Turn a fragile AI-generated prototype into software your business can actually run on.
Most AI-generated prototypes work beautifully in the demo and start breaking the moment real users, real data volume, or a real security review shows up — not because the idea was wrong, but because tools built for getting to a working screen fast aren't optimized for the data modeling, auth, and error handling production actually needs. We audit what's genuinely there, fix what's actually broken, and rebuild only the parts that need it, so you're not throwing away the weeks of validated product decisions already baked into the prototype.
Where this fits
It worked in the demo, not in production
Fine with five test users and clean data, then timeouts, silent failures, or wrong results once real traffic and real edge cases show up.
No tests, and nobody's sure why anything works
Changing one feature breaks another in a way nobody can explain, because there's no test suite telling you what the app is actually supposed to do.
A security review just flagged a dozen issues
Exposed API keys, missing input validation, and auth checks that only exist in the frontend — common in code generated fast, not reviewed carefully.
You've outgrown the AI builder platform itself
Migrating off a no-code/AI app builder into a real, portable codebase your own engineers — or any engineers — can actually own and extend.
A non-technical founder built the MVP, and it's time for engineers
The product decisions are validated and worth keeping — the codebase underneath them needs a team that can take it from here.
What we deliver
The AI Wall audit
A structured architectural review that maps exactly where the prototype's data model, auth, and error handling will break under real load, before you commit budget to a rebuild.
Data model hardening
Loose, implicit, or duplicated data structures replaced with a real relational schema built for the queries your app will actually run at scale.
Authentication & authorization rebuilt properly
Real, server-enforced auth instead of client-side checks and hardcoded keys — the single most common production risk in fast-generated code.
Test coverage from zero
A real test suite added where none existed, so the next change doesn't silently break a feature three screens away.
Security remediation
Exposed secrets, missing input validation, and injection risks fixed as a first pass, not an afterthought once something's already gone wrong.
Incremental refactor, not a rewrite from scratch
We keep the validated UX and product decisions already proven by the prototype, and replace only the parts that are structurally broken.
Production infrastructure & monitoring
A real deploy pipeline, error tracking, and logging added, so the next issue surfaces on a dashboard instead of in a user's bug report.
How we run this engagement
The AI Wall audit
Map exactly what's fragile, what's fine, and what's genuinely broken in the current codebase.
Triage & roadmap
Prioritize fixes by actual production risk, not by what's easiest to change first.
Refactor & harden
Rebuild the data model, auth, and test coverage incrementally, with continuous preview deploys.
Launch & monitor
Ship with real observability in place, and iterate based on production data instead of guesswork.
Built on a proven, modern stack
Next.js
A common target architecture when the prototype's frontend logic is worth keeping and restructuring around.
TypeScript
Types that surface the implicit assumptions AI-generated JavaScript often leaves undocumented.
PostgreSQL
Replaces loose or ad hoc data storage with a schema built for real query patterns and integrity constraints.
Docker
Makes the app's environment reproducible instead of dependent on whatever configuration happened to work locally.
Sentry
Real error tracking, so failures surface immediately instead of being discovered by a user first.
AWS
Production-grade infrastructure to replace whatever hosting got the prototype to a demo link.
Things people ask before starting
Can't find what you're looking for? Reach out and we'll answer directly.
The AI Wall audit maps this explicitly — we keep whatever's structurally sound and genuinely rebuild only what's fragile, rather than defaulting to either 'rewrite everything' or 'patch everything,' both of which are usually wrong.