Services / Product Engineering
We design and build testing that gives fast, trustworthy signal: risk-based coverage, reliable automation in your CI pipeline, performance and security checks, and measured evaluation for AI features. Your team owns the result.
Risk-based coverage. No flaky suites. Quality gates with named owners.
The business case
A bug caught by a unit test costs minutes; the same bug found by a customer costs trust, support time and sometimes revenue. Testing is an investment in change speed: teams with trustworthy suites ship more often because they are afraid less.
Releases depend on manual re-checking, defects reach users too often, an existing suite is slow or flaky, or AI features ship without measured quality. Access to the codebase, environments and the people who know the workflows is essential.
If the product itself is still changing shape weekly, a lighter testing foundation alongside an MVP build may serve better than a full automation programme. If AI behaviour is the only concern, our testing & evaluation service goes deeper there.
Where this applies
These are example situations, not pre-packaged promises or claims about past results.
Build an automated regression suite around the workflows your business depends on, so frequent releases stop depending on manual re-checking.
Learn: does the release cycle get faster without a rise in defects reaching users?
Design test coverage alongside a new build, from unit tests to end-to-end journeys, so quality is part of the product rather than a phase at the end.
Learn: are defects found in development and CI rather than by customers?
Pin down the behaviour of an unstable or under-tested application with characterisation and integration tests before further change.
Learn: can the team change code without re-discovering old behaviour by accident?
Test the contracts between your systems, partners and third parties, including failure modes, retries and data validation at the boundaries.
Learn: do integration failures surface as test failures instead of production incidents?
Measure the behaviour of AI features with scenario sets, scoring rubrics and adversarial cases, since a single prompt check proves nothing.
Learn: is AI quality tracked as a measured trend, with regressions caught before release?
Load, stress and resilience testing that shows how the system behaves at real and peak volumes, and what breaks first.
Learn: do you know your system's limits before your customers do?
How the pipeline works
A change enters from development, passes automated checks at the levels that catch problems cheapest, and only continues to release when the quality gate passes. Failures route back with evidence, not guesswork.
Risk-based coverage design, CI integration, visible results and flaky-test triage. These are what make a suite trustworthy enough to act on.
Coverage targets pursued for the number, end-to-end tests for things a unit test proves cheaper, and automation of workflows that are about to be deleted.
Test strategy & coverage
More tests is not the goal. The goal is fast, trustworthy signal about whether the system still works. We design coverage around risk, not coverage percentages.
Map the workflows where a failure costs money, trust or compliance, and weight the test investment there instead of spreading it evenly.
Business rules as fast unit and service tests, integration behaviour at API level, and a small set of end-to-end journeys for what only a real browser can prove.
Every automated test names the risk it covers. Tests that prove nothing or duplicate cheaper checks are deleted, not kept for the count.
Skilled manual exploration stays in the process for usability, edge cases and the failures nobody thought to automate.
Test automation engineering
A flaky suite is worse than no suite: it trains people to ignore failures. We build automation as software, with the same review and maintenance standards as the product.
Browser tests built on stable selectors and explicit waits, run in parallel, with retries quarantined and investigated rather than silently passed.
Automated checks at service boundaries, including schema validation, error handling and backwards compatibility for consumers.
Controlled, repeatable test data: fixtures, seeded states and synthetic data that respects privacy, instead of copies of production.
Page objects, shared setup and clear naming so tests are easy to read, extend and delete. Ownership is assigned, not assumed.
CI integration & quality gates
Automation earns its keep inside the delivery pipeline, where it runs on every change and blocks what should not ship.
Fast checks on every commit, broader suites on merge, and full regression before release, tuned so feedback arrives in minutes, not hours.
Agreed pass criteria, coverage of critical paths and security checks as merge conditions, with named owners who can override and a record when they do.
Test results, trends and flaky-test rates reported where the team works, so quality is a shared signal rather than a QA-side report.
Test environments managed as code where possible, so a green run means the software works, not that the environment was lucky.
AI testing & AI-assisted QA
AI changes QA in both directions: AI features in your product need systematic evaluation, and AI tools can speed up test work when engineers stay accountable for the result.
Scenario sets, scoring rubrics, adversarial inputs and regression suites for prompts, retrieval and agent behaviour, with results tracked over time.
Statistical pass thresholds, seeded runs and human review of borderline output, because AI features do not fail in binary ways.
Models help draft test cases, data and scaffolding from requirements and code. Engineers review everything; generated tests that do not verify real behaviour are removed.
No test data or code is sent to a model provider without your approval of provider, region and retention terms. Private or self-hosted options exist where policy requires.
