MVP development services: launch, learn and scale

It depends on scope. The main cost drivers are the platform (web, mobile or both), the number of core features, the integrations, any AI components and the depth of design. After a free call you get a written scope and one fixed quote in USD, GBP or INR, so you know the cost before work starts.

Scope is the main lever. The tighter the set of must-have features, the sooner real users can try it. Every MVP moves through the same phases: discovery, a clickable prototype, build in sprints with a demo at the end of each, then launch. You get a phased plan with your fixed quote, so you know what happens and when.

Only the features needed to test your core value proposition and its success metric: usually sign-up, the one workflow users come for, and enough admin and analytics to run it and measure it. Everything else goes on a dated backlog. We use MoSCoW prioritization in discovery to agree the list in writing.

A proof of concept proves something can be built, often with no interface. A prototype shows how the product will work, usually as clickable screens with no real back end. An MVP is a real, working product that early users can sign up for, built to prove they want it. See the comparison table above.

Sometimes. No-code tools like Bubble or Webflow can be the fastest way to test a simple flow. The trade-offs are platform limits as you grow, costs that rise with users, less control over data and integrations, and logic that lives on the vendor’s platform. For AI MVPs or products where the technology is the value, custom code is usually the better start.

Ours is. We build MVPs on production-grade foundations: a clean data model, typed code, tests on the core flows, CI/CD and documentation. The goal is that version two builds on your MVP instead of replacing it, and that any future team can pick it up from the handover.

Yes. We build MVPs with LLM features, RAG search over your data and AI agents, with evals and usage limits from the start. Our flagship build, Kenerate AI, is an AI product we designed and engineered, with 200+ AI models and 23+ studios and tools in one app.

We measure what users actually do against your success metric, run user interviews and plan iteration sprints from the data. When users stay, we help you scale the architecture, hosting and team. You can keep us for ongoing development and care, or hand over to your own team with full code and docs.