Behind-the-scenes failure review
AI-built app stuck, broken, or hard to deliver?
Get a second technical judgment before wasting more development time on the wrong fixes. Built for AI agencies, no-code builders, automation consultants, and non-technical founders.
When an AI-generated app becomes unstable, unclear, or impossible to deliver, I help identify what actually broke, which layer caused the failure, and whether the safest next step is to fix, rebuild, hand off, or stop.
- Independent judgment
- Failure layer analysis
- Safe next step before more work
- Client-facing explanation when needed
For agencies and builders handling stuck AI projects
I help agencies and builders review stuck AI-built apps before they spend more time on the wrong fixes.
You keep the client relationship. I provide the technical review your team can use internally or adapt for client communication.
For AgenciesUse AINotWorking when
- An AI-built MVP becomes unstable before delivery
- Auth, database, API, payment, or deployment issues are mixed together
- Client requirements keep changing and the project is becoming hard to control
- AI-generated code is difficult to debug or safely extend
- Your team is unsure whether to fix, rebuild, hand off, or stop
- You need a clear technical explanation before promising more fixes
Common high-risk situations
Lovable prototype looks finished but is not production-ready
The demo flow works, but GitHub handoff, Supabase access, storage rules, deployment, and rollback are still unclear.
Supabase auth works but user data is not isolated
The UI may show the right records while RLS, storage policies, or RPC functions still expose data incorrectly.
Preview or AI builder changes may touch production
Vercel previews, environment variables, and generated database changes can blur staging and production boundaries.
AI-generated code keeps breaking old features
Repeated prompts may add duplicated logic, frontend-only checks, or conflicting implementations.
Founder is unsure whether to fix, rebuild, hand off, or stop
The safest next step depends on code structure, data model, production risk, and business urgency.
What I review
- Auth and user flows
- Database access and RLS
- Storage and file permissions
- Edge/API functions
- Deployment and environments
- AI-generated code risks
- Fix / rebuild / handoff decision
What you get
- Executive technical judgment
- Failure layer analysis
- Root cause summary
- Fix-or-rebuild decision
- Safe recovery sequence
- What not to do next
- Optional client-facing explanation
Featured fix-or-rebuild paths
Still having tool-level issues?
Tool outages and ChatGPT access issues still matter, but they are secondary to production risk when your AI-built app is heading toward real users.
Diagnosis
Need a fix-or-rebuild judgment?
Submit a stuck AI app for review before spending more time on the wrong fixes.
Second technical judgment · failure layer · safe next step