Performance, security & non-functional checks
Systems pass every functional test and still fall over under load, leak through a misconfigured header or degrade slowly over months. Non-functional testing makes those visible.
Realistic traffic models against production-like environments, with capacity limits and bottlenecks reported as evidence, not estimates.
Dependency failures, timeouts, retries and partial outages exercised deliberately, so degradation behaviour is known in advance.
Dependency scanning, static analysis and baseline dynamic checks automated in CI, with deeper assessments for sensitive systems.
Automated checks plus manual review against WCAG criteria, so accessibility is verified rather than claimed.
Tools & frameworks
We work with the tools that fit your product and your team's skills. These are options, not a requirement to use everything on the list.
Cross-browser journey tests with stable selectors, traces and video on failure.
Fast checks close to the code, in the language the product uses.
Boundary behaviour and backwards compatibility verified automatically.
Load models, soak tests and failure injection with measured results.
Measured AI quality with regression tracking across releases.
Baseline security and accessibility checks automated in the pipeline.
Tests run on every change with quality gates and visible trends.
Results, flakiness and defect trends visible to the whole team.
Tool selection depends on your technology, team skills and licensing. We prefer fewer, well-maintained tools over a broad stack nobody owns.
Delivery approach
You work with engineers inside the delivery process. Reviews, demonstrations and scope decisions stay connected to the quality goal.
Review the product, current testing, release process and incident history to find where failures cost the most.
Output: Quality assessment and risk map with recommended coverage.
Agree what gets tested at which level, the quality gates, the environments and the ownership model.
Output: Test strategy and automation roadmap.
Implement the core suites, test data, CI integration and reporting for the highest-risk workflows first.
Output: Running automated suites in your pipeline with visible results.
Broaden coverage, remove flaky tests, add performance, security and AI evaluation where the risk justifies it.
Output: Trusted regression, non-functional suites and quality gates in force.
Transfer ownership to your team with documentation, pairing and maintenance practices that keep the suite healthy.
Output: Runbooks, ownership model and a team that maintains its own tests.
A decision-maker, knowledge of the critical workflows, access to code and environments, and the people who currently test, formally or not. For regulated workflows, bring compliance stakeholders into scoping early.
We show working suites early, keep flaky tests and coverage gaps visible and document tradeoffs. Quality-gate overrides have named owners; a deadline does not turn an untested risk into an accepted one silently.
Handover & ownership
Everything lives in your repositories and pipelines from day one. Handover includes the strategy and the maintenance practices, so the suite stays healthy after we leave.
Tests live in your repositories, run in your pipelines and are maintained by your engineers. Nothing depends on us being present.
The risk map, coverage decisions and quality gates are written down, so future test work follows a rationale rather than habit.
Flaky-test triage, review standards and pruning routines that keep the suite fast and trustworthy as the product grows.
Performance, AI quality and defect trends recorded as baselines, so future regressions are visible against evidence.
Delivery does not include unlimited maintenance or a service-level commitment. We can agree ongoing quality engineering support, or hand over fully to your own team.
Related engineering services
Quality work often connects to new builds, AI features or dedicated engineering capacity.
Questions before starting
The first high-risk workflows are usually automated within the initial two-week sprint and start catching issues immediately. A broader, trusted regression suite typically takes a few months of incremental work alongside feature delivery. We sequence by risk so value arrives early, not at the end.
Yes, and it is a common starting point. We triage the suite: fix what is fixable, quarantine what is not, delete what proves nothing and rebuild the unreliable parts on stable foundations. The goal is a suite the team trusts enough to act on.
No. Automation takes over the repetitive regression checking; skilled testers focus on exploration, usability and the edge cases automation misses. Most teams end up valuing their testers more, not less.
Yes. We build scenario sets, scoring rubrics and adversarial cases for prompts, retrieval and agent behaviour, tracked over time so regressions are caught before release. AI quality is measured as a trend, not asserted from a few manual prompts.
It depends on your stack and team. Playwright covers most modern web end-to-end needs; unit and API tests stay in the language of the product. We choose tools your team can maintain, and we do not introduce a framework for its own sake.
We use fixtures, seeded states and synthetic data rather than copies of production wherever possible. Where realistic data is essential, it is anonymised and access-controlled, and the approach is agreed with your security or privacy stakeholders.
Done well it speeds them up: fast checks run in minutes on every change and catch problems while the context is fresh. Suites that take hours or fail randomly are a design problem, and fixing that is part of the work.
Your team owns everything: the suites, the pipelines, the strategy document and the maintenance practices. We pair with your engineers throughout so the knowledge transfers with the code, and we can stay available for review or further phases if useful.
Start with the risk
Share where defects hurt most and how you release today. We will agree a practical first sprint.