Quality engineering, software testing automation and verification

Service Capability

Quality Engineering

Test automation strategy, performance testing, API and contract testing, and quality engineering embedded in delivery — not bolted on at the end of a sprint.

What we do

Quality is an engineering concern, not a testing phase. When QA is a gate at the end of a sprint — a team that verifies what developers handed over — it becomes a bottleneck that slows delivery and surfaces defects too late for them to be cheap to fix.

We embed quality engineering within development cycles using a shift-left philosophy: writing tests as part of feature development, automating at the right level of the test pyramid, and making quality feedback fast enough to be genuinely useful during development rather than retrospectively after release.

Our quality engineering practice covers the full automation stack — unit to end-to-end, API testing, contract testing, performance baselines, and the CI integration that makes all of it run on every commit. We help teams build automation frameworks they can own and maintain, not Selenium grids that nobody wants to touch.

Problems we address

  • — Manual testing is the bottleneck that prevents teams from releasing more frequently
  • — Existing automated tests are flaky — they fail intermittently for reasons unrelated to code changes, eroding trust in the test suite
  • — API integration points have no automated test coverage — breaking changes are discovered in staging or production
  • — Performance characteristics under load are unknown until a production incident reveals them
  • — QA is siloed from development — testers receive builds at the end of the sprint with no involvement in requirement or design discussions
  • — Test data management is chaotic — tests depend on specific database states that are difficult to set up and maintain

Capabilities

Test Strategy & Automation Architecture

End-to-end test strategy aligned to the test pyramid: which tests to automate, at which layer, and with which tools. Framework architecture designed for maintainability, parallelism, and CI integration.

UI & End-to-End Automation

Reliable browser automation using Playwright or Selenium with Page Object Model patterns, parallel execution, visual regression testing, and deterministic test data management to eliminate flakiness.

API Test Automation

Comprehensive REST and SOAP API test suites using REST Assured or Karate, covering functional correctness, error handling, schema validation, and negative paths. Integrated into CI pipelines for continuous feedback.

Contract Testing

Consumer-driven contract testing with Pact to ensure API producer changes don't break consumers without either party needing to run integrated environments. Particularly valuable in microservices architectures with multiple teams.

Performance & Load Testing

Scripted load tests using k6, JMeter, or Gatling with defined performance baselines, soak tests, and spike tests. Performance budgets agreed upfront, with automated baseline comparisons in CI to catch regressions before they reach production.

BDD, Mobile & Test Data Management

BDD test suites with Cucumber for business-readable scenarios. Mobile automation with Appium. Structured test data management strategies including factory patterns, database seeding, and service virtualisation to decouple tests from live dependencies.

Our approach

Testing as engineering, not a phase

Quality engineers on our engagements work in the same sprint as developers, contribute to design discussions, and write tests alongside features — not after they're built. This is not a semantic distinction. Tests written concurrently are better designed, better maintained, and catch more bugs at a lower cost to fix.

The test pyramid, taken seriously

Most codebases have too many slow, brittle end-to-end tests and too few fast, reliable unit and integration tests. We assess the existing test distribution and rebalance it — adding unit coverage where the business logic is, adding API tests for integration contracts, and reserving UI tests for critical user journeys that genuinely need a browser to validate.

Shift left on performance

Performance regressions are cheaper to find in a CI pipeline run against a representative load test than in a production incident. We baseline performance early, run automated performance checks in CI, and make performance a shared engineering concern rather than a pre-launch activity.

Frameworks your team can maintain

We don't build automation frameworks and then leave. We use standard, well-documented tools with clear patterns and invest in developer documentation and pair programming so your team can maintain, extend, and debug the framework confidently after the engagement ends.

Delivery process

  1. 1

    Test strategy & current state assessment

    Audit existing test coverage, identify gaps by risk area, assess test reliability and execution time. Recommend automation tooling based on your tech stack and team skills.

