Software testing and QA for web apps, mobile apps and APIs

Traditional QA testing checks software after it has been built, while quality engineering builds quality into the whole delivery process. In practice that means agreeing acceptance criteria before development, designing features to be testable, automating regression checks in the CI pipeline and watching behaviour after release. Manual testing still matters, but it focuses on exploration and new features instead of repeating the same checks each release.

No. Every automated test has to be maintained, so automation should be kept for checks that are stable, valuable and run often. Exploratory testing, usability and visual judgement, one-off data migrations and features that still change every sprint are usually better tested by a person. We aim for a fast automated layer that catches regressions on every change, with manual time spent where human attention finds the most problems.

Start with the journeys that would cost the most if they broke and that you currently retest most often. A short risk review ranks features by business impact, change frequency and defect history, which usually puts payments, sign-in, permissions and data that feeds other systems first. API-level tests often come before UI tests because they run faster, fail less randomly and cost less to maintain.

We add test stages to the pipeline you already use, or set one up in GitHub Actions if you have none. Fast unit and API checks run on every pull request, broader end-to-end suites run before deployment to staging, and smoke tests run straight after each production release. Critical failures block the merge or deployment, and results are posted where your team already works.

We treat a flaky test as a defect, in either the test or the product, and fix its cause rather than rerunning until it passes. Common causes are timing assumptions, shared test data, order-dependent tests and unstable environments. Unreliable tests are quarantined so they do not block releases while being fixed, and we track flakiness over time so the suite stays trusted.

We usually run load and stress tests against a production-like environment rather than the live system. Traffic models are built from your analytics or expected peaks, and we measure response times, error rates and infrastructure use as load increases. Where testing in production is unavoidable, we agree a time window, limits and a stop condition with your team, and our DevOps engineers watch the system throughout.

No. Our security testing is part of everyday QA: we check that every feature enforces the access rules it should, and test sessions, password resets, file uploads and input handling as features are built. A penetration test is a separate, adversarial assessment by an independent specialist, worth commissioning before a major launch or when customers ask for one. We can help you prepare for it, fix the findings and retest them.

We test APIs directly, below the user interface, which makes the checks faster and more precise. Tests cover response schemas, status codes, validation, authentication, error handling and the data written to databases. For integrations with payment providers, CRMs or partner systems, we use their sandboxes and add contract tests, so a change on either side is caught before it reaches production.

We test on the browsers, operating systems and devices your analytics show customers actually use, rather than a fixed list. That usually means current versions of Chrome, Safari, Edge and Firefox, the iOS and Android versions that make up most of your traffic, smaller screens and slower connections where they matter. Automated suites run against the main browser engines in the pipeline, and manual sessions cover real phones and tablets.

We split the feature into what can be checked exactly and what needs judgement. The interface, hand-off to a person, permissions, rate limits and the data the assistant may read are covered by normal automated tests. Answer quality is scored against a set of real questions with agreed criteria for accuracy, tone and refusals, and rerun whenever a prompt, model or data source changes. We also confirm one user can never see another's data.

We prefer generated or anonymised test data to copies of production. Seed scripts create the accounts, orders and records each test needs, so tests are repeatable and never depend on shared data. Where realistic volumes are required, personal fields are masked before data leaves production. Test data handling is designed with UK GDPR in mind, and we follow your own data policies where they are stricter.

Setup time depends mainly on whether a CI pipeline, a stable test environment and usable test data already exist. Because we automate the highest-risk paths first instead of building a complete suite before anything runs, the first checks run in your pipeline early and coverage grows at each one-to-two-week review. Older applications with no test hooks take longer, because some refactoring for testability is usually needed first.

Cost depends on the size of the application, the number of platforms and integrations, how much test coverage already exists and whether you need a defined project or ongoing testing. We start with a short assessment of your product and release process, then give a fixed quote for the setup work and a clear monthly scope if a QA specialist stays embedded with your team.

Yes. We can work inside your existing team, and you own every test, script and report we produce. Test code lives in your repository next to the application, runs in your pipeline and goes through the same review process as product code. We document the strategy, data setup and how to add new tests, so your developers or QA staff can maintain the suite without us.