How we build
"AI-built software" can mean two very different things. It can mean code nobody read, shipped on a good feeling — or it can mean a disciplined engineering practice that happens to move at machine speed. We're the second kind, and because claims are cheap, here is the actual machinery.
Every feature ships with the test tiers that apply to it — unit tests for the logic, integration tests against a real database, end-to-end tests driving the product the way a person would. Tests encode intended behavior: when code and test disagree, the code changes. Weakening a test to make it pass is a firing offense we wrote into our own operating rules.
There is no side door. Every change — a feature, a one-character fix — goes through a branch, a pull request, and a wall of required checks: the full test suite, type checking, linting, static security analysis, and a dependency audit. Supply-chain hygiene is part of the gate too: locked, integrity-checked installs and reviewed dependency updates, because we've watched package ecosystems get attacked and stayed unharmed on discipline alone.
Passing tests aren't the finish line. Features are exercised against a real deployed preview — the actual product, running on the actual platform — before anyone approves them. For our products that includes recorded video walkthroughs of the feature in use, so the review is of reality, not a description of it.
A release only publishes after the full check suite passes on the exact code being shipped. Then the live site is smoke-tested — and if that check fails, the deploy rolls itself back automatically, before most people could notice. A failed deploy opens a tracked issue; it is structurally impossible for one to fail silently.
We build with agentic engineering — AI agents write most of the code, around the clock, at a pace a studio this size couldn't otherwise touch. What makes that safe isn't hope, and it isn't vibes: it's that every rule above is written down as machine-enforced doctrine the agents must operate inside. Branch protection they can't bypass, hooks that block bad commits, tests they aren't allowed to weaken, and a human approval on every merge.
We call it surgical because every change must trace directly to an intent — no drive-by rewrites, no speculative abstractions, no 200 lines where 50 would do. The result is AI speed with engineering-culture standards. We didn't invent these practices; we just refuse to skip them.
Everything the studio learns — operating rules, tooling, pipelines, hard-won fixes — lives in a shared, versioned kit that every product consumes. A lesson learned in one product becomes doctrine for all of them, usually the same day. The second product starts where the first one finished.
None of this is glamorous. That's rather the point — trust is built out of boring, repeated care.
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