  2. 2

    Test pyramid design

    Define target test distribution: what proportion of coverage at unit, integration, API, and E2E layers. Align with risk assessment and business criticality to maximise confidence-per-second-of-CI-runtime.

  3. 3

    Automation framework build

    Implement the automation framework with patterns, shared utilities, test data management, and reporting. Pair with your team throughout to transfer knowledge.

  4. 4

    CI pipeline integration

    Integrate tests into CI with appropriate execution strategies — fast feedback in pull request pipelines, full suite on merge. Performance baseline runs on schedule with regression alerting.

  5. 5

    Quality metrics & ongoing improvement

    Track test coverage, flakiness rate, mean time to detect, and CI feedback time. Continuous improvement backlog to maintain framework health as the codebase evolves.

Technologies

UI & E2E Automation

PlaywrightSeleniumCypress

API Testing

REST AssuredKaratePostman/Newman

Contract Testing

PactSpring Cloud Contract

Performance Testing

k6JMeterGatling

Unit & Integration Testing

JUnit 5TestNGJestMockitoTestcontainers

BDD & Reporting

CucumberAllure ReportsAppium

Industries served

Financial Services Healthcare Retail Manufacturing Technology Logistics

Related case studies

Financial Services

Banking Platform Modernization

Quality engineering programme embedded across six delivery squads — including Pact contract testing, Playwright E2E suites, and Gatling performance baselines run on every merge to main.

 Retail

Retail Cloud Migration

Load testing programme for a re-architected order management system, validating 11× peak traffic capacity before the peak trading season with automated k6 performance regression gates in CI.

Frequently asked questions

Flaky tests usually fail for one of three reasons: timing dependencies (tests that rely on sleep statements or assume network calls complete in a fixed time), shared mutable state (tests that depend on data set up or modified by other tests), or brittle selectors (UI tests that use fragile CSS selectors or XPaths that break on cosmetic changes). We audit the existing suite, categorise flakiness by root cause, and remediate systematically — fixing the root cause rather than adding retries. We also recommend quarantine policies so flaky tests don't block CI while they're being fixed.
Coverage percentage is a proxy metric, not a goal. 80% line coverage with tests that assert nothing meaningful provides less protection than 60% coverage with well-designed tests that validate business behaviour. We focus on risk-based coverage: what are the consequences of a defect in this code, and are those paths tested? We target high coverage for business-critical logic, financial calculations, and security-related code, and accept lower coverage for UI glue code and configuration. We treat declining coverage as a signal to investigate, not an absolute mandate to chase a percentage.
For new automation frameworks, we recommend Playwright in almost all cases. It has built-in auto-waiting that eliminates most timing flakiness, better cross-browser support, a faster execution engine, and a more modern API. Selenium remains relevant for organisations with large existing Selenium codebases where migration cost outweighs the benefits of switching. If you're starting fresh or rebuilding a framework from scratch, Playwright is the right choice unless there's a specific constraint (e.g., testing against an embedded browser control that only Selenium supports).
Contract testing verifies that a service's API behaves according to the contract that its consumers depend on — without both systems needing to run simultaneously. A consumer defines the interactions it expects, and the provider verifies it can satisfy those interactions against its own tests. This is most valuable in microservices architectures where multiple teams develop independently and integration testing across all services simultaneously is slow or logistically difficult. It's also valuable when a consumer team wants to detect breaking changes in a provider before deploying to a shared environment.
We start by identifying the key user journeys and API endpoints that are performance-sensitive — typically the ones with the highest traffic volume or lowest latency tolerance. We then build load scripts that simulate realistic concurrency levels using production traffic logs as input, run them against a production-representative environment, and record the baseline latencies and error rates at current load levels. This baseline becomes the reference point for all subsequent performance testing, allowing us to detect regressions automatically rather than discovering them as production incidents.

Build quality into your delivery process

Whether you need to rescue a flaky test suite, build an automation framework from scratch, or establish performance baselines, our quality engineering team can help.

Talk to our QE team View case studies