Key takeaway: A PRD-to-deployed-MVP sprint takes 14 days when you combine Claude Code for scaffolding, senior decision-making for architecture gates, and a day-by-day cadence that ships real product — not a prototype.
Two weeks. Real product, not a prototype. Deployed on a real domain, hitting real APIs, serving real users. This is the cadence we quote on AI-powered SaaS engagements now, and it is not marketing. It is the sequence we run.
Here is how the 14 days actually work, what gates exist between stages, and where Claude Code still needs a senior human standing behind it with a red pen.
Day 1–2: The PRD
The first two days are not code. They are a product requirements document sharp enough that everything downstream can be driven from it.
A good PRD in 2026 is not a 40-page spec. It is tight. Problem statement. User roles. Core flows written as user stories with acceptance criteria. Data model sketch. Third-party APIs we are committing to. Non-goals, stated explicitly. Success criteria for the MVP itself.
We write the PRD collaboratively with the founder, using Claude as a drafting partner for the boring structural parts. The founder brings the product vision. We bring the architecture instincts — what should be one table versus three, what should be a background job versus inline, where the edges are going to bite later.
Gate 1: the founder signs the PRD. Nothing after this day reopens questions the PRD answered. If scope changes mid-build, it is a change request with a time estimate, not a "while you're in there" add-on.
Day 3–5: Design system and screens
Three days is enough to design an MVP when two things are true. First, we start from an existing design system (ours is Nova). Second, we are designing to be implemented by Claude Code, which means machine-readable docs, strict token naming, and explicit component props.
We do not design 40 screens. We design the ones that matter — the primary flow end to end, the empty states, the error states, and one or two secondary flows. The rest get implemented from the system without bespoke designs. If your design system cannot handle a settings page without a Figma file, your design system is too weak.
Gate 2: design review. Founder signs off on the primary flow. We stress-test edge cases internally.
Day 6–10: Claude Code implementation
This is the block where the compounding happens. Five days to go from empty repo to working app.
The rhythm is specific. A senior developer scopes each implementation unit — a route, a component, an agent, an integration — and hands it to Claude Code with clear input and output contracts. Claude Code writes the code. The developer reviews every diff before it lands. Tests get written alongside, not after.
The worked example here is our AI Business Analyst. The architecture is a 5-agent Anthropic pipeline wrapped in an Express server. Agents run in parallel where they can — business model, competitor, and digital presence run concurrently, then the checklist agent, then the assembler. Each agent has its own prompt file under prompts/, its own runner under agents/, and returns strict JSON.
JSON discipline matters more than it sounds. Every agent return is parsed by utils/jsonCall.js, which will extract valid JSON even when a model slips in a stray sentence. Without that discipline, the pipeline is a house of cards. With it, we can chain five agents and assemble their output into a typed Report object that drives the frontend directly.
Where did Claude Code actually struggle on this build? Two places. First, the React component choreography when we had nested conditional renders with theme tokens. The model kept producing plausible-looking markup that did not quite bind to the JSON shape. We fixed that with tighter prop documentation and examples in the prompts.
Second, and more interesting: the "grounded vs inferred" signal discipline had to come from the humans. The original design of the report let every agent claim high confidence on everything. We noticed the model was cheerfully inventing things like "competitor A has a domain authority of 47" with no way to ground that number. We had to rewrite the prompts so that every section declares its data source, and intentionally drop sections we could not ground — SOV rank, per-competitor DA, revenue-impact predictions. That decision came from Dima, not from the model. The model is happy to fabricate. A senior on the team has to choose what honesty looks like.
Gate 3: end-to-end happy path works. The app runs locally. The primary flow completes. Any skipped edge cases are written down, not forgotten.
Have a PRD and a two-week window?
We've done discovery-to-shippable in 14 days more than once. Send us your scope — we'll tell you if it fits.
Day 11–12: Integrations
Two days for the third-party wiring. On the AI Business Analyst that meant Google PageSpeed Insights, the Anthropic API, and a Make webhook for lead forwarding. The pattern is always the same: wrap each external service in its own file under services/, with a narrow interface, a timeout, and a non-blocking failure mode.
"Non-blocking" is the word that matters. PageSpeed fails on 10% of real sites for reasons that have nothing to do with us. The rest of the report still renders, and the Performance score slot says "Not measured." If your app cannot tolerate a partial failure of a third-party service, it is not production-ready.
Rate limiting goes in here too. The AI Business Analyst is rate-limited to 3 analyses per IP per hour. Cinnaboner's own chatbot is limited to 10 requests per minute per IP. Not because we expect abuse. Because a runaway loop on someone's dev machine should not empty the Anthropic budget.
Gate 4: integrations tested against real services, not mocks. Failure modes verified.
Day 13: QA
One day of QA is not much. It is enough if the build has been tested along the way. We run through the primary flow on three browsers and two devices. We run it with PageSpeed disabled, with the Anthropic key invalid, with Make down. We check that rate limits return the right status codes. We check that every error message is either helpful or safely generic.
We do not run a two-week UAT. The founder has been using the build since Day 9 in staging.
Gate 5: zero known critical bugs. Known minor bugs are listed in a post-launch ticket queue, not blocking ship.
Day 14: Deploy to Render
Deploy day is the least interesting part of this sequence, which is exactly as it should be.
The AI Business Analyst ships with a render.yaml Blueprint. Create the GitHub repo, push, go to Render, pick New → Blueprint, select the repo. Render reads the Blueprint, prompts for the secrets — ANTHROPIC_API_KEY, optionally PAGESPEED_API_KEY, MAKE_WEBHOOK_URL, ALLOWED_ORIGIN — and stands up the service. Health check is /health. Total deploy time, counting DNS, is under an hour.
A note on plans: the Blueprint pins to starter at $7/month. The pipeline takes 45–90 seconds. Free-tier Render sleeps idle services and has short request timeouts, so the first analysis after a sleep will timeout. We do not ship production MVPs on free tiers.
What Claude Code does well
Scaffolding. CRUD. Route definitions. Component libraries from a design system. API wrappers. Test stubs. Prompt templates. Anything that is pattern-matching on well-documented shapes. Speedup is 3–5x on this kind of work, routinely.
What Claude Code does not do well
Judgment calls. "Should this be one table or three." "Is this feature worth building." "Will this decision backfire in six months." "This is technically correct but confusing for users." "This third-party API will change in Q3 and we should wrap it differently." None of that is in the model's training distribution in a usable way.
Also: anything where the domain is narrow and the stakes are high. Solar engineering, as on Enurgen. Enterprise cryptography, as on Securosys. Blockchain legal infrastructure, as on Jurat. In those builds, the senior human does more typing than Claude, and rightly so.
What this means for you
Fourteen days is real, for the right kind of product. The right kind is a SaaS MVP with a clear PRD, a standard data model, off-the-shelf auth, and third-party integrations that already have good APIs. If your MVP has custom real-time infrastructure, regulated data, or invented hardware, double the timeline and staff it with more seniors.
If you have a PRD roughly ready and want to run the 14-day PRD-to-MVP track, drop us a line: hello@cinnaboner.com.
Ship in weeks, not quarters.
Bring the idea. We'll bring the team. MVP live in fourteen days for the right shape of product.
