Speed up QA cycles, reduce test maintenance and expand coverage with AI-powered automation. Tests are generated from structured specifications, run continuously in CI and reviewed against clear quality standards.
Trusted on systems that cannot fail
Certified, accredited and independently reviewed
AI delivers the most value when testing can no longer keep pace with development. These five signs suggest that manual effort and traditional automation are slowing delivery. If AI-powered automation will not deliver enough value for your project, we will say so.
Testing takes longer than the feature work itself. Smarter test selection runs the checks that matter most, giving developers faster feedback without sacrificing essential coverage.
Regression suites that take hours delay releases and surface defects too late. AI helps prioritise high-risk tests so teams can identify problems sooner.
Expanding test coverage should not require the QA team to grow at the same rate. AI-assisted test generation helps the suite keep pace with the product.
Frequent interface and logic changes can break brittle tests. Self-healing controls and more stable test patterns reduce routine repairs and keep the suite reliable.
Manual test design can overlook edge cases and high-risk user journeys. AI helps identify missing scenarios before those gaps become production defects.
One accountable team supports the full lifecycle, from deciding what to automate to keeping the test suite reliable in CI.
Turn structured requirements into reviewable automated tests. Each acceptance criterion is linked to the behaviour it verifies, so coverage remains aligned with the product as requirements change.
order-limit.spec.md
Then the order is held for approval
order-limit.spec.ts, generated
expect(status).toHaveText("Held for approval")
Run the right tests on every pull request. The pipeline selects the checks affected by each change and blocks the merge when a required test fails.
pull_request
$ npx playwright test --only-changed
Create a risk-based plan for what to automate, where each test should run and what should remain manual. Effort stays focused on the failures that would have the greatest business impact.
test-strategy.md
Automate the user journeys and system interactions that carry the most risk. Tests run against controlled environments, while external services such as payment providers use secure sandbox integrations.
checkout.e2e.ts
Browser → Storefront → Orders API → Payments
orders.integration.ts
Orders API → Database → ERP
Stable selectors and shared patterns keep the suite dependable as the product evolves. When a test becomes unstable, the pipeline isolates it, records the evidence and prevents it from weakening trust in the results.
flake-policy.md
Map coverage to business requirements rather than only to lines of code. Each requirement shows the tests that verify it, making gaps visible before release.
coverage by requirement
Six practical outcomes are measured and reported from the first sprint.
Shorter regression runs give developers faster feedback and prevent validated changes from being held back by slow legacy suites.
Self-healing controls, automated upkeep and faster triage reduce the engineering time spent repairing broken tests.
Critical workflows and high-risk edge cases are tested before release rather than discovered by users.
Coverage, stability and response metrics show whether the investment is improving software delivery.
Your engineers learn the framework and retain ownership after handover. Knowledge stays within your team instead of remaining with an external supplier.
Tests adapt to controlled interface changes without hiding genuine failures. The suite remains useful without constant manual repair.
Four measures reveal whether the suite is fast, stable and trustworthy. Reporting begins in the first sprint.
passed / executed
Shows how many tests pass and classifies failures as product defects, broken scripts or unstable environments.
passed on retry / executed
Tracks tests that pass only after a retry. This exposes false alarms that waste engineering time and reduce trust in the suite.
time to results
gate decision - commit
Measures the total time between a code commit and the final quality-gate decision.
time to assign
mean(assigned - failed)
Measures how quickly a failed test is analysed and assigned to the right owner.
AI agents generate, run and maintain test coverage. Senior engineers define the standards, review the evidence and control each release gate.
Our Agent-Centric Development Cycle turns AI speed into a testing process that remains structured, traceable and accountable as the product changes.
Agents review the specifications, source code and relevant support history before generating tests. This helps them test the intended behaviour, not simply the current implementation.
Self-healing controls adapt tests to approved product changes. Routine breakage is reduced without masking genuine defects.
Every relevant pull request is checked. New coverage gaps are identified and addressed as the product evolves.
Each failed regression includes reproducible evidence and an initial root-cause analysis, helping engineers move directly to diagnosis.
Tests run automatically across the required changes and environments. Senior engineers control the quality gates and approve any exception.
We choose tools that fit your systems, delivery process and internal capabilities. The resulting stack remains practical to operate, extend and maintain after handover.
Playwright 
Cypress 
Selenium 
Detox 
Jest 
Vitest 
JUnit 
xUnit 
pytest 
REST Assured 
Pact 
k6 
Postman 
GitHub Actions 
GitLab CI 
Azure DevOps 
Jenkins A transport organisation in Japan needed to detect motor faults while equipment was still running. Scheduled inspections could not provide continuous coverage across changing operating loads.
We built an AI anomaly-detection system that monitors three-axis vibration and workload data in real time. When a fault pattern appears, the system raises an automated alert, helping the organisation reduce manual inspections.
Every engagement uses our Agent-Centric SDLC and includes senior review at each quality gate. We begin with a fixed-price assessment and finish with a written implementation plan.
The plan remains yours, even if you decide not to continue.
Identify what to automate first, where each test should run and how success will be measured.
Build reliable automated coverage around the workflows and integrations that carry the greatest risk.
Keep coverage, tooling and quality controls current as the product continues to evolve.
The two services solve different problems. Test automation creates a capability your team can operate, while independent QA provides separate oversight of release quality.
Many enterprise teams benefit from both. Automation runs continuously within delivery, while independent QA provides an additional release gate. If the right starting point is unclear, the initial assessment will identify it.
AI test automation Internal ownership. Your team owns and operates the automation capability.
01
Independent QA and testing Independent oversight. A separate team assesses whether the release is ready.
AI test automation Faster feedback. You need broader coverage or faster CI rather than a separate approval process.
02
Independent QA and testing Traceable evidence. Buyers, regulators or auditors need clear proof of what was tested.
AI test automation Defined requirements. Requirements already exist and can be converted into automated tests.
03
Independent QA and testing Human judgement. The work requires evaluation that extends beyond automated checks.
AI test automation Long-term capability. Your engineers maintain and extend the suite after handover.
04
Independent QA and testing Release authority. Someone outside the development team controls the final release gate.
Something not covered here? Ask an engineer directly, no SDRs, no funnel.
It generates them from structured specs, and engineers review them. A test nobody read is not a test.
Yes. Where there are no specs we start with characterization tests, then move to spec-derived coverage as areas get worked on.
Stable selectors, hermetic test data, a flake budget and a quarantine policy. Flaky tests get fixed or removed, never ignored.
Usually a pilot suite in two to four weeks, and a suite the team trusts within a quarter.
Yes, with a maintenance playbook and pairing so your engineers own it.
Tell us what you're building. Our engineers will respond within 1 business day with a concrete next step - no sales script, no obligation.
