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Six weeks is not a slogan. It is a constraint we design around. When a founder comes to us with an AI product idea, the worst thing we can do is spend three months building the wrong thing beautifully. The goal of an MVP is to put a real, working product in front of real users while the market is still the same shape it was when you had the idea.
Here is exactly how we compress that into six weeks without cutting the corners that matter.
Week 1 — Strategy, not slides
We do not start in Figma and we do not start in the codebase. We start with the single decision that determines everything else: what is the one job this product does that no spreadsheet, no human, and no existing tool does well today?
In week one we run two or three working sessions to pin down the core loop — the smallest sequence of actions that delivers value. We define the data we have, the data we need, and where the AI actually earns its place. Most ideas have one AI-shaped problem buried inside five conventional ones. We find it.
By Friday of week one you have a one-page product definition, a prioritised scope, and a thrown-away version of every feature that did not survive contact with reality.
Weeks 2–3 — The vertical slice
We build the riskiest thing first. Not the login screen. Not the settings page. The part that, if it does not work, kills the product.
For an AI-native MVP that is almost always the model behaviour: retrieval quality, prompt reliability, evaluation, the cost-per-action math. We get one end-to-end path working through real infrastructure early, because that is where the unknowns live.
What we build in these two weeks:
- The core AI workflow running against production-grade data
- A thin but real UI around it — no mockups pretending to be product
- An evaluation harness so we can measure quality instead of vibing it
- Cost and latency instrumentation from day one, not bolted on later
If the hard part works in week three, the rest is just craft. If you save the hard part for week five, you do not have an MVP — you have a demo that breaks under load.
Weeks 4–5 — Make it a product
Now we widen out. The vertical slice proved the concept; these weeks turn it into something a stranger can use without you in the room.
This is where design discipline pays off. Because we reused a real component system from the start, building the remaining screens is assembly, not invention. We add the supporting flows — onboarding, empty states, error handling, the unglamorous surfaces where trust is actually won or lost.
We also harden the AI layer here: guardrails, fallbacks for when the model is uncertain, and graceful degradation so a bad generation never becomes a dead end for the user.
What we deliberately leave out
An MVP earns its name by what it refuses to ship. We cut admin dashboards, role hierarchies, billing tiers, and every “we’ll need it eventually” feature. Eventually is not week six.
Week 6 — Ship and instrument
The final week is about going live and learning. We deploy to real users, wire up analytics that answer product questions rather than vanity ones, and set up the feedback loop that turns the next six weeks of work into informed decisions.
You launch with:
- A product in users’ hands, not a roadmap
- Real usage data and AI quality metrics
- A clean, documented codebase the team can keep building on
- A clear, evidence-based view of what to build next
Why six weeks works
Speed is not recklessness. It is the result of saying no early, building the hard part first, and reusing everything that can be reused. A larger team does not move faster — it moves with more coordination overhead. A small senior team with a sharp scope ships what a twenty-person agency is still scheduling kickoff calls about.
Six weeks from blank repo to live AI product. Then the real work — growth — begins.