An AI product built to be sold,
not just a demo that impressed one room
Most AI ideas do not fit a template, and most AI demos never become a product people pay for. We build the MVP as something sellable from day one: auth, payments, tests, and a scope narrow enough to actually finish.
The gap between a demo and a product
Most AI ideas do not fit a template, and most AI demos never turn into a product people actually pay for. A real MVP needs the parts a demo skips: authentication, payments, tests, and a scope narrow enough to actually finish and launch.
This fits founders and teams with a genuinely new AI idea. They need a real first version in front of customers, not a proof of concept that stops at the demo. It is not the right fit for an idea an existing product already serves well. Building custom only makes sense once nothing on the market already does what you need.
What is inside
Scoping comes first, and it is often the hardest, most valuable part of the whole project. It means cutting a broad idea down to a version narrow enough to ship on a real timeline while still proving the core value. Architecture decisions account for where the product likely goes next, not just what the first version needs. Early choices should not turn into expensive rewrites six months later.
The AI layer sits behind an interface that allows swapping model providers later. The best model for your use case today may not be the best one in a year. Authentication, payments and automated tests go in from the first week. This is the unglamorous infrastructure that turns a working demo into something you can actually charge for and trust in production.
How we build it
We start with an honest scoping conversation. It is often the single highest-value session in the whole project. Cutting the idea down to something that can ship in a reasonable timeline decides whether this becomes a real product or a never-finished ambition.
Architecture gets decided with the next version in mind, not just the MVP. The model layer, data model and auth system should not need rebuilding as the product grows. We build with automated tests from the first week. We get a working deployment up early, so you can show real progress to early users or investors well before the full scope is done. Launch includes monitoring and backups from day one. We treat the MVP as a real product from the start, not a throwaway prototype.
What to watch
The real risk with any new AI product is scope creep eating the timeline before anything ships. That is why honest scoping at the start matters more than almost any technical decision that follows. It is also why we tell you directly when an idea needs cutting down, rather than letting the timeline slip quietly.
Model provider dependency is worth planning for explicitly too. Building behind a swappable interface costs a little more upfront, and saves a potentially expensive rewrite later if pricing, quality or availability shifts. Expect the real product to diverge somewhat from the original plan once real users interact with the first version. Budget for that iteration.
Timeline and price
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| MVP | from $2,500 | One core AI capability, auth and payments, automated tests from week one | 5 to 6 weeks |
| Production | from $6,500 | Several integrated capabilities, polished first release, monitoring and backups | 8 to 10 weeks |
| Full control (handover-ready) | from $7,500 | Everything in Production, plus a full handover package: architecture docs, test suite, admin access audit, and a walkthrough so your own team or another vendor can run it without us | 10 to 12 weeks |
Running cost on top of the build is usually $25 to $90 a month in hosting and model costs, depending on usage and which model provider is used.
What you own at the end
You own the full codebase, the architecture, the deployment and every account it runs on, in your name from the first commit. The handover package and documentation mean your own team, a future hire, or another developer can keep building without needing us.
Related
Pairs with AI agent orchestration platform when the product’s core is several agents working together. Add AI model evaluation product for catching regressions as the product grows past MVP. See the development service page and the AI agents service page for the broader range of builds we run. Real builds to look at: the SENET AI assistant product case study and the AI persona digital expert case study. Both are custom AI products built from idea to real, paying usage. Have an AI idea that does not fit anything already on the market? Get in touch and we will scope it honestly.
FAQ
How much does a custom AI product MVP cost?
From $2,500 for a tightly scoped first version: one core AI capability, basic auth and payments. A fuller MVP with several integrated capabilities and a polished first release runs $6,500 to $7,500.
How long does it take?
Five to six weeks for a narrowly scoped MVP. A broader first version covering several capabilities takes longer, typically nine to twelve weeks. We will tell you honestly if the idea needs cutting down to ship on a reasonable timeline.
What is the stack?
Python and FastAPI, or Node and TypeScript, depending on the product. Claude or GPT for the AI layer, built behind an interface that allows swapping models later. PostgreSQL, and Next.js or React Native for the client, depending on web or mobile.
Who owns the product after launch?
You do. The full codebase, the architecture decisions and the deployment are yours from the first commit, with documentation that lets your own team or another developer continue without us.
What if the idea is bigger than an MVP budget allows?
We say so directly and help cut it down to the version that proves the core idea and can actually ship. We will not take a bigger budget to build something that never finishes. Honesty here saves more money than it costs.