Quality Engineering & Testing
Conventional testing assumes a deterministic system: given this input, expect exactly this output. AI systems break that assumption, and teams that try to test them with assertions end up either with flaky suites or with no testing at all.
We do both kinds of work — established automation and performance engineering, and the evaluation harnesses that make probabilistic systems measurable.
Platforms we typically deliver this on
Scope of the service
Test automation
Functional and regression automation with frameworks chosen to be maintainable by your team afterwards.
Performance and load testing
Realistic load modelling, bottleneck identification and capacity recommendations.
Accessibility testing
WCAG 2.1 AA audit combining automated tooling with manual and assistive technology testing.
API and integration testing
Contract testing and service virtualisation so integrated systems can be tested independently.
AI evaluation harnesses
Test set construction, scoring rubrics, LLM-as-judge with human calibration, and regression gates in CI.
Test strategy and QA uplift
Practical strategy and coaching for teams whose testing has fallen behind their delivery pace.
Where this is used
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Regression coverage for a PeopleSoft estate
Every patch cycle required extensive manual regression. We automated the critical paths across student and finance modules.
Outcome — Patch cycles shortened and coverage made consistent rather than variable.
Accessibility audit before launch
A public service had to meet accessibility obligations at launch. Audit with assistive technology found issues automated scanning had missed.
Outcome — Compliance verified against real assistive technology use, not just a scanner.
Building an evaluation suite for a customer assistant
Quality was assessed by spot-checking. We built a maintained test set with scoring rubrics wired into the deployment pipeline.
Outcome — A measured quality baseline, with regressions blocked before release.
Before you get in touch
How is AI testing different?
Outputs vary between runs, so you measure distributions rather than assert equality: maintained test sets, rubric scoring, thresholds and trend monitoring instead of pass/fail on exact strings.
Which frameworks do you use?
Playwright, Selenium, Cypress, Appium, JMeter, k6 and Postman among others — chosen for what your team can maintain.
Can you help our team rather than replace it?
Yes. A common engagement is building the framework and coaching your testers to own it.
Often engaged alongside this
Custom Software Development
Custom web applications, APIs and internal platforms — built to be handed over, maintained and outlive the team that wrote them.
Read moreAICustom AI & Generative AI Development
We build the AI systems that do not exist off the shelf: document processing against your forms, classifiers trained on your taxonomy, copilots that read your systems of record.
Read moreManaged ServicesDevOps & MLOps Managed Services
CI/CD, environment management, release automation and MLOps — the engineering platform your teams build on, run by us.
Read moreStart with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.