AI Development
AI MVP development guide: ship your first AI product in 30 days
Most AI startups waste their first 6 months building infrastructure instead of shipping to users. This guide gives you the 30-day playbook for building and shipping an AI MVP that real users can validate — before you spend $500K on the full build.
By Aravind Srinivas, Former Head of Engineering at PyjamaHR··12 min read
The AI MVP mistake most startups make
They build for scale before validating the core interaction. They spend weeks on: fine-tuned models, custom vector databases, multi-agent orchestration, and beautiful UX — before a single user has validated that the core AI interaction is valuable.
The right approach: one user, one workflow, one model, two weeks. Then iterate.
Week 1: Define and validate the core AI interaction
- Day 1–2: Define the exact AI interaction you're validating. “AI for productivity” is not a use case. “AI that drafts a sales email from a LinkedIn URL in under 10 seconds” is a use case.
- Day 3–4: Test the interaction manually with real users — you, running the AI behind the scenes. The “Wizard of Oz” technique. Does the output solve the problem?
- Day 5–7: If the manual version works, choose your model and write your first prompt. Keep it simple. Measure the output quality manually against 20 real inputs.
Week 2: Build the minimum viable interface
- Day 8–10: Build the simplest possible UI that lets a user trigger the AI interaction. No auth, no onboarding, no settings — just the core interaction.
- Day 11–12: Add basic error handling and retry logic. Handle the cases where the LLM returns malformed output or the API is unavailable.
- Day 13–14: Ship to 5–10 users. Watch them use it. Don't ask for feedback — observe the behavior.
Week 3: Evaluate and improve
- Day 15–17: Build a basic evaluation harness — a spreadsheet or Braintrust trace log with 50 real inputs and your quality rating for each output.
- Day 18–20: Iterate on your prompt. Run your eval set after each change to make sure you're improving. Ship the best version.
- Day 21: Add the minimum infrastructure for production: rate limiting, logging, a fallback when the model fails.
Week 4: Launch and start charging
- Day 22–24: Add auth and basic billing (Stripe). You need to know if people will pay before you invest in AI infrastructure.
- Day 25–27: Launch to a wider audience. Post in relevant communities, cold outreach to target users, ship to your waitlist.
- Day 28–30: Review usage data. What inputs are users sending? Where does the AI fail? What features are they asking for? Prioritize your next sprint.
The AI MVP stack (2026)
- Frontend: Next.js + Tailwind (you can ship fast without a design system)
- Backend: FastAPI (Python) for LLM integrations, or Next.js API routes for simple use cases
- LLM: Claude 3.5 Sonnet via Anthropic API, or GPT-4o via OpenAI API
- Database: Supabase (Postgres + auth + storage in one)
- Payments: Stripe Checkout — the fastest path to a working paywall
- Evals: Braintrust or a Google Sheet + manual review — don't over-engineer
What NOT to build in your AI MVP
- Fine-tuned models — prompting is almost always sufficient for validation
- Custom vector databases — use pgvector in Supabase
- Multi-agent orchestration — it rarely works reliably enough for MVP validation
- Streaming responses — nice to have, not required for validation
- Complex UX — users will forgive rough UI if the AI is genuinely useful
Need to ship an AI product fast?
HyperNest's AI engineering team has shipped AI MVPs in as few as 2 weeks. We combine fractional CTO strategy with hands-on AI engineers who build production systems.