Every AI Platform We Rescue, Build On, and Ship From
The AI app development landscape moves fast. Lovable, Bolt.new, Cursor, Replit, Bubble, FlutterFlow — each promises to turn your idea into a working app. Most of them get you 70–80% of the way there. That last stretch is where projects stall: auth breaks in production, features the AI couldn't finish, deployment errors that loop forever, or a codebase that's grown beyond what the platform can manage. We specialise in that final stretch — and we've done it more than 300 times across 16 platforms.
Free written assessment within 1 business day. No commitment.
How we decide what a project needs
Every rescue starts with a 30-minute discovery call and a 48-hour written assessment. We read the codebase, reproduce the errors, and map out the exact gap between what was generated and what production needs. Only then do we give you a scope and a price — and you approve it before we start.
We don't rebuild unless we have to. If 80% of the project is solid, we fix the 20% that's blocking you. If the generated code has structural problems that make patching impossible, we tell you that too — and we quote a rewrite. Either way, you know the full cost upfront.
Our team has worked with AI-generated code since the earliest vibe-coding platforms arrived. We know where each tool excels, where it systematically fails, and which failure modes require custom backend work versus a configuration fix. That pattern recognition is what makes our assessments fast and our fixes reliable.
One of the most common things we hear from clients is "it worked perfectly on the platform but broke the moment we tried to go live." This is not a bug in your code — it's a fundamental mismatch between the sandbox environment the AI tool uses and the real-world infrastructure your app will run on. Supabase row-level security, environment variables, CORS headers, OAuth redirect URIs, and database connection pooling all behave differently in production. We bridge that gap every day, and we've built checklists and playbooks for each platform that make the transition predictable.
We also run into projects where the AI got caught in a loop — generating code to fix a bug, breaking something else, then generating more code to fix that, until the codebase is a layered tangle of patches. In those cases, assessment means untangling the change history, identifying the original structural issue, and fixing it cleanly rather than patching the patches. It takes longer than a simple environment fix, but it produces a codebase you can actually maintain after we hand it back.
AI code generators
These tools write real source code — React, TypeScript, Python, Node.js — that you own and can host anywhere. They're powerful for getting an app running quickly, but they hit walls when business logic gets complex, when production environment differences emerge, or when the AI's context window drifts across a long session. That's where we step in.
No-code builders and AI-assisted platforms
These platforms store your app logic in a proprietary editor and generate or manage code behind the scenes. They're fast to prototype in but often hit limits when you need custom business logic, performance at scale, or a feature the platform's component library doesn't cover. We extend, migrate, and launch apps from all of them.
Platform rescue difficulty at a glance
Rescue difficulty reflects how often AI-generated code from that platform needs structural changes rather than targeted fixes. Low means most issues are configuration or environment problems. Medium means some generated patterns need refactoring. High means we frequently rewrite critical modules — though we still preserve as much of the original as possible.
| Platform | Rescue difficulty | Most common issue | Typical hours to fix |
|---|---|---|---|
| Lovable | Medium | Auth and RLS errors in production | 8–20 hrs |
| Bolt.new | Medium | Build errors after export to local dev | 5–15 hrs |
| Cursor / Windsurf | Low | Bug loops from context-window drift | 5–12 hrs |
| Base44 | Medium | Platform limits blocking custom features | 10–25 hrs |
| Bubble | High | Slow page loads and workflow bottlenecks | 15–40 hrs |
| FlutterFlow | Medium | App Store rejection or custom code integration | 10–30 hrs |
| Replit | Low | 404 errors after deploy or environment mismatch | 5–15 hrs |
| Builder.ai | High | Delayed delivery and incomplete integrations | 20–60 hrs |
Common problems we fix across every platform
Regardless of which AI app development platform generated your project, the same failure modes appear over and over. These are the issues that consistently block launch across Lovable, Bolt.new, Cursor, Bubble, FlutterFlow, and every other vibe-coding tool we work with.
Auth and login not working in production
OAuth redirect URIs, session cookies, CSRF tokens, and Supabase RLS policies all behave differently on a real domain. We configure them correctly for your production environment so users can actually sign in.
App works in preview but breaks on deploy
Environment variable handling, build-time vs runtime configuration, and CORS headers are the most common culprits. We diagnose exactly what changed between the preview environment and your production host.
Stripe payments or webhooks failing
AI tools often generate placeholder Stripe code that works in test mode but fails with real keys, missing idempotency keys, or unverified webhook signatures. We get the payment flow working end-to-end with proper error handling.
Database RLS and permission errors
Row-level security policies generated by AI tools frequently contain logic errors that only surface when real users with real data hit edge cases. We audit and rewrite RLS policies so they're correct and auditable.
AI credit loops eating your budget
When a platform's AI runs into a bug it can't fix, it often regenerates code in a loop — consuming credits without making progress. We identify the root structural issue and fix it cleanly, ending the loop.
Custom integrations the platform can't build
Third-party APIs, complex webhook flows, real-time features, and multi-tenant data models routinely exceed what AI code generators can produce reliably. We build the custom backend logic your app needs to actually work.
Built on these technologies
AI code generators don't invent new tech stacks — they assemble existing ones. The platforms above almost universally output code in a small set of proven technologies, and that's exactly what we specialise in. Our engineers are senior-level practitioners in each one, which means we can read AI-generated code, identify structural problems, and fix them without starting from scratch.
Because we work across so many AI and no-code platforms, we see which technology combinations cause the most friction at production time. React with Supabase and Stripe is by far the most common stack we rescue. Next.js with Vercel edge functions is a close second. Python backends — usually FastAPI or Flask — appear frequently behind FlutterFlow mobile apps and internal tools built with UI Bakery or Bubble. Knowing these patterns in depth means our engineers don't need to figure out your architecture from scratch — they recognise it immediately.
TypeScript & modern CSS
AI tools love TypeScript but often produce loosely typed code that breaks at scale. We add strict typing, resolve type errors, and clean up Tailwind CSS output so it's maintainable long-term.
We also work extensively with Vue, Svelte, Angular, and Astro — as well as any database, auth provider, or third-party API your app depends on. See the full technologies overview for more detail on each stack we support.
Stuck on a platform not listed?
The AI development space launches new platforms every quarter. If your project was built with a tool that isn't listed here — Webflow, Framer, Dora, Glide, Softr, Pika, or anything else — there's a good chance we can still help. Our AI app rescue service is platform-agnostic: we assess the codebase or the exported output and tell you honestly what it will take to finish and ship it.
The same is true for apps that combine multiple platforms — for example, a Lovable frontend connected to a Replit backend, or a FlutterFlow app talking to a Bubble API. Hybrid architectures are actually where we spend a lot of our time, because no single platform's documentation covers the gaps between them.