The most expensive words in AI are "let's build the whole thing." The cheapest are "let's find out in four weeks." This is the exact structure of our AI Sprint — the smallest honest way to answer the question every founder actually has: will this work on my data, for my users, at a cost that makes sense?
Why small-first beats big-vision
AI projects carry a kind of uncertainty normal software doesn't: nobody — including us — knows exactly how well a model will perform on your specific data until it's tried. Pretending otherwise is how six-month contracts end in demos that impress nobody. A sprint converts that uncertainty into evidence, at fixed cost, in weeks.
Week 0: the scoping call
Before anything is signed: a free 20-minute call with a senior engineer — not a salesperson. We need three things from you: the problem in your own words, access to a sample of the real data, and a decision-maker who'll look at weekly demos. If the idea doesn't need AI at all, this is where we say so and point you at something cheaper.
Week 1: the thin slice
We take the narrowest complete path through the problem — one document type, one workflow, one conversation — and make it work end to end on your actual data. Ugly, but real. At the same time we start the eval set: the first fifty real questions or cases, with what a correct result looks like. Friday: you see it run.
Week 2: the working core
The slice becomes a system: proper retrieval over your knowledge, guardrails for the obvious failure modes, the first accuracy numbers from the growing eval suite. This is the week expectations meet reality — sometimes the model exceeds them, sometimes we adjust the approach. Either way you're watching it happen, not reading about it later.
Week 3: hardening
Edge cases, error handling, the integration that matters most (your CRM, your helpdesk, your telephony), and cost measurement: what does one task actually cost in model calls? We tune that number down — caching, routing simple cases to smaller models — because unit economics decide whether this scales.
Week 4: the decision packet
You end the sprint with four things: a working prototype you own outright; eval results — real accuracy numbers on real cases, not adjectives; a cost model for running it at your volume; and a roadmap with a fixed quote for the production build, if the evidence says go.
Sometimes the answer is no — that's a win
A sprint that ends in "the accuracy isn't there yet" or "the economics don't close" for the price of a prototype is the cheapest good news in software. You've spent a fraction of a failed build to avoid the whole failed build — and you keep the eval suite and everything we learned.
Have an idea that deserves four honest weeks? Ask the studio live or book the 20-minute scoping call — week 0 is free either way.
