The Honest Version of "Fast"
Three weeks sounds like marketing. It isn't. It's a constraint that forces ruthless scope discipline. Our AI MVP sprint delivers a working, production-ready product — not a prototype — in 21 days.
Most AI product failures don't die from technical complexity. They die from scope creep — teams that spend months perfecting a feature set that users never asked for, on a foundation that was never tested with real data.
Our sprint model inverts that. We start with what ships, not what's possible.
Week 1: Architecture & Data
The first week is the least glamorous and the most important.
We're not writing UI code. We're answering:
- What data does this AI actually need?
- Where does it live, how clean is it, and what transformation is required?
- Which model performs best on your specific task?
- What does a good output look like vs. a bad one?
By Friday of week one, we have a working inference pipeline — no UI, but the core AI logic is functional and tested against real inputs.
Week 2: Product Layer
Week two is where the product emerges. We build the interface, authentication, data ingestion flows, and the feedback mechanisms users need to trust AI outputs.
Key decisions at this stage:
- Streaming vs. batch — Does the UX need token-by-token streaming, or is a batch result acceptable?
- Human-in-the-loop touchpoints — Where does the AI need human review before acting?
- Error states — What does the product do when the AI is wrong or uncertain?
Week 3: Hardening & Handover
The last week isn't polish — it's production-readiness.
We add:
- Rate limiting and abuse prevention
- Logging and observability
- Cost controls (token budgets, model routing)
- Documentation that lets your team extend the codebase
By end of week three, you have a deployed, working product with a handover call and complete documentation.
What We Don't Build in a Sprint
Scope discipline means making cuts. In three weeks, we don't build:
- Custom fine-tuned models (use foundation models + RAG)
- Native mobile apps (web-first, responsive)
- Multi-tenant enterprise auth (single-tenant or simple OAuth)
- Complex billing systems (Stripe checkout, not full subscription management)
These aren't impossible — they're week four, five, and six. But they're not week one through three.
The Model Selection Process
One thing clients often underestimate is model selection. The right model for your task can be the difference between a product that works and one that doesn't.
We test every sprint use case across at least two models before committing. For most production workloads in 2025:
- Claude 3.5 Sonnet — Coding agents, document analysis, long-context tasks
- GPT-4o — Multimodal workflows, tool-heavy agents, OpenAI ecosystem integrations
- Gemini Flash — High-volume, cost-sensitive classification and extraction
The model choice locks in at the end of week one, based on your actual data and actual prompts.
Why Founders Choose This Over Hiring
Building an in-house AI team takes 3–6 months from job posting to productive engineer. At senior AI engineer salaries ($200–350k/year), you're looking at $50–100k before shipping a line of production code.
A sprint gives you a working product in three weeks for a fraction of that cost — with documentation and architecture decisions your eventual engineering hire can extend.
It's not either/or. Most of our clients use a sprint to validate before hiring.
Ready to scope your sprint? Book a 15-minute call →
Related: How Much Does an AI MVP Cost in 2026? · Build vs Buy Your AI MVP · What is an AI Agent?
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