AI-Assisted Quality Assurance
QA spend that buys faster releases, not more testers.
A standalone QA engagement for retail and commerce systems. AI scaffolds and maintains the test suite, senior QA engineers own the coverage, and everything runs in your CI — so a regression fails a PR, not a launch.
What an engagement covers
Coverage that runs where the code lives.
Every layer lands in your repo and your CI. The suite is a deliverable your team keeps, not a report that goes stale.
Functional & regression
Scaffolded from your real product flows, reviewed and hardened by a QA engineer, then run on every PR — so a regression fails the build, not the release.
E2E journeys
Playwright suites across the paths that carry revenue — browse, cart, checkout, account — on real browsers, not a happy-path smoke test.
API & integration
Contract tests at the seams where the storefront meets ERP, OMS, and payment providers. The seam is where commerce systems actually break.
Performance
Load profiles shaped from your traffic patterns, run before release — so scaling surprises happen in staging, not on launch day.
Accessibility
WCAG 2.2 AA checks wired into the same pipeline as the functional suite. Accessibility regressions get caught the same way any regression does.
In CI, on every diff
Impacted-test selection runs only what a PR touches. A developer learns about a break in minutes, while the change is still in their head.
The economics
Where the QA budget stops leaking.
Traditional QA budgets go to writing tests, then to repairing them when the UI changes. AI-assisted QA moves those hours to where a human is actually needed.
Authoring becomes review
An LLM scaffolds Playwright tests from product flows and recorded sessions. The QA engineer reviews and hardens instead of typing every test from a blank file.
Maintenance stops eating the budget
Self-healing selectors survive copy changes and DOM shifts. The suite that traditionally rots in 6 months keeps passing for the right reasons.
Feedback arrives with the PR
Tests run per diff, not per release. Bugs get fixed while the author still has context, which is the cheapest moment a bug will ever have.
Engineers do engineer work
Humans design the coverage model, review generated tests, and run the exploratory sessions machines cannot. Nobody hand-executes a 400-step regression script.
QA in the era of AI
The test suite stopped being the bottleneck.
AI changed what a QA engagement is. Suites that wrote themselves into a maintenance burden are being replaced by suites that repair themselves — and the QA engineer's job moves from writing steps to defining what correct means.
Enterprise QA platforms — Perforce partnership
Destm is a Perforce partner: our QA engineers deliver on Perforce's enterprise testing stack — Perfecto for web and mobile device-cloud testing, BlazeMeter for performance — for clients who run on those platforms, alongside our own Playwright and Vitest work.
What the AI actually does
Generates candidate tests from specs and session traces, heals selectors when the UI shifts, and picks the impacted subset to run per commit. Humans still own the definition of correct — that part does not automate.
How an engagement runs
From audit to a suite your team keeps.
Week 1 — Coverage audit
Map the critical flows, the existing tests, and the CI setup. Pick the paths where a failed release costs the most and baseline what covers them today.
Weeks 2–4 — Suite build
Playwright E2E and Vitest suites scaffolded from your flows, reviewed by QA engineers, and wired into your CI. The suite lives in your repo from day one.
Ongoing — Hold the line
New flows get covered the sprint they ship. Selectors self-heal, humans run exploratory passes, and we either run the suite for you or hand it over documented.
Tooling
The house QA stack: Playwright + Vitest.
2 tools, deliberately. A small stack your engineers already know beats a proprietary platform they have to learn — and the AI layer generates against it, so nothing here is exotic.
Playwright
E2E on real browsers
Journey tests across checkout, cart, and account flows. LLM-scaffolded, engineer-reviewed, self-healing selectors.
Vitest
Unit & component
Fast unit and component coverage next to the code it tests, in the same CI run as the E2E suite.
Questions
Questions buyers ask before booking QA
- Is this manual-testing outsourcing?
- No. The deliverable is an automated suite running in your CI, plus the QA engineers who designed it. Humans do the exploratory testing and release judgment; nobody hand-executes scripted test cases.
- What does the AI actually do?
- It scaffolds Playwright tests from product flows and recorded sessions, proposes selector fixes when the DOM shifts, and picks which tests a given diff needs to run. A QA engineer reviews everything before it merges.
- Do we have to adopt your stack?
- The suites are plain Playwright and Vitest in your repository, running in your CI — GitHub Actions, GitLab, Jenkins, whatever you already run. End the engagement and the tests keep running.
- Who owns the tests?
- You do. They live in your repo, your engineers can read and edit them, and the generation workflow is documented as part of handover.
- Can this replace our manual regression cycle?
- Most of it. Repeatable regression belongs in the automated suite; the release-blocking manual cycle shrinks to exploratory passes and the judgment calls that need a human.
How an engagement starts
Three steps to a partnership
Intake call
30 minutes. We listen, you talk. No deck.
Diagnostic
We audit the surface, name the bottleneck, propose a path.
Kickoff
Senior engineer in your standup by week two.
Put your next release behind a suite.
Tell us which flows keep you up before a launch. We start with a 1-week coverage audit and a suite proposal you can hold us to.