QA and testing as a standalone engagement, or alongside one of our development teams. Functional, smoke, integration, performance, UI/UX and load - calibrated to the product and the release cadence.
QA and testing as a service, picked up either alongside one of our development teams or as an independent engagement on someone else’s codebase.
We see software at every stage – early prototypes, products under load, codebases years past a rewrite. The test plan is calibrated to where the product is, not to a generic template.
Most engagements combine manual and automated work. Manual exploratory testing for new features and edge cases; automation in CI for regression, smoke and core flows. We use Selenium, Cypress, Playwright, Appium and the platform-native frameworks (XCTest, Espresso, JUnit, pytest).
Bug reports come back actionable: a screenshot or video, the steps, the environment, the build, the priority. Developers should be able to reproduce without a follow-up call.
The team has seen most release cadences – weekly deploys with feature flags, monthly stable cuts, big-bang quarterly releases. The QA process bends to the cadence, not the other way around.
Testing covers the whole stack: API and contract tests, browser and mobile UI, performance under load, accessibility, basic security checks. The deliverable is a system the team trusts to ship.
Verifying that the product does what the requirements say it does, feature by feature. Test cases come from the spec where one exists, or from the actual flows where it doesn’t. The output is a coverage report against the agreed scope – what passes, what fails, and what was found that wasn’t in the spec at all.
A short, deliberately shallow pass over the core flows after every build. Smoke runs in minutes, not hours, and answers one question: is this build worth running the full suite on? Useful as a gate in CI and before every release.
Tests at the function and component level, written next to the code by the developers who own it. We set the coverage targets, the conventions and the CI gates; the developers write the cases. Catching bugs at this layer is cheaper than catching them later by an order of magnitude.
Checking that components and services talk to each other correctly – API contracts honored, error states handled, data shapes consistent across boundaries. Where unit tests can lie, integration tests usually can’t.
Measuring how the system behaves as load grows – response time, throughput, error rate, where the bottleneck moves. We use k6, JMeter or Gatling depending on the protocol, then walk the team through the chart and the call to action.
Real users on real devices – exploratory testing against the design system, accessibility checks (WCAG 2.1 AA), keyboard navigation, screen-reader passes. The output is a list of usability issues with screenshots and a suggested fix where one is obvious.
Sustained traffic, not just peaks. We simulate the realistic mix of users and requests over hours – or, when needed, days – to surface memory leaks, connection pool exhaustion, slow degradations that don’t show up in a five-minute performance run.
Send a short brief. A reply usually arrives within a working day with rough estimates and next steps.
