Service 04 / 12
MVP Development services
MVP engineering for founders: one core job, a measurable success metric, and a codebase you can keep building on.
Service overview
An MVP is an experiment. The goal is to learn whether a product works with the smallest build that can answer that question, while keeping the foundation clean enough to grow.
For AI MVPs, that means extra discipline: an evaluation set before the first prompt, clear limits on what the AI does, and model costs that make sense per user. For SaaS MVPs, it means authentication, billing and onboarding that don't need rewriting after launch.
We scope with you, build in short increments you can test, and help you decide what to build next from real usage.
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AI MVPs
Products built around one AI job, with an evaluation harness from day one.
- 02 / 04
SaaS MVPs
Core workflow, onboarding, billing and admin, ready for first customers.
- 03 / 04
Shopify app MVPs
Embedded apps and extensions to test with merchants before scaling.
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Internal tool MVPs
AI-assisted tools that prove a workflow before a wider rollout.
How we build MVPs
06 —
One core job
We cut scope to the one thing the MVP must prove.
Measurable success
A success metric agreed before building, and analytics to track it.
Real users early
Working increments you can put in front of users quickly.
Code you keep
A clean foundation that grows into the product, not a throwaway prototype.
AI costs understood
Per-user model costs estimated and tracked from the start.
Clear next steps
After launch, we help decide what to build next from real usage.
What's included
- F/01
Scoping workshop
Users, the core job, success metric and what's out of scope.
- F/02
Evaluation harness
For AI MVPs: test cases and pass criteria that run in CI.
- F/03
Core product
The main workflow, onboarding and a simple admin.
- F/04
Auth and billing
Sign-in, roles and Stripe billing when you're ready to charge.
- F/05
Analytics
Events that measure the success metric from day one.
- F/06
Launch support
Monitoring, error tracking and quick fixes after release.
Our MVP development process
Short cycles from idea to real users.
- 01
Scope
One core job, one success metric, and a list of what we won't build yet.
- 02
Prototype
A clickable or working prototype to test the riskiest assumption.
- 03
Build
The MVP in short increments, reviewed with you each cycle.
- 04
Launch
Release to first users with analytics and monitoring.
- 05
Learn
Review usage and decide what to build, change or drop next.
Tools we use
01Product
- TypeScript
- Next.js
- React
- Node.js
02Data and billing
- PostgreSQL
- Supabase
- Stripe
03AI
- OpenAI
- Anthropic Claude
- Google Gemini
- Vercel
We answer what matters to your business.
A minimum viable product: the smallest version of a product that real users can use, built to test whether the core idea works before investing in the full product.
AI output varies, so an AI MVP needs an evaluation set and pass criteria before building, clear limits on what the AI does, and an estimate of model costs per user.
It depends on the core job, integrations and how much of the product needs to exist for users to try it. We agree a scope and plan after a scoping call.
We build MVPs on a clean foundation, usually TypeScript, Next.js and PostgreSQL, so the code can grow into the product rather than being replaced.
Yes. We help read usage, prioritize what to build next and continue development as your product team or alongside your hires.
It depends on scope, integrations and AI usage. We share an estimate after a scoping call.