Every agency now says it uses AI. The honest question is not whether, but where. Point a model at a whole project and let it run, and you get a demo that looks finished and falls over the first time a real user does something unexpected. The value is in drawing a firm line between the work AI is good at and the work that still needs a person who has shipped this before.
What we hand to AI
AI earns its place on the repetitive layers of a build, the parts that are well understood and tedious rather than 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 that a developer then reads, corrects and hardens.
- Migrations and imports: turning a messy export into clean, typed data.
- Tests from a written spec: once a human has agreed what a feature should do.
This is the work that 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, and it is why an AI-accelerated web app can be scoped, prototyped and delivered in a fraction of the traditional agency schedule.
What stays with people
The decisions that are expensive to get wrong stay with senior developers, because a model has no stake in the outcome and no memory of 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: when the honest answer is that you do not 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 does not know that is a problem, because it is pattern-matching, not accountable. That gap is the whole reason a person signs off the work.
The review gate
Nothing an AI produces reaches a client without a developer reading it line by line. That sounds obvious and it is the step most people skip, because reviewing generated code carefully is slower than generating it and the demo already looked fine. We treat the review as the real work and the generation as the draft. The output is not "AI code" by the time it ships; it is code a senior developer has read, understood and is prepared to support on a Saturday.
The same discipline applies to content. 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 do not.
Why this is what fixed pricing needs
Hourly billing rewards slowness, which is why we quote fixed prices. A fixed price only works if the estimate holds, and it holds because the predictable parts of a build are genuinely faster now while the risky parts are handled by people who can see the risk coming. Take away the review gate and the savings turn into rework, which is the one thing a fixed price cannot 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, and you can walk away with everything intact. The same principle that keeps us from shipping slop, that a person stays accountable, is what keeps your business out of someone else's training set.
If you have inherited a build that was generated fast and never finished, that is a specific problem we fix; the vibe coding completions page covers it. If you want to see how we run this on our own operations rather than a client's, the about page is the short version.