Custom AI Development

It is building AI software around one business instead of selling the same product to everyone. The model is the easy part now. The work is in your data, your process, the exceptions nobody wrote down, and the systems it has to live inside. That is what makes it custom, and it is why the same idea costs different amounts at two different companies.

Builds start at $2,500 one-time. If you want us to keep running and growing it, that is Growth Partner: the same build plus $1,250 a month at the introductory rate, which renews at $2,500 a month. Anything with several systems or unusual rules gets its own conversation, and we will say that rather than pretend a number. Scope is what moves the price, which is why we scope before we quote instead of after.

Our six builds took two weeks to three months, and each of those numbers is published on its case study. Two weeks was a Chrome extension; three months was a full marketplace. The honest version is that a tight scope ships fast and a vague one does not ship at all, so the timeline is mostly decided in the scoping call.

Buy it, if it exists. Genuinely. If something on the market does most of the job, the cheap and fast move is to buy it and pay us to wire it into your business properly. Build when the tool does not exist, when the thing you do differently is the whole point, or when the off-the-shelf version would make you the same as your competitors.

The software and the source code are yours. It runs on your infrastructure, in your accounts, under your brand, and the repository is yours from the first commit rather than on handover day. Fire us tomorrow and it keeps running, and any competent developer can pick it up, because that is the test we build to. If your build uses a brain, only its structure is licensed rather than sold; the data in it and everything it learns about your business stay yours, on the terms our pricing page sets out.

The build runs in your accounts on your keys, so your customers' data lives with you, not with us. We do not pool client data, we do not train shared models on it, and nothing one client's product learns is available to another's. What we do touch during a build, we handle under our privacy policy, which says plainly what we hold and how you get it back or deleted. If your sector has rules of its own, tell us on the first call and we will design to them or tell you we are the wrong people.

That is what step three is for. You are using the real thing on real work as early as the scope allows, so the moment of truth lands while the build is small and it is still cheap to change course or stop. We would rather scrap a small build that proved the idea wrong than deliver a big one that quietly proves nothing.