SQA and Quality Engineering
Test strategy, automation and release gates that catch defects before your users do, and keep catching them as the codebase grows.
- Playwright
- Selenium
- Cypress
- Locust
- JMeter
- Postman
- GitHub Actions
- Jenkins
What this covers
Test strategy and planning
Risk-based test plans mapped to your actual release cadence, so coverage goes where failure would cost the most rather than where tests are easiest to write.
Automation frameworks
Maintainable UI, API and end-to-end suites built on Playwright, Selenium or Cypress, structured so a failing test points at a cause instead of a symptom.
Performance and load testing
Locust and JMeter runs that find the concurrency at which your system degrades, and identify whether the ceiling is application code, database or infrastructure.
CI integration and release gates
Suites wired into your pipeline with quality gates that block a bad merge, plus flake quarantine so a noisy test never trains the team to ignore red builds.
Manual and exploratory testing
Structured exploratory sessions for the paths automation cannot reasonably cover, including accessibility and cross-browser verification.
What you receive
- Test plan and risk-prioritised coverage matrix
- Automated regression suite running in your CI
- Defect reports with reproduction steps and severity triage
- Performance baseline with documented breaking points
- Release readiness sign-off report
Quality is a property of the process, not a phase at the end
Most teams do not have a testing problem. They have a feedback-timing problem. Defects are found late, by the wrong people, in the wrong environment, and the cost of fixing them multiplies at every step they travel.
We work backwards from that. The question is not “how many tests do we have” but “how quickly does a broken assumption become visible to the person who broke it”.
How an engagement usually starts
We begin with an audit rather than a proposal. That means reading the existing suite, watching a real release go out, and measuring where time is actually lost. In most codebases the findings cluster in three places: flaky tests that have quietly trained the team to ignore failures, coverage concentrated on easy paths instead of risky ones, and a manual regression pass that grows every sprint until it becomes the release bottleneck.
You get the audit findings whether or not the engagement continues.
Automation that survives its author
An automated suite is a codebase. It has an architecture, it accumulates debt, and it can rot. Suites we build use the page-object or screenplay pattern, isolate test data setup, and avoid the sleep-and-hope timing that makes so many suites unreliable on slower CI machines.
The measure of success is not the count of tests. It is whether a developer trusts a red build enough to stop and read it.
Performance work is diagnosis, not just numbers
Load testing that reports a p95 latency without explaining the bottleneck is a number, not an answer. We run stepped concurrency against realistic traffic mixes, correlate against server-side signals rather than the load tool’s own tail, and identify whether the ceiling is uncached dynamic rendering, connection pool exhaustion, or a query that was fine at a thousand rows and is not fine at a million.
Tell us what you are building.
Send the problem, the constraints and the deadline. You get a considered reply from an engineer, not a sales sequence.