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.
What it is and who needs it
A custom AI product MVP is the first sellable version of an idea that does not fit any existing template, built with the parts that make something a real product rather than a demo: authentication, payments, tests, and a scope narrow enough to actually finish and launch. It fits founders and teams with a genuinely new AI idea who need a real first version to put in front of customers, not a proof of concept that stops at the demo. It is not the right fit for an idea well served by an existing product; building custom only makes sense once nothing on the market already does what you need.
What is inside
Scoping comes first and is often the hardest, most valuable part: 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, so early choices do not become expensive rewrites six months later. The AI layer sits behind an interface that allows swapping model providers later, since the best model for your use case today may not be the best one in a year. Authentication, payments and automated tests are built in from the first week, 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, often the single highest-value session in the whole project, since cutting the idea to something that can ship in a reasonable timeline is what determines whether this becomes a real product or a never-finished ambition. Architecture gets decided with the next version in mind, not just the MVP, so the model layer, data model and auth system do not need to be rebuilt as the product grows. We build with automated tests from the first week and a working deployment 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, treating 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, which is why honest scoping at the start matters more than almost any technical decision that follows, and why we will tell you directly when an idea needs cutting down rather than quietly letting the timeline slip. Model provider dependency is the other consideration worth planning for explicitly, building behind an interface that allows swapping providers 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 as 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 $11,000 | 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 continue building without needing us.
Related
Pairs with AI agent orchestration platform when the product’s core is several agents working together, and 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: the SENET AI assistant product case study and the AI persona digital expert case study, both 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 $11,000.
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, and 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. 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, rather than taking a bigger budget to build something that never finishes. Honesty here saves more money than it costs.