The $4,999 MVP Development Sprint: How It Works
An MVP development sprint is the fastest way to go from a validated problem to a working product in the hands of real users. At 100x Engineering, our sprint runs exactly 21 days, costs a fixed $4,999, and delivers a production-ready AI product — not a mockup, not a proof-of-concept you can't show anyone.
This page walks through exactly what happens each week, what's included, and what you walk away with.
Why Fixed Price? Why 21 Days?
Most agency engagements fail founders before the first line of code is written. Open-ended time-and-materials contracts reward scope creep and punish fast shipping. We inverted the model: a fixed price and a fixed timeline force the one discipline that MVPs actually need — ruthless prioritization.
The $4,999 price point is real. It's not a loss-leader bait. It's sized to cover one core AI workflow, scoped tightly during a discovery call before we start the clock. If your idea needs more than that scope, we'll tell you before you pay anything.
For context on what AI MVP development typically costs at agencies that don't have this model, see our AI MVP cost breakdown.
Week 1: Foundation and Architecture
The first week is the unsexy week. No UI. No demos. Just the infrastructure that determines whether the product works.
Days 1–2: Kickoff and data audit
We start with a structured kickoff call: you, your technical co-founder if you have one, and our lead engineer. We map the exact input-output pair the AI needs to produce. Then we audit your data — its format, cleanliness, volume, and gaps.
Most founders are surprised by what the data audit reveals. Either they have more than they think, or the data they've been storing is in a format that takes two days to transform. Either way, we know by end of day two.
Days 3–5: Inference pipeline
By end of week one, we have a working inference pipeline: the AI takes real inputs from your actual data and produces real outputs. No wrapper, no demo mode, no mocked responses. We evaluate two to three model configurations against your data and pick the one with the best accuracy-to-cost ratio.
You'll see real outputs by Friday. Not slides. Actual results.
Week 2: Product and Interface
With the AI logic working, week two builds the thing people actually use.
Days 6–8: API and backend
We wrap the inference pipeline in a clean API. Authentication, rate limiting, error handling, logging — the production-grade infrastructure that most "fast" builds skip and then spend three months retrofitting.
Days 9–10: UI and workflow
The interface is scoped to your primary use case. If users need to upload a document and receive a structured output, that's what we build. If they need to query a system and get a dashboard, we build that. No marketing pages, no settings you won't use for six months — just the workflow loop that proves the value.
You'll have a staging environment to test with by end of week two.
Week 3: Deployment and Handoff
Week three is about getting to production and making sure you can operate what we built.
Days 11–14: QA and iteration
You use it. You break it. We fix it. We run edge cases against the inference pipeline, stress-test the API, and handle the three to five things that always surface when a real user touches a new product for the first time.
Days 15–21: Production deployment and documentation
We deploy to your cloud environment (AWS, GCP, or Vercel depending on stack), configure monitoring, and write the operational runbook. You get:
- Full source code in your GitHub repo
- Deployment scripts and environment configs
- Architecture documentation (what each component does and why)
- A 60-minute handoff call where we walk your team through the codebase
What's Included (and What Isn't)
Included:
- One core AI workflow, end-to-end
- Production deployment on your cloud account
- Source code ownership — no licensing, no lock-in
- 14-day bug fix warranty post-launch
- One revision round during week two
Not included:
- Third-party API costs (you pay OpenAI, Anthropic, or your cloud provider directly)
- Ongoing hosting or maintenance (we can quote separately)
- Additional workflows or features beyond the scoped sprint
If you want to compare this model against hiring an in-house AI developer, we've written a detailed breakdown at /blog/ai-agency-for-startups.
Who This Is For
This sprint works best for:
- Pre-seed and seed founders who need to show investors something real, not slides
- Operators at SMBs who have a manual workflow eating 40+ hours a week and want to automate it
- Product teams at growth-stage companies who need a validated AI feature before committing headcount
It's not for enterprise procurement cycles, multi-team integrations, or products that require custom model training. For those, schedule a call and we'll scope appropriately.
How to Get Started
The sprint starts with a 15-minute scope call. We ask you three questions: what problem are you solving, what data do you have, and what does success look like in three weeks?
If the scope fits, we'll send a one-page agreement and a start date within 24 hours.
Book your scope call — no commitment, no pitch deck required.
Related Resources
More articles:
Our solution: AI Workflow Automation
Glossary:
Comparisons:
Free Tool: Get a real cost breakdown for your MVP sprint. → AI MVP Cost Estimator