Plans and acceptance
Risk-based test plans, missing-scenario reviews, traceable acceptance criteria and synthetic test data.
Evidence before confidenceWrite better tests.
Write better tests.
Make releases easier to judge.
AI can propose scenarios, generate synthetic test data and explain failures. The evidence comes from executing the tests, reviewing the results and checking the journeys that matter to the business.
A large number of tests is not the same as useful coverage. Payment safety, access controls, data correctness and recovery paths need deliberate cases and independent review.
Discuss this service →What we can design and deliverA defined scope.
A defined scope.
A useful outcome.
Application and API tests
Unit, integration, API and regression checks built around meaningful behaviour and error paths.
Browser and mobile journeys
Responsive layouts, key user journeys, device/browser coverage and visual regression checks.
Performance and security
Authorised load profiles, bottleneck analysis, abuse cases, dependency checks and code review.
Data and migration verification
Reconciliation, completeness, transformation and recovery checks against agreed acceptance rules.
AI system evaluation
Scope evaluation sets for grounded answers, permission boundaries, prompt injection, tool actions and human handover.
A controlled workflow
Clear steps. Visible decisions.
- 01Agree the risks
- 02Design the cases
- 03Generate and review tests
- 04Run and inspect evidence
- 05Approve the release
Engineers review test intent and results. Security and load tests run only within authorised scope. Agree the environments, workload, acceptance thresholds and release decision before execution.
Your delivery pack
What you take away.
- Test strategy and critical-journey inventory
- Executable test suite and synthetic data
- Failure traces and reviewable reports
- CI integration and release checks
- Ownership and maintenance guide
Evidence from the pilot
What we measure.
- Critical acceptance criteria exercised
- Defects found before and after release
- Flaky tests and maintenance effort
- Performance against an agreed workload
Baseline, workload, scope and success criteria are agreed before the pilot. Results and savings are measured, not guaranteed.
Testing coverageChoose the tests
Choose the tests
that change the decision.
The engagement defines which testing types, environments and licences are in scope. The tools listed are options; we agree the right ones with you for each engagement.
| Testing area | Purpose | Tooling options |
|---|---|---|
| Test planning | Draft scenarios and acceptance checks from requirements | Jira / Azure DevOps, TestRail, Xray |
| Unit and integration | Propose tests around functions, services and boundaries | PHPUnit, Jest, pytest, JUnit, NUnit |
| Browser journeys | Exercise sign-in, basket, checkout and other agreed flows | Playwright, Selenium, Cypress |
| Mobile and responsive | Check device layouts and user journeys | Playwright emulation, Appium, BrowserStack, Sauce Labs |
| Visual regression | Compare approved layouts and investigate differences | Playwright screenshots, Applitools, Percy |
| API testing | Verify contract, errors, access and edge cases | Postman, REST Assured, Karate, Playwright API |
| Performance and load | Build an authorised workload and investigate bottlenecks | k6, JMeter, Gatling, Locust |
| Security testing | Review code, dependencies and authorised abuse cases | OWASP ZAP, Burp Suite, Snyk, SonarQube, Semgrep |
| Independent code review | Review change intent, bugs, payment paths and security | AI-assisted review and human engineering judgement |
| Synthetic test data | Create fictitious data and boundary cases | Faker, Mockaroo and reviewed AI-generated fixtures |
| Test maintenance | Assess changes and repair brittle selectors under review | Playwright; specialist tools such as Mabl or Testim where appropriate |
| Failure analysis | Use logs, traces and screenshots to propose a cause | Playwright traces and client-approved AI tools |
| Accessibility | Automate checks and combine them with manual review | axe-core, Lighthouse, WAVE |
| Database and migration | Reconcile source and target data against acceptance rules | SQL, dbt tests, Great Expectations |
| Regression in CI/CD | Run and retain agreed checks for each release | GitHub Actions, Azure DevOps, Jenkins, GitLab CI |
| AI system evaluation | Test grounding, permissions, unsafe actions and injection | Promptfoo, DeepEval, LangSmith or a client-specific evaluation set |
Works with your environment.
A starting stack may include Playwright, Selenium, k6, pytest or Jest, OWASP ZAP, SonarQube and GitHub Actions. Alternative tools are assessed against the application and existing licences.
View the technology capability map →Connected services
Build the right combination.
AI-assisted software development
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Explore the serviceAI vulnerability remediation
Connect prioritisation, ownership, change approval, remediation and verification into one accountable workflow.
Explore the serviceHow every product startsOne project or one application, measured in writing
1. ShadowThe AI works beside your team
2. Run approved tasksYour process owner checks the results
3. MeasureHours and cost saved, in writing
Let’s work together
Discuss your project Start with one useful outcome.
Let’s agree the scope, evidence and controls for a focused pilot.