The Rapid AI MVP Playbook
Building an AI-powered MVP quickly requires a different approach than traditional software development. The AI component adds unpredictability — LLM behavior, data quality, and integration complexity all require specific mitigation strategies. Here's the playbook SpeedMVPs uses to ship in 2–3 weeks.
Step 1: One Core AI Interaction
Rapid AI MVP development starts with radical scope reduction. Identify the single AI interaction that delivers the most value: a document summary, a recommendation engine, an intelligent form, a voice interface. Build ONLY that first. Everything else is v2.
Step 2: Choose the Right LLM
Don't experiment with models during the build. Pick one and use it. Our defaults: GPT-4o for most use cases, Claude 3.5 Sonnet for long-context or document analysis, Gemini Flash for cost-sensitive high-volume applications. You can swap models post-launch once the product is live.
Step 3: Build the Minimal Stack
Next.js + Vercel + Supabase + Vercel AI SDK. This stack can support 99% of AI MVP use cases and has the fastest time from zero to deployed product. Don't add complexity until you need it.
Step 4: Parallel Development
Frontend and backend can develop in parallel if the API contract is defined in the discovery sprint. SpeedMVPs uses OpenAPI specs to let frontend and backend teams work concurrently — cutting total build time by 30–40%.
Step 5: LLM Quality Gates
Set up a basic eval suite in week 1. Test 20–30 example inputs and expected outputs. Run it on every build. This prevents the most common rapid development failure mode: shipping an AI product that works 70% of the time.
Ready to build your AI MVP in 2–3 weeks? Book a discovery call.
