Vibe coding is fine for a prototype. It is not a system. Five places it breaks the moment it meets real customers, and what it actually takes to fix each one. We do not throw the code away. We harden it.
37+
BX1X production modules, multi-year, no drift
15+
Years shipping production software
3+
Years applied AI, day-to-day
100+
Verified client reviews
You vibe-coded the MVP. It demos beautifully. You have customers waiting and no one to make sure the production version does not embarrass you.
The freelancer is gone. The code works for the happy path. You cannot extend it. You cannot debug it. You cannot get a second opinion fast.
You built it with Cursor or Claude Code in a sprint. It is now business-critical. Keeping it alive has become a part-time job for the wrong people.
API keys in the frontend bundle. Secrets in the git history. Endpoints with no auth. Database queries built from raw user input. The vibe-coded app gets found by automated scanners within days of going public.
Every change creates two new bugs because nothing has clear boundaries. The codebase is functional, not structural. See /fixes/vibe-coding-structure.
Light use is fine. Real load reveals race conditions, missing indexes, unbounded queries, and concurrency bugs the prototype never exercised. See /fixes/vibe-coding-scaling.
Every change is fragile because nobody trusts the code enough to extend it safely. The next change costs four times the original build. See /fixes/vibe-coding-maintenance.
The original LLM session is gone. The engineer who joined yesterday cannot orient. The bug stays open for days. See /fixes/vibe-coding-support.
The SEO AI Toolbox on this site is six months of iteration, deterministic eval gates, and 22 articles of governed output. The same engineer who hardens your vibe-coded prototype is the engineer who built that. Pro-AI and governance-first is not a contradiction. It is the only sustainable position.
The hardening sprint is the typical engagement. The existing AI-built code is not thrown away. It is audited, restructured where needed, and given the missing scaffolding (tests, types, deployment, monitoring, secrets handling).
No account managers. No handoffs. You work directly with Anton from first conversation to final handover.
Each article walks the symptom, the diagnosis, and the engagement I would propose for that named failure mode. Pick the one closest to your situation.
Your AI-built app is alive. Every new change breaks something. Every fix takes longer than the last. Maintenance debt compounds weekly. Here is the diagnostic method, the catalogue of debt patterns, and the stabilisation engagement I would propose.
Your AI-built app handled five users beautifully. At fifty it slows. At five hundred it corrupts data. The LLM wrote the happy path under no concurrency. Here is the catalogue of scaling failure modes, the diagnostic method, and the production-hardening sprint I would propose.
Your AI-built app works. The codebase has no coherent boundaries, no naming convention, and no rules for how change happens. Every fix introduces two new bugs. Here is the diagnostic method, the architecture work, and how I would approach the cleanup.
The original LLM session is gone. The freelancer left. A bug breaks the app at 2 a.m. and the new developer takes a week to orient. Here is the diagnostic method and the takeover engagement I would propose to make a vibe-coded app supportable.
Your AI-built app demos beautifully. Nobody has checked whether it is safe to put in front of real customers. Here is the catalogue of gaps AI-generated codebases ship with, the diagnostic method I use to find them, and how I would approach the hardening sprint.
No. It is great for prototypes. It becomes a problem the moment the prototype starts taking real customer money or storing real customer data.
Usually yes. The hardening sprint audits the existing code, restructures the parts that fight every change, and adds the scaffolding the AI did not write. The goal is to keep what works.
Sometimes. The assessment tells you which path is cheaper. Usually it is hardening, not rewrite.
You do. Documented handover is part of the engagement.
No. Each hardening sprint is scoped to a named outcome with a defined end.
Same engagement, same approach. The audit does not blame; it produces a path forward.
Security gaps the AI never closed. Code structure that fights every change. Scaling failures the demo never showed. Maintenance debt that compounds weekly. A support gap when nobody can read the code fast enough at 2 a.m. The fix is not to throw the code away. It is to audit it, harden it, and add the engineering scaffolding the AI did not write.
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