case, per team and per interaction, so you can see where the money goes and forecast it properly.
Prompt and context reduction, caching and model routing, so the same outcome costs less without hurting quality.
How we implement
Fixed deliverables, agreed durations and a decision point at the end of every step.
1 week
We agree what good looks like for each system, build the first test set from real cases and record where it stands today.
Deliverables
2 to 3 weeks
Evaluation and cost tracking are wired into the release process, so nothing ships without a score attached to it.
Deliverables
ongoing
We tune prompts, context and routing against the numbers, then review quality and spend together each quarter.
Deliverables
Related
Next step
A 30 minute call on evaluation & cost control, what it would take in your business, and whether it is worth doing at all.