QA Shield™

Playwright automation

We standardize on Playwright over older tools like Selenium, mostly because it holds up noticeably better when tests are being drafted or maintained with AI assistance. We don't chase full coverage. We prioritize “major paths”, the flows where a bug would actually cost you serious money or trust. A smaller suite that's stable and trusted beats a huge one nobody believes enough to act on.

Spec-driven agentic workflows

Every test ties back to a written requirement, not someone's memory of what a feature was supposed to do. We use agentic workflows to speed up the work like drafting test cases, generating variations, flagging discrepancies between spec and code, but a person reviews and signs off before anything ships. That keeps the process fast without letting an agent quietly redefine what “correct” means.

Requirement and test management

Our traceability matrix is a living map of your features, it shows which requirement is covered by which test, what that test's last result was, what's the coverage for each feature etc. It sounds bureaucratic, but it's the difference between “we tested this” and “we can prove it.” We run this in our own proprietary tooling by default, but if you're already using something else, we can work inside that instead.

Performance testing, with a tool built for AI-era code

We built an in-house tracker that logs API response times over time and uses statistical and Machine Learning methods to flag anomalies and slow drifts early. We built it because of a pattern we kept noticing. AI-generated code is often correct on day one, it passes the tests, returns the right answer, but it's not optimal. This won't show up as a bug. It accumulates and shows up weeks or months later as your API getting noticeably slower under normal load, and by then it's hard to trace back to the change that caused it. Our API Response Time Analyzer catches it while it's still one commit.

Security testing

Once-a-year security audits don't work for AI-written code. 45% of AI-generated code contains at least one exploitable security weakness (Veracode, GenAI Code Security Report), often without the developer noticing, because the code looks fine and runs fine in testing. We test for the standard categories, injection, broken access control, exposed secrets, the OWASP Top 10, inside the same pipeline your code already runs through, on every release.

Real-time production monitoring

Testing before release catches what you thought to test for. Monitoring after release catches what you didn't. With AI-written code this is especially important due to the undeterministic nature of LLMs. We track error rates, latency, and failed transactions on your highest-risk flows in real time, on production. When something slips through, and eventually something always does, it's much better to find out right away from an alert, than later from a customer support ticket, a bad review or a lost client.

Compliant audit trails and documentation

Over the years, we've built a structured, high-governance workflow with minimal administrative overhead. As the work happens, every test run, requirement, and coverage decision gets logged. Quickly drafted into the right format by an LLM agent, modified and approved by a human. The result is quick and consistent documentation, thorough enough that when an auditor or an enterprise customer's security team asks for evidence, you just pull it.

Requirement Intake & Spec Approval

Raw project inputs (like product briefs or notes) enter through an ingestion engine. The pipeline automatically scans for missing details, validates the schema, and turns requirements into Gherkin syntax (Given/When/Then scenarios). Nothing moves forward until a human approves the requirement. Once approved, the Markdown spec becomes the single “source of truth,” and development tickets are automatically generated from it.

Development & Code Review

Developers build the feature using the approved spec and tickets for reference. Automated checks, like automated pull request reviews (via CodeRabbit) and static code analysis. It runs on every change before code is merged into a Pull Request.

Test Generation & Execution

The QA phase reads directly from the approved spec and tickets:

• Pre-check & Test Planning: The system performs a requirement check against the code and generates a dynamic testplan.md.
• Test Creation: An automated agent builds executable test cases based on the plan.
• Test Runs: The system executes the automated Playwright test suite while allowing room for on-demand exploratory testing by human testers.

Failure Analysis & Feedback Loop

If tests fail, a TestAnalyzer agent steps in to triage the root cause:

• Code Defect: If the code broke, the issue loops directly back to Development to be fixed.
• Spec Defect: If the failure occurred because the original requirements were incomplete or inaccurate, it loops all the way back to Intake for re-analysis and spec updates.