How we ship MVPs in 2-8 weeks

The AI-native development workflow

Most teams are still building software the old way: large teams, long cycles, heavy planning up front.

We've rebuilt our entire development process around AI as a force multiplier. Here's how we consistently ship production-ready MVPs in 2–8 weeks with a team of 2–3.

1. Start with the problem, not the solution

Week 0: Discovery (3–5 days)

Before writing any code, we:

Interview 5–10 potential users
Map their current workflow (tools, pain points, workarounds)
Identify the 1–2 things that, if solved, would create immediate value

Output: A one-page spec with:

Problem statement
Target user
Core value prop (one sentence)
3–5 must-have features for v1

AI assist: We use Claude to synthesize interview notes and identify patterns across conversations.

2. Design in hours, not weeks

Week 1: Design (2–3 days)

We design directly in Figma with our shared component library:

Mobile screens first (most usage happens on phones)
Max 5–7 unique screens for MVP
Use existing patterns from our design system

No custom illustrations, no branding exercises, no pixel-pushing. Just clean, functional UI.

AI assist:

Generate screen content and microcopy with GPT-4
Use Midjourney for placeholder imagery if needed
Let AI suggest component layouts based on our design tokens

3. Build in public, test with real users

Weeks 2–6: Build + iterate

We build in 1-week sprints:

Monday: Plan the week's features
Tuesday–Thursday: Build + ship to staging
Friday: User testing session with pilot partners

Every Friday, we put the product in front of real users. No prototypes—actual working software. We watch them use it, take notes, and adjust priorities for the next sprint.

AI assist:

Cursor + Claude for code generation and debugging
Automated test generation with GitHub Copilot
AI code review for security and performance issues

Our stack:

Frontend: Next.js + React Native (shared component library)
Backend: NestJS + PostgreSQL
Infra: Vercel + Railway + AWS S3

We use Turborepo to share code between web and mobile. One component library, two platforms.

4. Instrument everything from day one

Telemetry > opinions

We ship with analytics, error tracking, and feature flags built in:

PostHog for product analytics
Sentry for error monitoring
Stripe for payments (even if pricing isn't finalized)

Why? Because we want data, not guesses.

Week 3 decisions are informed by Week 2 usage. We know:

Which features get used (and which get ignored)
Where users drop off
What errors are blocking adoption

AI assist: We use AI to analyze user behavior patterns and suggest feature priorities based on engagement data.

5. Pilot-first, scale second

Weeks 7–8: Pilot onboarding

We don't launch publicly. We onboard 1–3 pilot customers who:

Represent the target market
Are willing to give weekly feedback
Will pay for the product (even if discounted)

Pilots get:

Hands-on onboarding (Zoom + Loom)
Direct Slack channel to our team
Co-design input on roadmap

Why pilots matter:

They validate (or invalidate) the value prop
They surface edge cases we didn't anticipate
They become design partners, not just users

Exit criteria: When 2+ pilots say "I'd pay full price for this," we're ready to scale.

The AI multiplier effect

Here's what AI handles for us:

Code generation (40% time savings)

Boilerplate CRUD operations
API route scaffolding
Database migrations
Test generation

Documentation (60% time savings)

API docs auto-generated from TypeScript types
User-facing help articles drafted from Loom recordings
Technical specs written from Figma + Notion notes

Code review (30% time savings)

Security vulnerability scanning
Performance optimization suggestions
Accessibility compliance checks

What AI can't do:

Understand user pain
Make product decisions
Design delightful UX

That's where we focus our time.

Our 8-week breakdown (example: WellnessOS)

| Week | Focus | Output | |------|-------|--------| | 0 | Discovery | One-page spec + 5 user interviews | | 1 | Design | Figma screens (7 screens, mobile-first) | | 2 | Infra + auth | User accounts, Stripe setup, basic dashboard | | 3 | Core feature 1 | Mindbody sync + POS UI | | 4 | Core feature 2 | Shopify integration + inventory sync | | 5 | Payments + checkout | Stripe Terminal SDK, checkout flow | | 6 | Polish + testing | Bug fixes, mobile responsiveness, pilot prep | | 7 | Pilot onboarding | Onboard 3 studios, train staff | | 8 | Iterate + validate | Weekly check-ins, fix blockers, add quick wins |

Result: Production-ready product with real users and revenue.

What we've learned

1. Small teams move faster Big teams create coordination overhead. 2–3 people can out-ship a team of 10 if they're using AI effectively.

2. Ship to real users early Don't wait for polish. Get feedback when it still hurts to hear it (Week 3, not Month 6).

3. AI is a tool, not a replacement AI makes you faster, but it doesn't make decisions. You still need taste, intuition, and user empathy.

4. Pilots > beta lists A pilot who pays $50/month is worth 100 people on a waitlist.

Try this yourself

This week:

Pick one feature you've been planning
Timebox it to 5 days
Use AI to handle the boilerplate
Ship to one real user by Friday

You'll learn more in one week than a month of planning.

Want to pilot a product with us? Apply here.