An AI MVP is the smallest version of an AI product that produces measurable user value and produces measurable evals. We ship most of ours in six weeks. Not because we cut corners, because we're disciplined about what an MVP is for: validated learning, not feature completeness.
What an AI MVP actually is
An AI Minimum Viable Product is a working AI feature that real users can use to do a real job, paired with a working evaluation harness that tells you whether the model is doing that job well. If either half is missing, you don't have an MVP, you have a demo.
The six-week cadence
- Week 1, Lock the user job, the eval rubric, and the golden dataset
- Weeks 2–3, Build the thinnest possible end-to-end flow with a frontier model and instrumented retrieval
- Week 4, Run evals, fix the top three failure modes, hold a real user session
- Week 5, Harden the deployment, add observability, integrate auth and rate limits
- Week 6, Soft launch to a closed cohort, measure, decide what to keep building
Why six weeks beats six months
Every additional week before users touch the product compounds risk. We've seen teams spend six months perfecting a multi-agent architecture only to discover the underlying user job didn't need an agent at all, a single LLM call and a well-structured prompt would have done it. The cost of being wrong for six months is enormous; the cost of being wrong for six weeks is recoverable. Andreessen Horowitz's 2025 State of AI report notes that the median time from AI prototype to production for teams without structured MVP processes is 14 months — nearly three times what a six-week MVP approach achieves.
Most AI projects take too long because teams spend months on infrastructure before validating the idea — McKinsey found that 72% of AI pilots never reach production, primarily due to extended scoping and build cycles that outlast executive patience.
An AI MVP that ships in six weeks with mediocre quality teaches you more than a perfect prototype that ships in six months. Real users surface failure modes no eval set will catch on its own.
What we deliberately leave out
Fine-tuning, custom embedding models, multi-agent orchestration, and elaborate caching layers are almost always the wrong place to spend MVP weeks. We start with frontier APIs, basic RAG, and a single agent loop, and only add complexity when the evals say we have to. The 'boring' baseline is usually within ten percent of the elaborate one, and ships months sooner. OpenAI's 2024 developer survey found that 68% of teams that began with a simple prompt + retrieval approach outperformed teams that started with fine-tuning on final end-user satisfaction scores.
The benefits compound
- Cost efficiency, six weeks of pod time is a recoverable bet, six months is not
- Faster time to revenue, paying users surface monetisation signal evals never will
- Flexibility, pivoting from a six-week MVP costs days; pivoting from a six-month build costs careers
- Validated learning, every user session sharpens the eval set and the prompt strategy
Speed to production matters more than perfection in the first version. GitHub's 2024 Octoverse report found that AI-assisted teams ship features 55% faster — the competitive advantage accrues to teams that iterate in production, not teams that perfect in staging.
The goal of an AI MVP isn't to ship the AI product. It's to learn whether you should.