A real product, not a prototype
The word MVP has been stretched to cover everything from a clickable Figma file to a fully-featured platform. We use it precisely: the smallest complete product that a real customer can sign up for, use, and pay for.
That means authentication, billing, error handling and basic analytics are in scope from the start, because a product missing those cannot validate anything commercially. It also means ruthless scope discipline on everything else.
- Authentication, billing and analytics included as standard
- Deployed to production infrastructure, not a staging demo
- Instrumented so you can see what users actually do
- Fixed scope agreed before the build starts
- Clean codebase your future engineering hires can extend
Architecture that outlives the MVP
The classic MVP trap is a codebase that gets you to launch and then has to be thrown away at the first sign of traction. Rewriting under growth pressure — while hiring, while onboarding customers — is one of the most expensive positions a startup can occupy.
We build MVPs on the same stack we use for scaled products. It is not slower to do it properly at this size, and it means your Series A engineering team inherits something they can build on rather than something they have to apologise for.
Scope discipline is the service
Most MVPs fail on scope rather than execution. The founder wants the full vision; the market only needs the first slice of it. A large part of what we contribute is arguing for a smaller build.
We work backwards from the single riskiest assumption in the business and design the smallest product that tests it. Everything not serving that goes into a phase-two document — recorded, not lost.
What you receive
- Live product in production with real users able to sign up
- Authentication, billing and subscription management
- Product analytics instrumented on key funnels
- Admin tooling for your team to operate the product
- Full source code, documentation and deployment pipeline
Technologies we use
AI MVP Development — frequently asked questions
How much does an AI MVP cost?
Most AI MVPs land between $30K and $70K depending on complexity and integration count. That covers a production product with auth, billing, analytics and admin tooling — not a prototype. We quote fixed scope after a discovery session.
How fast can you ship an MVP?
Six to ten weeks from kickoff to production for a well-scoped build. The main variable is scope discipline rather than engineering speed — projects that slip almost always slip because scope grew, not because development was slow.
What if we need changes after launch?
Expected — that is the point of shipping early. Most clients continue on a monthly retainer for iteration based on real user behaviour. Others take the codebase in-house and continue with their own team, which is a perfectly good outcome and one we plan the handover for.
Will the MVP scale if we get traction?
Yes, within reason. We build on architecture that handles meaningful early growth without a rewrite. No system scales indefinitely without work, but you will not be forced into an emergency rebuild the moment the product succeeds.
Do you work with pre-funded founders?
Yes, frequently. Several clients have used the MVP to raise their first round. We can structure phased scope so an initial milestone is fundable and the remainder proceeds once the round closes.
Talk through your ai mvp development project
A free 30-minute call with an engineer — not a salesperson. You leave with a scope, a cost range and an honest view of the risks.
Book a free strategy call
