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Vibe coding is useful because it lowers the cost of starting. A founder can describe what they want, iterate with an AI tool, and get to a working interface with surprising speed.

That speed is not the problem. The problem is what often gets skipped while the product is taking shape.

Technical debt does not always look like bad code at first. In AI-built products, it often looks like momentum. The app works, the demo is impressive, and each new prompt seems to add another feature. Then a real user arrives, a customer asks for a change, or the team prepares to launch, and the product starts fighting back.

The demo path is not the production path

Most early AI-built apps are optimized around visible behavior:

• Can the user sign in?
• Can they create a record?
• Can the page show the right data?
• Can the model respond?
• Can the workflow appear complete?

Those are important, but production asks harder questions:

• Are permissions enforced everywhere?
• Are API keys and private data protected?
• Can the app handle malformed inputs and edge cases?
• Can the team safely change one feature without breaking another?
• Can errors be monitored and diagnosed?
• Can the deployment be rolled back?
• Can the product support real customers without manual heroics?

When those questions were never part of the build, the debt becomes visible after the demo works.

The debt patterns we see

AI-generated apps often accumulate the same kinds of problems.

First, logic gets duplicated. Similar checks appear in multiple files. Similar components behave slightly differently. The app has features, but not a stable internal model.

Second, data boundaries get blurry. A prototype may not clearly separate user data, workspace data, admin access, test records, private integrations, and public-facing output.

Third, security is implied instead of designed. Auth may exist, but authorization is inconsistent. Tool access may work, but not be scoped. Generated code may call APIs without enough validation.

Fourth, the product has no reliable change path. Tests are thin or nonexistent. Deployments are manual. Error logs are incomplete. Nobody is sure which parts of the app are safe to touch.

Fifth, AI features introduce new risk. Prompt injection, sensitive information disclosure, excessive agency, unbounded usage cost, and unreliable model output all need practical controls.

Why feature creep gets worse with AI

AI makes adding one more feature feel cheap. That changes founder behavior.

A new idea comes in. A user asks for something. A competitor has a feature. The tool can probably generate it. So the product grows.

The hidden cost is not only development time. It is product clarity. Every feature adds states, permissions, support cases, analytics needs, UI decisions, and maintenance burden. When the app is still trying to find its market, too many features can become avoidance. The team feels productive while postponing the harder question: which users are we actually winning?

This is why cleanup should be tied to product strategy. Refactoring for its own sake is not the goal. The goal is making the product stable enough to learn from real users.

Cleanup is not always a rewrite

A fragile AI-built app does not automatically need to be thrown away. Sometimes the right move is targeted hardening:

• Tighten auth and authorization.
• Fix data model assumptions.
• Remove duplicated business logic.
• Add tests around critical flows.
• Separate prototype-only shortcuts from launch-critical paths.
• Add monitoring, backups, and deployment discipline.
• Review AI prompts, tools, data exposure, and output controls.

Other times, a rebuild is cheaper than preserving a confused foundation. The point of a senior review is to make that call clearly.

LOJI's vibe-coded app cleanup work starts with that distinction: what can stay, what needs to be refactored, what should be rebuilt, and what matters most before launch.

What to review before launching an AI-built app

Before putting real users on the system, review the parts that create the most exposure:

• User roles, permissions, and tenant boundaries.
• Data storage, backups, migrations, and deletion flows.
• Payment and billing paths.
• Third-party integrations and API credentials.
• LLM prompts, retrieved context, tool calls, and logs.
• Admin surfaces and support workflows.
• Error handling, monitoring, and rollback plans.
• The first three customer support scenarios you expect after launch.

If the app already has users, include their behavior and support issues in the review. Technical cleanup should follow the product reality, not just the code structure.

How LOJI fits

LOJI helps teams move from AI-built momentum to production reality. That can mean AI prototype to production, AI app security review, or cleanup of a fragile codebase before the product becomes harder to change.

The first step is an AI App Launch Readiness Audit. Bring the repo, prototype, known bugs, launch timeline, user feedback, and next roadmap pressure. We will help separate urgent cleanup from optional polish and decide whether the next phase is hardening, refactoring, rebuilding, or post-launch product support.