How we help / Quality engineering
When you need to trust what you are about to release
Quality built into delivery: testing aimed at the risks that matter, automation that earns its keep, and focused evaluation of how AI features behave.
What you get
Quality engineering covers these six things. Most engagements need some of them, not all.
- Test strategy
- Prioritize critical user flows, integrations, and domain-specific risks.
- UI & API automation
- Create repeatable checks across interfaces, APIs, and connected systems.
- AI output evaluation
- Assess consistency, output format, context retention, and potential bias.
- Performance testing
- Check application behavior under realistic load and usage patterns.
- Exploratory testing
- Examine edge cases and user experiences that scripted checks can miss.
- Continuous testing
- Integrate checks into delivery pipelines and make failures actionable.
How it goes
Four steps, in this order. You see working software along the way, not only at the end.
- Identify the risks
Understand the product and agree the behaviors that matter most.
- Design the coverage
Choose automated checks and focused manual testing.
- Test continuously
Run checks across relevant environments and investigate failures.
- Inform the release
Share findings, unresolved risks, and evidence for release decisions.
Where we have done it
Verity, our AI quality solution — accuracy testing and LLM evaluation for AI that has to be trusted.
Is this where you are?
Tell us what is going on and we will reply with what we would do first.