Quality engineering earns its place when it is part of how the software is built, not a stage at the end of it. We design and operate quality programmes that work the way modern delivery actually works — continuous, automated, and owned by the same teams that build the product.
The end state we work towards is one where the dedicated QA function gets smaller because quality is built in. That is the opposite of what most QA consultancies sell, and it is the right answer for almost every client we have worked with.
What we work on
Test automation
Unit, integration, contract and end-to-end suites — designed to run in minutes against every change, not overnight. We are opinionated about what to automate and what to leave to exploratory testing.
The fastest way to fail at test automation is to automate everything. We focus the automated suites on the critical paths and the brittle integrations, and we explicitly leave room for exploratory testing on the UX surfaces where automation gives diminishing returns.
Performance and load
Load, soak, spike and chaos testing — informed by realistic traffic profiles, not nominal load. Built into the delivery pipeline so performance regressions are caught before users see them.
Performance is a product feature. Treating it as a pre-launch gate rather than a continuous concern is how most teams end up with a system that works in test and falls over on launch day.
Accessibility
Automated and manual accessibility testing to WCAG, integrated into the development workflow rather than treated as a one-off audit before launch. We have strong opinions on screen-reader testing and the limits of automated tooling.
Automated checks catch maybe 30% of accessibility issues. The rest needs people who actually use assistive technologies. We work with those testers; we recommend our clients do the same.
Security testing
SAST, DAST, SCA and the manual security testing that catches what tools cannot. Coordinated with the wider security programme rather than run as a separate effort.
Security testing surfaces real findings; security tools surface alerts. We invest in the operating model that turns one into the other — triage, prioritisation, and the fix workflow the development team actually uses.
Quality engineering for AI systems
Evals, prompt-injection defence, drift detection, regression suites for model upgrades. The CI/CD muscle for AI is younger than for traditional software, and we have spent the last two years building it for clients in regulated sectors.
Operating model
Quality engineers embedded in delivery teams, with a small central practice that owns standards, tooling and the hard problems. The central practice maintains the test infrastructure and the discipline; the embedded engineers maintain the relationship with the product.