How to Build an AI MVP in 2 Weeks (Without a Technical Co-Founder)

A week-by-week playbook for non-technical founders to go from idea to working AI prototype. Covers LLM selection, no-code tools, and the exact stack I use to ship MVPs fast.

Girish Kotte

February 10, 2026 · 6 min read

Most founders spend months building their first product. They hire agencies, write 40-page specs, and burn through runway before a single user touches the product. I've shipped three AI startups in 12 months. Here's the exact playbook I follow to go from idea to working MVP in two weeks.

Why Two Weeks Is the Right Timeline

Two weeks forces ruthless prioritization. You can't gold-plate features when the clock is ticking. More importantly, speed-to-learning matters more than speed-to-market. Every day your MVP sits unbuilt is a day you're operating on assumptions instead of data.

The goal isn't a polished product. It's a functional prototype that lets you test your core hypothesis with real users. If your AI solves the problem, you'll know in two weeks. If it doesn't, you've saved months.

Week 1: Foundation and Core AI Logic

Days 1-2: Define Your One-Thing

Every failed MVP I've seen tried to do too much. Your AI product should do one thing exceptionally well. Not three things adequately.

Ask yourself: "If my product could only have one feature, what would it be?" That's your MVP scope.

For LeoRix, the one thing was automated lead scoring. Not the CRM, not the pitch deck generator, not the project marketplace. Just: "Given this lead, how likely are they to convert?" Everything else came later.

Write down your one-thing and tape it to your monitor. Every feature request gets filtered through it.

Days 3-4: Choose Your LLM Stack

Here's where most founders overthink things. You don't need to train a custom model. You need to pick an API and start building.

My default stack for AI MVPs:

LLM selection criteria:

• Claude if your product needs nuanced analysis, long-context understanding, or handles sensitive content
• GPT-4o if you need vision capabilities, function calling, or broad tool integration
• Gemini if you're processing very long documents (1M+ token context)
• Open-source (Llama, Mistral) only if you have strict data residency requirements

For 90% of MVPs, Claude or GPT-4o through their APIs is the right call. Don't self-host models at the MVP stage.

Days 5-7: Build the Core Loop

Your MVP has exactly one loop: Input → AI Processing → Output. Build that loop and nothing else.

Here's the pattern I use:

1. Simple input form - collect the minimum data your AI needs
2. Server-side API route - call your LLM with a well-crafted system prompt
3. Output display - show the result in a clean, readable format

Skip authentication for now. Skip payment. Skip onboarding flows. You can add a simple password gate if you need to control access.

The system prompt is where your product lives. Spend 60% of your development time on prompt engineering. A mediocre UI with an excellent prompt beats a polished UI with a generic prompt every time.

Week 2: Polish, Deploy, and Get Users

Days 8-9: Add Just Enough UI

Your MVP needs to look credible, not beautiful. I use a consistent pattern across all my MVPs:

• Clean typography with a clear hierarchy
• One brand color plus grays
• Card-based layouts for structured output
• Mobile-responsive from day one (most early users will see your demo on their phone via a shared link)

Use a component library. Don't design from scratch. Tailwind CSS plus a few well-structured components gets you 80% of the way there.

Days 10-11: Error Handling and Edge Cases

AI products fail differently than traditional software. Your LLM will hallucinate, timeout, or return unexpected formats. Handle these gracefully:

• Timeout handling - set a 30-second max and show a retry button
• Output validation - check that the AI's response matches your expected structure before rendering
• Fallback responses - when the AI fails, show a helpful message, not a stack trace
• Rate limiting - prevent a single user from burning through your API budget

Days 12-13: Deploy and Share

Deploy to Vercel (or your platform of choice) with a real domain. First impressions matter. my-mvp.vercel.app says "side project." yourbrand.com says "real product."

Set up basic analytics. You need to know:

• How many people visit
• How many complete the core action
• What inputs they provide (anonymized)

Day 14: Get 10 Users

The hardest part of any MVP isn't building it - it's getting those first 10 users. Here's what works:

1. Post in relevant communities - LinkedIn, X, Reddit, Indie Hackers
2. Direct outreach - message 20 people who have the problem you're solving
3. Demo video - record a 60-second Loom showing the product in action
4. Ask for feedback, not sign-ups - "Would you mind trying this and telling me what's broken?" gets more responses than "Check out my new product!"

Common Mistakes That Kill AI MVPs

Building a wrapper. If your product is just "ChatGPT with a different UI," you don't have a product. Your AI needs proprietary context: your data, your domain expertise, your specific workflow.

Optimizing too early. Don't cache, don't batch, don't optimize prompts for cost until you have users who care. Premature optimization is the root of all evil, especially in AI products.

Ignoring the prompt. Your system prompt is your product's brain. Version control it. Test it. Iterate on it with the same rigor you'd apply to code.

Waiting for perfect data. Start with synthetic or manually curated data. You can build your data pipeline after you've validated the concept.

What Comes After the MVP

Once you've validated your core hypothesis, the roadmap becomes clearer:

1. Authentication and user accounts - now that people want to come back
2. Payment integration - Stripe takes an afternoon
3. Data pipeline - automate the data your AI needs
4. Feedback loops - let users rate AI outputs to improve over time

The two-week MVP isn't the end. It's the beginning of a build-measure-learn cycle that compounds over time.

I've used this exact playbook to build LeoRix, TradersHub Ninja, and several client projects. If you're a founder sitting on an AI idea, stop planning and start building. Two weeks from now, you'll either have validation or a valuable lesson - both are worth more than another month of planning.

Ready to build your AI MVP? Let's talk about your project - I help founders go from concept to launch every month.

Girish Kotte

AI entrepreneur, founder of LeoRix (FoundersHub AI) and TradersHub Ninja. Building AI products and helping founders scale 10x faster.

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