how we build
AI makes a strong engineering process faster and a weak one worse. So most of our effort goes into the process: guardrails, reviews and approvals that let agents move fast without anyone losing control.
the release path
What to build and the checks that prove it is finished.
Coding agents implement in an isolated branch and run the tests.
Hooks, CI, tests and types catch every failure class we have seen before.
An independent model reviews read-only. Findings are verified, not obeyed.
A person signs off. Production needs a single-use approval for that exact commit.
Deployed, monitored, and recorded with who approved it.
Anything that slips through comes back as a new automated check at stage 03.
mistakes become checks
The first time something goes wrong we fix the root cause and write it down. If it happens again, it moves down the ladder until a machine catches it, and the written rule is deleted. Pick a rung to see one example.
First time: an API change ships without updating the client SDK. Root-cause fix, plus a written lesson so the agents know.
Second time: a hook warns the agent the moment an API file changes without the SDK being regenerated.
CI regenerates the SDK types and fails the build if they drift from the API. The written lesson is deleted.
A test exercises the real API shape the client depends on, so a mismatch fails before review.
Strongest: the client is generated from the API contract, so the mistake cannot even be written.
what you can count on
01 · mistakes become checks
Every failure gets a root-cause fix and a written lesson. If the same kind of failure happens again, it moves down a ladder until a machine catches it: a hook, a CI check, a test, or a compile error. The prose rule it replaces is deleted.
for youFewer regressions, and the safety net stays with your codebase after we leave.
02 · human approval
AI agents write and test code, but they cannot approve their own work, merge it, or deploy it. A production release needs a single-use approval tied to the exact commit being shipped.
for youYou always know who signed off on what is live.
03 · two reviewers
An independent AI model from a different vendor reviews in a read-only sandbox. Its findings are treated as hypotheses to verify, not orders. A human makes the final call.
for youBlind spots one model shares with itself get caught by the other.
04 · work that resumes
Each piece of work carries its spec, its "done when" checks, a live state file with the next action, and a log of decisions already made. A fresh session picks up exactly where the last one stopped.
for youNo stalled tickets and no paying twice for the same context.
05 · measured, not asserted
Agent sessions are measured: time to first change, idle time, rework, tokens. A new rule stays only if the numbers improve. Rules that do not help are removed.
for youDelivery speed that comes from a tuned system, not from cutting corners.
06 · yours from day one
Everything lives in your repository and your cloud accounts. We write down which models and tools touch your code, and your data is never used to train anything.
for youNo lock-in, and a clear answer when your own clients ask how AI was used.
what we won't claim
faq
We are. Agents do much of the typing, but a named engineer reviews and approves every change and every release, and stands behind it.
No. We only use AI tools on terms that exclude training on your code or data, and we list in writing which models and tools touch your project.
Yes. Some parts of a codebase, or whole projects, are better done by hand. Tell us and we will scope and price it that way.
We price the outcome, not the hours. The system makes us faster and the quality more consistent; you get a fixed quote either way.
next step
Tell us what you are building. Start with a fixed-price review or go straight to a build; either way you see the system at work from week one.