QA & Release Assurance
Automated Testing
Automated tests that catch regressions before they reach your users. We build unit, API and end-to-end suites that run in your CI pipeline, and keep them reliable as your product changes.
Teams that ask us to automate
Every release needs the same checks, but running them by hand is slow and gets skipped under deadline pressure. Meanwhile a suite that fails at random teaches the team to ignore red builds.
- Teams that release often but still click through regression checks by hand
- Engineering leads whose CI suite is slow, flaky or re-run until green
- Teams using AI coding agents whose pull requests outpace manual testing
Which suites run when
Typical schedule for a web product; the split depends on how fast your pipeline must stay.
| Every pull request | Nightly | Before release | After deploy | |
|---|---|---|---|---|
| Unit tests | Runs and gates the step | Not run at this point | Runs and gates the step | Not run at this point |
| API and contract tests | Runs and gates the step | Runs and gates the step | Runs and gates the step | Not run at this point |
| End-to-end smoke | Runs and gates the step | Runs and gates the step | Runs and gates the step | Runs and gates the step |
| Full end-to-end regression | Subset, or reports only | Runs and gates the step | Runs and gates the step | Not run at this point |
| Visual comparison | Subset, or reports only | Not run at this point | Runs and gates the step | Not run at this point |
| Third-party sandbox checks | Not run at this point | Runs and gates the step | Subset, or reports only | Not run at this point |
- Runs and gates the step
- Subset, or reports only
- Not run at this point
Automation that catches regressions, not noise
A test suite only helps if your team trusts it. Slow, flaky or shallow tests get ignored, and regressions slip through. For teams that release often or still rely on manual checks, we design automation around your requirements and riskiest flows, then build unit, API and end-to-end tests that run in your CI pipeline. This matters more when code is written with AI tools: changes arrive faster, and generated tests can simply confirm what the code does, not what it should do.
AI-assisted test writing, reviewed by engineers
How AI assists
- Drafts unit, API and end-to-end tests from requirements, API specs and existing code, for engineers to review.
- Compares coverage reports with your critical flows and recent changes to show where tests are missing.
- Investigates flaky and failing tests using run history, logs and traces, and suggests likely causes.
- Updates selectors, fixtures and test data when the interface or API changes, as reviewed pull requests.
What our experts own
- Engineers decide what belongs in fast unit tests and what needs integration or end-to-end coverage.
- Every generated test is checked for meaningful assertions; tests that only mirror the code are rewritten.
- QA engineers decide which checks block a merge or release and which only report.
- Flaky tests are fixed or quarantined deliberately, not retried until they happen to pass.
What you receive
A test suite that runs in your pipeline
Automation strategy
What to automate, at which level and with which tools, such as Jest, Vitest, pytest, Playwright or Cypress, chosen to fit your stack and team.
Unit and integration tests
Fast checks for business rules, data access and service boundaries, with test data and mocks your developers can maintain.
API and contract tests
Requests, responses, errors and permissions checked against your API specification, including your contracts with third-party services.
End-to-end journey tests
Browser tests for sign-up, checkout, roles and other critical flows, with screenshots, traces and visual comparison where layout matters.
CI pipeline integration
Suites run on pull requests and before releases in GitHub Actions, GitLab CI or your current pipeline, with clear pass and fail gates.
Test health reporting
Coverage of critical flows, flaky-test tracking and failure trends, so you can see whether the suite deserves your team's trust.
How we build your test suite
- 01
Audit current testing
Review existing tests, CI setup, coverage and failure history, and identify the flows where a regression would hurt most.
- 02
Agree the approach
Choose test levels, tools and gates, agree conventions, and set up test data and environments your team can reuse.
- 03
Build critical coverage
AI drafts tests and engineers review and refine them. Critical paths come first, then coverage grows by risk.
- 04
Run, report, maintain
Suites run in CI with reporting. We hand them over with documentation or keep them current in ongoing QA.
Two ways to work with AI tools
AI helps draft tests and investigate defects. Choose where it may process your code and test data.
- Private / Local AI Engineering
Privately hosted models inside infrastructure you control or an agreed isolated environment.
Discuss with this package - Claude Code / OpenAI Codex Engineering
Claude Code and/or OpenAI Codex with cloud settings your organization approves.
Discuss with this package
Not sure? We'll recommend one during scoping. Compare AI delivery options
Typical automation requests
Typical scenarios we scope, not client case studies.
A flaky suite nobody trusts
A team's browser tests pass on one run and fail on the next. We separate shared test data and timing problems from real defects, fix the tests at the source and agree which checks may block a merge.
Manual regression days before each release
A product team spends release days clicking through the same flows. We automate the critical paths at the lowest reliable level — unit or API first, browser only where the journey needs it — and run them as release gates.
Gates for agent-written pull requests
A team lets an AI coding agent open pull requests. We add required checks so agent changes face the same unit, contract and smoke tests as human ones, and a person still approves each merge.
What automation work doesn't include
- Exploratory testing and the release-risk recommendation come with Software QA & Testing; this service builds and maintains the automated checks.
- Setting up or migrating the CI platform itself is DevOps & CI/CD work; we plug suites into the pipeline you already run.
- Load and endurance runs need their own tooling and environments — see Performance Testing.
- Scoring LLM answers against evaluation datasets is a different discipline — see AI Evaluation & Testing.
How automation fits design, QA and operations
Design: states worth testing
Empty, loading, error and permission-denied states from the design become explicit test cases, so they keep working as screens change.
QA: automation plus exploration
Automation handles repeatable checks, so QA specialists can spend their time exploring new features. Each confirmed defect gets its own regression test.
Operations: a clear release signal
Suites run in the deployment pipeline before release and smoke tests run after it, giving DevOps a clear signal to proceed or roll back.
Ongoing: suites kept current
We update tests as features change, retire obsolete ones and track flakiness, as part of an ongoing QA plan agreed with you.
FAQ
Frequently asked questions
Which testing framework should we use?
Usually the one that fits your stack and team: Jest or Vitest for JavaScript and TypeScript, pytest for Python, Playwright or Cypress for browser tests. We keep tools that already work for you and change them only when there's a clear reason.
How much test coverage do we need?
A coverage figure on its own is a weak target. We start with critical flows, business rules and past defects, and report how well those are covered, not just lines of code. A high score with weak assertions protects very little.
Can you add tests to legacy or AI-generated code?
Yes. We start with characterization tests that record current behavior, compare that behavior with your requirements and flag differences as defects or open questions. Refactoring then happens with tests in place. The approach is the same whether the code was written by people or generated with AI tools.
Do AI tools see our source code while writing tests?
Only within the boundary you choose. With Private / Local AI Engineering, models run on infrastructure you control or in an isolated environment we agree with you. With Claude Code / OpenAI Codex Engineering, commercial providers process code under account, data-handling and retention terms agreed before work starts.
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Build a test suite your team trusts
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