AI-Powered Quality Engineering
Getting real leverage out of AI in testing — with the review, traceability and cost controls that stop a generated suite from becoming expensive noise.
What this covers
Four pieces of work. The controls go in before the throughput does.
Generation from requirements
Cases generated from the requirement and its acceptance criteria rather than from the diff, so each one can name what it proves.
A gate on every generated case
Candidates run against a deliberately broken build. Anything that passes while the feature is broken is discarded before a human reads it.
Traceability that survives an audit
Every case pointing at the requirement it exists for — the thing a regulated team is usually maintaining by hand.
Spend per accepted test
Generation cost tracked against accepted output, not calls, so regenerating a case forty times is visible rather than invisible.
How an engagement runs
Three stages, twelve to eighteen weeks, tuned with your own engineers in the loop.
- Stage 01
We audit what the model is producing now
Usually the finding is volume without value: cases that assert the implementation back at itself and would pass against a bug.
- Stage 02
We put the controls in first
Review, traceability and the mutation gate go in before throughput goes up. Faster generation of the wrong thing is not an improvement.
- Stage 03
We tune the loop with your team
The engineers who own the requirements do the reviewing, because that is where the judgement the model lacks actually lives.
What you get
What the coverage is worth once it is measured properly.
- Coverage that maps to requirements, not to lines of diff
- A suite that grows more slowly and catches more
- AI spend you can put in front of a finance team
Case studies
What this looked like when a team actually had the problem.
Start where it hurts most.
Tell us what this looks like in your delivery and we'll say what we would do first — and whether it needs us at all.