Quality Assurance

Ship confident.
Not just shipped.

No more 3AM bug calls. No more “it worked on our machines.” The client who finds a bug before you do doesn’t just file a ticket — they start asking questions about what else you missed. We run QA as a continuous layer throughout the build, not a gate at the end of a sprint. Every build covered. Every edge case hunted. Deploy on Friday without the weekend watch.

Schedule a Free Consult    See pricing →
4–5×
More coverage per dollar

Than a manual QA team of the same cost

Every
Build is tested

Automated suite runs on every commit, not just before release

0
“Works on our machines”

Production validation under real load before anything ships

How It Works

You’re not hiring a team to find bugs. You’re building a system that can’t miss them.

Manual QA processes take days, cover a fraction of the paths, and only run at the end of a sprint. AI-augmented QA runs the whole time, catching issues the moment they’re introduced, not after they’ve propagated through the codebase.

AI-generated test cases

We feed specs and implementation into AI tools that generate test cases covering happy paths, edge cases, and boundary conditions, in hours, not weeks.

Continuous regression

The full test suite runs on every commit. Issues surface the moment they’re introduced, not after three sprints of compounded breakage.

Adversarial manual QA

Senior QA engineers focus on what AI can’t replicate, real-world usage patterns, exploratory testing, adversarial edge cases, and production load simulation.

Production validation

We don’t sign off until the build runs clean under production-representative load. Staging environments mirror prod. No surprises on deploy day.

Two Layers, One Practice

AI and human QA aren’t the same thing. You need both.

AI-Augmented Layer

What AI handles

AI excels at coverage, repetition, and speed. Things that would take a QA team weeks to write and maintain, AI generates and keeps current automatically.

  • Unit and integration test generation from specs
  • Regression suite that runs on every commit
  • API contract validation
  • Data model boundary testing
  • Performance benchmarking baselines
  • Test coverage gap analysis
Senior Manual QA Layer

What humans handle

Senior QA engineers focus on what AI gets wrong, understanding how real users behave, finding the paths no spec described, and breaking things intentionally before production does it for you.

  • Exploratory and adversarial testing
  • Real-user workflow simulation
  • Cross-browser and cross-device validation
  • Edge cases that aren’t in the specs
  • Load and stress testing under real conditions
  • Sign-off before every production deploy

The Real Numbers

What you get per dollar in each model

Traditional Manual QA
AI-Augmented QA
Test case generation
Days to weeks, manually
Hours, AI-generated
When tests run
End of sprint, manually
Every commit, automated
Coverage %
Partial, what they had time for
Comprehensive, AI fills gaps
Regression on refactor
Manual re-test cycle
Automatic, instant
Cost per test maintained
High, manual upkeep
Low, AI keeps tests current
Coverage per dollar
Baseline
4–5× more

Let’s Talk

Tell us what’s shipping without enough coverage.

Most engagements start with a single conversation. Tell us what’s broken, what’s slowing you down, or what you’re trying to build. We’ll give you a straight answer, no pitch deck, no fluff. If we’re a fit, great. If not, we’ll tell you that too.

Start a Conversation →