Thirty days from idea to a working AI MVP is not a marketing number. It's the standard timeline for our fixed-scope sprints, and it works because of what we refuse to do, not because we type faster. This post is the exact week-by-week process we run for AI MVP development engagements — including what we deliberately cut, the tooling we default to, and the specific ways the timeline slips when discipline breaks.
One framing point before the schedule: an AI MVP is not a normal MVP with a model bolted on. In a traditional MVP, the risk is "will anyone want this?" In an AI MVP there's a second, prior risk: "can the AI actually do the core task reliably enough to charge for?" The entire 30-day structure exists to answer that second question as early and cheaply as possible.
What "MVP" means for an AI product
For AI products, viability is measured in eval scores, not feature counts. A five-feature product where the core AI task succeeds 60% of the time is not viable — users forgive missing features, but they do not forgive a tool that's confidently wrong. So our definition:
An AI MVP is the smallest product where the core AI loop hits a pre-agreed quality bar on a representative eval set, wrapped in just enough product for a real user to complete a real task and pay for it.
That means the MVP is eval-driven, not feature-driven. We agree on the quality bar in week one, in writing — for example, "correct answer with correct citation on at least 85% of the 150-case eval set, under 4 seconds median latency." Everything else is negotiable; that number is not.
Week 1: Scoping and the thin-slice prototype
Days 1–2: Scoping. We compress discovery into two working sessions. Outputs:
- The core loop, written as one sentence: "User provides X, system produces Y, success looks like Z."
- The eval bar (the number above) and 30–50 seed test cases pulled from the founder's real data.
- The cut list — everything we're explicitly not building. This document prevents 80% of timeline slips.
- Access to real data. Not sample data, not "we'll get it to you next week." Real data, day two.
Days 3–5: Thin-slice prototype. We build the ugliest possible version of the core AI loop: a script or notebook that takes real input and produces real output using a frontier model and the simplest viable retrieval or tool setup. No UI, no auth, no infra.
By Friday of week one, the founder sees the model attempt their actual task on their actual data. This is the single highest-value moment of the engagement, because it surfaces the truth early: sometimes the task is easier than expected (great — expand scope slightly), sometimes a hidden dependency appears (the data is worse than described — adjust now, not in week three), and occasionally the honest answer is "current models can't do this reliably." Finding that out in week one for the cost of a scoping sprint, instead of month four for the cost of a full build, is the whole point — and it's why we run this phase as a risk-reversal offer: if the thin slice doesn't prove the loop, you don't proceed and you don't pay for a build.
Week 2: Core AI pipeline and evals
Week two turns the thin slice into a real pipeline, and — in parallel, not after — builds the eval harness.
Pipeline work:
- Proper data ingestion: parsing, chunking, indexing, refresh logic if it's a RAG product
- Tool integrations and guardrails if it's an agent product
- Model routing: cheap model for easy cases, frontier model for hard ones
- Structured outputs, retries, and failure handling — the model will return garbage sometimes; the pipeline must degrade gracefully
Eval work:
- Expand the seed cases to 100–200, covering the ugly middle: ambiguous inputs, edge-case formats, adversarial phrasing
- Automated scoring — exact-match where possible, LLM-as-judge with spot-checked calibration where not
- A one-command eval run wired into CI, so every prompt or pipeline change gets a score before merge
The rule that makes the timeline hold: no pipeline change ships without an eval run. Teams that skip this enter the "fix one case, break three" spiral in week three, which is the most common way 30 days becomes 60.
End-of-week-2 milestone: pipeline hits or approaches the eval bar on the core loop. If we're at 70% against an 85% bar, week three's plan gets adjusted before we build product surface on top of a shaky foundation.
Week 3: Product surface and integration
Only now does it start looking like a product. Building UI before the pipeline is proven is the classic mistake — you end up demoing beautiful screens around a model that doesn't work.
- Frontend: Next.js, a component library, and ruthless reuse. One primary screen for the core loop, done well. Streaming responses, honest loading states, and visible sources/confidence where relevant — AI UX is mostly about managing trust.
- Auth and billing: managed services (Clerk/Auth0-class for auth, Stripe for billing). Zero custom code here.
- Integration: wiring the pipeline into the product with production concerns — per-user rate limits, cost caps, prompt injection hygiene for anything touching user-supplied documents.
- Observability: every model call logged with inputs, outputs, latency, and cost. When a user reports a bad answer, we can see exactly what happened in minutes.
That observability habit comes directly from our agent work — the self-healing software engineering agent we built achieves an 87% first-loop repair success rate precisely because failures are captured with enough context to diagnose automatically. Debugging sessions on that system dropped from 4–5 hours to about 15 minutes. You can't fix what you didn't log.
Week 4: Hardening and launch
- Days 16–18: Adversarial pass. We attack our own product: junk inputs, huge documents, prompt injection attempts, concurrent load. Every failure becomes an eval case.
- Days 19–21: Cost and latency tuning. Caching, prompt compression, routing thresholds. This is routinely worth 30–50% off the inference bill — on our Klebbix retrieval system this phase of thinking produced a 68% latency reduction and 35% cost reduction.
- Days 22–25: Launch mechanics. Deploy pipeline, error alerting, model fallbacks (primary provider outage should degrade, not kill, the product), final eval run against the bar, and a founder walkthrough of the runbook.
We ship against our internal AI MVP launch checklist — the public version is the same list we use.
What we deliberately cut (and you should too)
- Multiple models/providers "for flexibility." One primary, one fallback. Abstraction layers for hypothetical futures are pure waste at MVP stage.
- Fine-tuning. Almost never in an MVP. You don't have the production traces to train on yet.
- Admin dashboards. Founders read logs and a metabase query for the first 90 days. Fine.
- Self-hosted models. API models until the bill genuinely hurts.
- Every second feature. If the core loop doesn't sell, feature two won't save it. If it does sell, feature two is a fast follow.
How founders should prepare (this saves a full week)
- Collect 30–50 real examples of the task with known-good outputs. This becomes the seed eval set and it's the one thing we can't create for you.
- Get data access sorted before day one — API keys, exports, permissions. Waiting on a data export is the number one avoidable delay.
- Nominate one decision-maker who can answer scope questions within 24 hours.
- Write your cut list first. Come to scoping with what you won't build. It signals you'll be a great client and it makes the sprint faster.
- Know your budget mechanics — run the AI build cost calculator so scoping starts from a shared number.
How the timeline actually slips
In order of frequency: (1) data access arrives in week two instead of day two, (2) scope creep disguised as "small additions" — each one costs an eval cycle, not just build time, (3) no single decision-maker, so open questions sit for days, (4) skipping evals early and paying the regression spiral tax late, and (5) the quality bar was never written down, so "done" becomes a negotiation instead of a number.
Every one of these is preventable in week one. That's why week one is the part of the process we're strictest about.
Ready to build?
If you have an idea and 30–50 examples of the task, we'll scope it and build the thin-slice prototype before you commit a dollar — start at /contact.