A founder came to us with a problem we're seeing more and more: they'd built an app with Lovable, shown it to investors, gotten positive feedback — and then watched it fall apart when real users showed up.

The app looked great. The demo was impressive. But under the surface, it had every problem AI-generated code tends to have. This is the story of how we rescued it.

What the Founder Built

The product was a health tech platform connecting patients with specialists through a matching algorithm. The founder — non-technical, deep domain expertise — had used Lovable to build a working prototype in about three weeks. It had user profiles, a matching flow, messaging, appointment scheduling, and a basic admin dashboard.

For a three-week prototype, it was remarkable. The founder showed it to early users and advisors. Everyone was excited. An angel investor wrote a check based on the demo.

Then they tried to onboard their first 50 users.

What Went Wrong

The problems showed up fast:

  • The app crashed under modest load. With 20+ concurrent users, page loads stretched to 8-10 seconds. Some requests timed out entirely. The database queries were unoptimized — full table scans on every request, no indexing, no caching.
  • User data was leaking between sessions. Due to how state was managed (or rather, not managed), one user would occasionally see fragments of another user's profile data. In health tech, this is a compliance nightmare.
  • The matching algorithm didn't actually match. It returned results, but the logic was essentially random with a plausible-looking sort. The AI had generated code that looked like a matching algorithm without implementing real matching logic.
  • No authentication security. User passwords were stored in plain text. API endpoints had no rate limiting. There was no session expiration. A basic security audit revealed 14 critical vulnerabilities.
  • Every new feature broke something else. The codebase had no separation between components. Changing the messaging feature broke appointment scheduling. The founder's developer couldn't figure out why.

Our Assessment

We started with a 3-day code audit. The goal: figure out what's worth keeping and what needs rebuilding. Here's what we found:

Worth keeping:

  • The UI design and user flows — they were well-thought-out and validated with real users
  • The business logic specifications (what the matching algorithm should do)
  • Asset files — icons, images, brand elements
  • The database schema concept (the relationships made sense, even if the implementation didn't)

Needed rebuilding:

  • The entire backend — no recoverable architecture
  • Authentication and authorization — security vulnerabilities throughout
  • The database layer — schema needed restructuring for performance and HIPAA compliance
  • State management — the root cause of the data leakage
  • The matching algorithm — needed to be written from scratch with real logic

Our recommendation: Path 2 — rebuild the backend, keep the design. The frontend design was solid. The backend was unsalvageable.

What We Built

We rebuilt the backend in Ruby on Rails with a Flutter frontend, using the original Lovable app as a detailed design spec.

Week 1-2: Foundation

  • Set up Rails API with proper authentication (bcrypt password hashing, JWT tokens, session management)
  • Designed a normalized database schema with proper indexing
  • Implemented HIPAA-compliant data handling (encryption at rest and in transit, audit logging, access controls)
  • Set up CI/CD pipeline with automated testing

Week 3-4: Core Features

  • Built the actual matching algorithm based on the founder's domain expertise (weighted scoring across 12 criteria, not the random sort the AI had generated)
  • Implemented user profiles, messaging, and scheduling with proper state management
  • Added error handling and graceful degradation for every API endpoint

Week 5-6: Polish and Launch

  • Rebuilt the Flutter frontend, preserving the original design but with clean architecture
  • Load tested to 500 concurrent users — page loads under 200ms
  • Security audit: zero critical vulnerabilities
  • Deployed to AWS with auto-scaling and monitoring

Total timeline: 6 weeks. Total cost: roughly $45K — significantly less than the $80K+ a ground-up build would have cost, because we weren't designing from scratch.

The Results

  • Performance: 8-10 second load times → under 200ms
  • Security: 14 critical vulnerabilities → zero
  • Stability: Crashes under 20 users → stable at 500+ concurrent
  • Data integrity: User data leakage → proper session isolation with HIPAA compliance
  • Matching accuracy: Random results → weighted algorithm with 87% user satisfaction score in first month

The founder went back to their angel investor with a real product. They've since raised a seed round and are onboarding healthcare providers.

The Patterns We See Every Time

This story isn't unique. We've now rescued a dozen+ apps built with AI coding tools (Lovable, Cursor, Bolt, Replit). The failure patterns are remarkably consistent:

  1. No authentication security. Plain text passwords, missing rate limiting, no session management. Every single time.
  2. No error handling. Happy path works beautifully. Anything else crashes the app.
  3. Fake business logic. The code looks like it implements the algorithm, but it doesn't actually work. The AI generates plausible-looking code that passes a glance but fails under scrutiny.
  4. State management chaos. Data leaks between users, forms lose input, navigation breaks state.
  5. Impossible to extend. Adding feature B breaks feature A because there's no architecture separating them.

Should You Still Use AI Coding Tools?

Yes — but with the right expectations.

AI coding tools are the best prototyping technology ever created. A non-technical founder can validate a product concept in days instead of months. That's genuinely transformative.

The mistake is treating the prototype as the product. Use AI tools to:

  • Validate the idea. Build a prototype, show it to users, learn what works.
  • Create a detailed spec. The prototype IS your specification document. It shows exactly what the app should do.
  • De-risk fundraising. Show investors a working demo, not a slide deck.

Then bring in professionals to build the production version. The prototype makes this faster and cheaper because you're not starting from zero — you already know what to build.

We've written a detailed guide on what to do when your AI-built app hits a wall, including how to evaluate the three paths forward (refactor, rebuild backend, or full rebuild).

If you've built something with AI tools and it's hitting production problems, let's take a look. We'll give you an honest assessment — including whether you actually need us or can fix it yourself.