Every agency says it uses AI now. The real question is where. Point a model at a whole project and let it run, and you get a demo that looks finished. It falls over the first time a real user does something unexpected. The value is in a firm line: the work AI is good at on one side, the work that needs someone who has shipped this before on the other.
What we hand to AI
AI earns its keep on the repetitive layers of a build. The parts that are well understood and tedious, not uncertain:
- Scaffolding: project setup, routing, the shape of components before any real logic goes in.
- Boilerplate: form fields, data mapping, the fortieth variation of a list view.
- First drafts: a working version of a function. A developer then reads it, corrects it and hardens it.
- Migrations and imports: turning a messy export into clean, typed data.
- Tests from a written spec, once a human has agreed what the feature should do.
This work used to fill the first week of a project. Compressing it is what makes a fixed price and a short timeline possible on custom builds. It's why an AI-accelerated web app can be scoped, prototyped and delivered in a fraction of the usual agency schedule.
What stays with people
The expensive-to-get-wrong decisions stay with senior developers. A model has no stake in the outcome. It doesn't remember the last time a shortcut caused an outage.
- Architecture: how the pieces fit, what the data model is, what happens under load.
- Security: authentication, permissions, where secrets live, how payments are handled.
- Anything a customer touches: the checkout, the copy, the moment something goes wrong.
- Judgement calls: like when the honest answer is that you don't need the thing you asked for.
A model will happily write code that stores an API key in the browser, or trusts a value it should check. It doesn't know that's a problem. It's pattern-matching, and it isn't accountable. That gap is the whole reason a person signs off the work.
The review gate
Nothing an AI produces reaches a client until a developer has read it line by line. That sounds obvious. It's also the step most people skip, because careful review is slower than generation and the demo already looked fine. We treat the review as the real work and the generation as the draft. By the time it ships it isn't "AI code". It's code a senior developer has read, understood and is prepared to support on a Saturday.
Content gets the same treatment. Our AI marketing systems draft with a model, ground every piece in the client's actual business, and put a human approval step in front of publishing. Thin, unreviewed AI content hurts rankings. Fewer, checked pieces don't.
Why fixed pricing needs this
Hourly billing rewards slowness, so we quote fixed prices. A fixed price only works if the estimate holds. It holds because the predictable parts of a build really are faster now, and the risky parts are handled by people who can see the risk coming. Remove the review gate and the savings turn into rework. Rework is the one thing a fixed price can't absorb.
What it means for your data
When we build AI features into your systems, they run on your own business-tier accounts with training on your data turned off. Your documents stay in your storage. Access follows your team's permissions. You can walk away with everything intact. The principle that keeps us from shipping slop (a person stays accountable) is the same one that keeps your business out of someone else's training set.
If you've inherited a build that was generated fast and never finished, we fix that specific problem. The Vibe-Code Rescue page covers it. To see how we run this on our own operations, the about page is the short version.