Case Studies
Every engagement we take starts with a broken deployment. This is a documented account of the patterns we see, the conditions that create them, and what a professional resolution looks like.
Failure Taxonomy
These are not edge cases. They are the predictable output of applying AI code generation tools to production infrastructure problems the tools were never designed to solve.
Symptom
The application builds and runs perfectly on a local machine. The moment it is pushed to a cloud deployment container, it crashes immediately. Error logs reference variables that are clearly defined in the codebase but somehow unavailable at runtime.
Root Cause
AI builders generate code that references environment variables but never establish the boundary between local development secrets and production deployment configuration. The two environments require distinct variable scoping — a distinction no AI tool currently handles automatically.
Business Impact
Complete deployment failure. Founders spend hours adding variables manually, in the wrong scope, to the wrong environment, without understanding why none of it works.
Symptom
Navigation works on some routes and silently fails on others. Pages load blank with no visible error. Server logs show 404s on routes that clearly exist in the codebase.
Root Cause
Modern Next.js uses two fundamentally different routing systems — Pages Router and App Router — with incompatible conventions. AI generators frequently produce hybrid outputs that mix both patterns, creating routing conflicts that are invisible to the builder but fatal in production.
Business Impact
Partial application functionality at best. Authentication flows, API routes, and dynamic pages are the most commonly affected. Debugging without knowing the root cause consumes days.
Symptom
User data is accessible to unauthenticated requests. Admin endpoints respond to public API calls. Sensitive records appear in browser network tabs without authentication.
Root Cause
AI-generated backends frequently provision a database with zero Row-Level Security policies. The application may appear to function correctly in local testing while being completely open to arbitrary read and write operations from anyone with the project URL.
Business Impact
A live security vulnerability. Depending on the data stored, this ranges from a compliance violation to a critical breach risk.
Symptom
Users log in successfully and are immediately redirected to a login screen. Session cookies are written and discarded within the same request cycle. Protected routes are either always accessible or always blocked regardless of authentication state.
Root Cause
AI tools frequently generate authentication scaffolding that is structurally correct but environment-specific. JWT signing keys, callback URLs, and session cookie domains all require production-specific configuration that development builds never validate.
Business Impact
The authentication layer is the security perimeter of the entire application. A broken auth flow means every user-facing feature gated behind a login is either inaccessible or unsecured.
Symptom
The CI/CD pipeline triggers on every commit but fails at the build step. Error messages reference missing modules, type mismatches, or configuration files that exist in the repository but are not found by the build container.
Root Cause
AI-generated projects frequently include configuration files that work in a local environment with specific global dependencies installed but fail in a clean, isolated build container where only the declared project dependencies are available.
Business Impact
No deployments ever complete. The development loop breaks entirely — every code change must be tested locally with no production validation path.
Case 001 — Confidential · Lovable Build Rescue
The Situation
A non-technical founder built a complete SaaS product using Lovable — a well-known AI application builder. The interface was functional. The user flows were polished. The application had genuine value.
What the AI builder did not provide — and what no AI builder currently provides — was any path to production infrastructure. No repository. No deployment configuration. No database. No environment management.
After ten consecutive hours of attempting self-directed deployment, the founder had made no measurable progress. Every attempt produced a different error. Each error felt like it was the final obstacle. None of them were.
What Was Actually Broken
The audit identified four distinct infrastructure failures, each one independent, each one sufficient to prevent deployment on its own.
Stack Transformation
| Layer | Before | After |
|---|---|---|
| Framework | Lovable (AI-generated) | Next.js 14 App Router |
| Database | None | Supabase PostgreSQL |
| Auth | Broken mock session | Supabase JWT Auth |
| Hosting | Local only | Vercel (Production) |
| Version Control | None | GitHub + CI/CD Pipeline |
| Delivery | — | Under 24 hours |
Delivery Timeline
Client submitted an AI-generated app after spending 10+ hours completely stuck in deployment loops. Codebase lacked version control, database mapping, and production configuration.
Infrastructure blueprint mapped and approved: Initialize Git version control, provision cloud database schemas, and establish a continuous hosting deployment pipeline.
Codebase structurally migrated to a secure GitHub repository. Cloud database provisioned with active schemas, and continuous pipeline connected.
Application fully live and operational on a production URL. Client went from 10 hours of failure to a functioning deployment in under a day. Client Quote: "How did you do that? I've been working on this for 10 hours."
“How did you do that so fast? I've been hitting a wall with this setup for over 10 hours straight.”
Client, Anonymized · Lovable Build Rescue · June 2026
The Pattern
The client was not inexperienced. The problem was not solvable through more effort or better prompting.
The failures were structural — built into the output of the AI tool itself. No amount of documentation reading, forum searching, or trial-and-error would have resolved all four simultaneously without a systematic infrastructure audit. This is the gap between AI-generated code and production-ready software. It is not a skill gap. It is an infrastructure gap. And it is resolvable.
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