From Vibe-Coded Prototype to Production: What Actually Breaks (and How We Fix It)

A founder can now go from idea to a working demo with Lovable, Bolt, v0 or Cursor in a weekend. That's genuinely useful — it validates an idea fast, and we use the same tools ourselves for early-stage builds. The problem shows up a few weeks later, once real users and real data arrive.

What AI-assisted prototypes get right

Speed to a clickable demo, and often a surprisingly reasonable first pass at UI. For validating product-market fit or pitching investors, that's exactly what's needed — nothing more.

Where they break under real usage

• No real data model. Prototypes tend to hardcode shapes that don't hold up once you need relationships, permissions or migrations.
• Security gaps. API keys in client code, missing auth checks, no rate limiting — fine for a demo, not fine once the URL is public.
• No architecture for change. Everything lives in one file or one giant component, so the tenth feature takes longer to add than the first.
• Performance falls over. Prototypes are rarely built with real load, pagination or caching in mind, and it shows the first time a client sends real traffic.

The rescue pattern we use

We don't throw the prototype away — that discards the product-market fit you already proved. We audit the codebase first, decide what's structurally sound versus what's a liability, harden security and data modelling, and rebuild only what needs rebuilding. It's the same pattern behind our Libero Health and Burnt engagements, just applied to AI-generated code instead of an older human-written codebase. The starting point is different; the discipline required to get to production isn't.