Fictional sample report
Sample AI App Failure Review
A typical review is designed to help agencies, builders, and founders decide whether a stuck AI-built app should be fixed, rebuilt, handed off, or stopped.
This is a fictional sample used to show the report structure. It does not describe a real client project.
Example project
A non-technical founder used an AI coding tool to build a small SaaS dashboard. The app works locally but fails after deployment. Login is unstable, database records are inconsistent, and the previous builder is unsure whether to keep patching or rebuild the app.
Executive judgment
The project should not receive more feature work until the authentication, database schema, and deployment configuration are isolated. The current issue is not one bug. It is a layered failure across app structure, auth state, and production configuration.
Failure layer
Primary layer: application architecture and data/auth boundary. Secondary layer: deployment/runtime configuration. Not primary: UI styling or isolated frontend bugs.
Root cause
The project was built through repeated AI-generated patches without a stable data model or clear separation between local demo behavior and production behavior.
Fix-or-rebuild decision
Short-term fix is possible only if the current scope is frozen. If the client expects additional features, a controlled rebuild may be safer than continuing to patch the existing structure.
Safe recovery sequence
- Freeze feature changes
- Confirm auth provider and database source of truth
- Reproduce the production failure
- Isolate deployment variables
- Create a minimal working path
- Decide whether to repair or rebuild based on the isolated failure
What not to do
- Do not add more features before isolating auth and database behavior
- Do not keep regenerating large blocks of code with AI
- Do not switch tools before confirming the actual failure layer
- Do not promise a delivery date until the production path is stable
Client-facing explanation
The app is not blocked by a single visible bug. The failure is coming from how authentication, database state, and deployment behavior interact. The safest next step is to pause new features, isolate the failing layer, and then decide whether a focused repair or controlled rebuild is more reliable.
Need this for a real project?
Send a project summary for a fix-or-rebuild failure review.