AI QA, integrated and run for your team
You bought the AI test, review, or triage tool. Wiring it into your real repo, CI, and review process — without creating faster, more scalable chaos — is the hard part. We do that part: integrate proven AI QA tools into your stack with the guardrails to run them safely, so they catch real defects and stay quiet on correct code. Assistive — your team approves every change.
Blind test on a real open-source project: 2 / 2 merged-then-reverted regressions caught · 1 / 1 correct change left alone · ~3 min each.
If it can't be trusted to catch real regressions, a human re-reviews everything anyway — and you're paying for a tool that changed nothing.
Floods every PR with low-value nitpicks. The team learns to ignore it, and the one real bug scrolls by with the noise.
A reviewer is only useful if it does both. We prove ours does — on your code, during a paid pilot.
The proof
We took excalidraw — a widely used open-source project with skilled maintainers — and found pull requests that were reviewed by a human, merged, and later reverted for a regression. Then we ran our reviewer on the original diff, blind, with no knowledge of the outcome.
| The change | What our reviewer said (blind) | Ground truth |
|---|---|---|
| Refactored image-cache invalidation | Top finding: the refactor silently broadened the cache filter from “new files” to “all files,” re-decoding every image on each add. Proposed the exact fix. | ✅ Caught Reverted for “flickering and slow performance.” |
| “Optimize” a drag handler | Major finding: the change forced synchronous re-renders every drag frame — the opposite of an optimization. Found a second issue too. | ✅ Caught Reverted to undo exactly that change. |
| 2-line resize-math fix | Derived the geometry independently, confirmed the change correct, declined to invent a defect — and flagged the one real gap: no test covered it. | ✅ Stayed quiet The code was correct. |
2 / 2 real regressions caught blind · 1 / 1 correct change left alone · ~2.5–4 min per review
Every finding came with a file:line citation, the reason, and a concrete fix — and it always defers the merge decision to a human. Small sample (n=3), one project — a capability demonstration. Results on your codebase are validated during the pilot.
Services
The boring, reliable engineering that makes AI QA work instead of adding noise — from picking the right tools to running them safely in your pipeline.
We assess the AI QA tools you already have or are considering — Qodo, CodeRabbit, SonarQube, TestSprite, Diffblue — and deliver a clear eval & selection matrix, so you invest in what fits your stack, not the loudest marketing.
We map your review, testing, and release process end-to-end, pinpoint where defects slip through and where reviewers drown, and hand you a prioritized plan to fix the bottlenecks.
We wire AI test generation, code review, and bug triage into your repo and CI/CD — with the human-approval gates, eval suites, and cost controls that make it safe to run on real code.
We run, monitor, and continuously tune the integrated tooling so it keeps catching real regressions and stays quiet on clean code — and you watch the value in a dashboard.
We get your engineers and reviewers fluent in the new workflow — how to read AI findings, when to trust or override them, and how to keep the guardrails healthy — so the tooling sticks after we hand it over.
How to engage
We orchestrate proven tools — selecting, integrating, hardening, and running them in your repo and CI — with the guardrails to keep it safe. Three ways to start:
1–2 weeks · fixed fee
We map your codebase, CI, and review pain, pick the right tools, and run the reviewer on a batch of your recent PRs so you see real findings on your code.
fixed scope
We wire the chosen QA tools into your repo and CI/CD, add the human-approval gate, cost controls, and a dashboard so you can see it working — then train your team.
monthly retainer
We run, monitor, and tune it every month. New findings keep coming; you watch the value in the dashboard. The reviewer never sleeps and never rubber-stamps.
Where does your code go?
Who it’s for
Product startups, scale-ups, and dev agencies shipping on GitHub or GitLab — where review has become the bottleneck, AI is writing more of the code, and a regression that slips past review is expensive. If you’ve already tried an AI review tool that didn’t stick, that’s exactly the gap we close.
FAQ
We integrate and run proven AI QA tools — code review, test generation, and bug triage — inside your repo, CI/CD, and review process, with the guardrails to run them safely. The result: they catch real defects and stay quiet on correct code. Assistive, not a replacement for your team.
Buying the tool is the easy part. Wiring it into your real repo, CI, and review process without creating faster, more scalable chaos is the hard part — and where most teams get stuck. We do that integration, plus the evals, approval gates, and reliability work that make it trustworthy.
Your code stays in your environment by default — your repo, your CI. The reviewer is read-and-comment only: it never merges, pushes, or modifies code, and a human approves every action. We don't keep, train on, or resell your code, and an NDA and contract are in place before any access.
Small and mid-size software teams — roughly 10 to 50 engineers — at product startups, scale-ups, and dev agencies shipping on GitHub or GitLab, where code review has become the bottleneck.
No. It is assistive: it recommends, reviews, and flags; a human approves and merges. It removes reviewer fatigue and catches regressions humans miss — it does not ship code on its own.
On a blind test against the open-source project excalidraw, our reviewer caught 2 of 2 regressions that maintainers had merged and later reverted, and stayed silent on a change that was actually correct — each review in about 3 minutes. Results on your codebase are validated during a paid pilot.
With a 1 to 2 week paid pilot: we wire the reviewer into your repo and run it on a batch of your recent PRs, so you see real findings on your own code before committing to anything bigger.
We’ll wire the reviewer into your repo and run it on your recent PRs in a 1–2 week pilot — real findings on your own code before you commit to anything bigger.