Software testing & LLM evaluation.
QA and software testing for business-critical apps: automated tests, performance and reliability work, plus evals and red-teaming that prove your AI features are accurate and safe before users see them.
QA that ships, not blocks
Quality is a delivery practice, not a gate. Tests live next to the code and run fast, so they speed releases up. For AI features, evals run on every change, just like unit tests.
What our QA & testing team delivers
Classic QA and software testing, plus evaluation for AI systems whose output changes from run to run. You get one clear release-readiness signal for both.
- ✓LLM evaluation suites & golden datasets
- ✓AI red-teaming: jailbreaks, prompt injection, data leaks
- ✓End-to-end automated test suites
- ✓Component & unit test scaffolding
- ✓Regression & smoke testing
- ✓Performance & load testing
- ✓Manual & exploratory testing
- ✓Accessibility testing
- ✓CI/CD test pipeline integration
- ✓Test plans & release-readiness sign-off
Tests that get faster, not slower
We treat the test suite as a product: instrumented, monitored and trimmed. Flaky tests get fixed, not retried.
Audit
Test suite, CI, release process, bug history and AI failure modes. Where does quality actually break?
Plan
A prioritized test and eval strategy with measurable goals: coverage, speed, flake rate, eval scores.
Build
E2E, integration and eval foundations wired into CI, with dashboards. Checks run on every PR.
Operate
Ongoing test maintenance, eval re-runs, release sign-off, and on-call support during launches.
Three ways into QA & software testing
Cost depends on the size of your app, the test coverage you have today, and how many AI features need evals. An audit shows where to invest first.
QA audit
An audit of your test suite, CI, release process and AI features, with prioritized fixes.
- Test-suite analysis
- CI cost & speed audit
- Flake report
- Coverage map
- Roadmap
Test infrastructure
A clean E2E, integration and LLM eval foundation with CI integration and reporting dashboards.
- E2E foundation
- LLM eval harness
- Coverage dashboards
- Flake monitoring
- Team training
Embedded QA
A QA engineer or pod working alongside your team. Continuous test work and release sign-off.
- 1–2 QA engineers
- Test plans per release
- Release sign-off
- Bug triage support
- Quarterly OKRs
Testing & LLM evals, answered
What does a QA and testing engagement cost?
It depends on the number of apps and critical flows, the state of your test suite and CI, and how many AI features need evaluation. The QA audit ends with a prioritized roadmap, so you fund the fixes with the biggest release-speed payoff first.
How is LLM evaluation different from classic QA?
AI output changes from run to run, so pass/fail assertions are not enough. We build golden datasets and scored eval suites, red-team for jailbreaks, prompt injection and data leaks, and run evals on every change, just like unit tests.
Will more tests slow our releases down?
Not when the suite is treated as a product. We instrument it, fix flaky tests instead of retrying them, and trim slow ones, so checks run on every PR and the suite gets faster over time.
Do you work with our existing test tools?
Yes. We work in Playwright, Cypress, Jest, Pytest, k6 and Locust, wired into GitHub Actions or your current CI, with results in Datadog or Sentry. We extend what you have before adding anything new.
Not sure which engagement fits? Tell us what you’re building and we’ll reply within one business day with a recommended starting point.
Often paired with QA & evals
Most engagements blend two or three disciplines. These are the most common pairings.
AI integration
LLM apps, AI agents and RAG systems built into your product, with evals, guardrails and cost controls from day one.
Custom web apps
Fast, AI-ready web applications in React and Next.js, built to scale with traffic, data and your team.
AI-powered mobile apps
iOS and Android apps with AI features built in, from on-device ML to LLM-powered assistants.
Evals carried our LLM prior authorization case study, where every prediction and reviewer correction feeds a weekly eval review. Regulated teams building fintech AI solutions and healthcare AI solutions need the same discipline. More in our writing on LLM evals.
Ready to release with confidence?
We respond within one business day, with a real human and a real opinion.