AI MVP Sprint
From a real problem to a working, testable product in one focused sprint — designed with care, built fast with AI, deployed to real users.
How the sprint works
One process, four steps — the same Forward Deployed Engineering loop I use on my own products.
- 1
Scope
We pin down the real problem: who has it, what it costs them, and what “working” means. Output: a one-page scope you keep.
- 2
Design
The shortest path to a testable product — screens, data model, and the riskiest assumptions made explicit before any code.
- 3
Build
AI-accelerated implementation with daily progress you can click, not slides. Every decision documented in the open.
- 4
Launch & Learn
Deployed to real users with honest instrumentation — analytics events wired to the outcomes that matter, not vanity metrics.
Three ways to start
Fixed scope, fixed quote at the end of the scope call — no hourly billing, no surprise invoices.
Scope Call
- The real problem, written down
- One-page scope you keep
- Honest “build / don't build” verdict
- No obligation to continue
One-Feature MVP
- One feature, live in production
- Design + build + deploy
- Analytics wired from day one
- Handover walkthrough recorded
Full Sprint
- Complete MVP: problem → real users
- Weekly clickable increments
- Data model + admin basics
- 30-day post-launch support
Not ready to talk yet? Prep for free
The MVP Scope Builder walks you through the same five scoping questions and hands you a one-page scope — in about 3 minutes, nothing sent to a server.
What I need from you
- One decision-maker
- The problem in your own words (messy is fine)
- Access to whoever feels the pain daily
- Rough budget range — honesty here saves us both weeks
Questions I actually get
Why fixed scope instead of hourly?
Hourly rewards slowness. A fixed scope forces the design conversation that matters — what to leave OUT — and you know the number before we start.
What if I don't know exactly what to build?
That is the normal case, not the exception. The scope call exists precisely for this: you bring the problem, we shape the smallest thing that tests it.
Who owns the code?
You do — full repository transfer, documented decisions, nothing held hostage. I build it the way I build my own products: in the open.
What does “AI-accelerated” actually mean?
I use AI the way a master craftsperson uses power tools — for speed on the well-understood parts. The design thinking, the architecture, and the judgment stay human. That is what the FDE discipline is.
Built by the person who ships
The process behind this offer is the same one documented publicly on this site — see the work section for live products, honest status labels, and real progress percentages.