This is where every legitimate cleanup engagement starts, and the quality of the audit determines the quality of everything that follows.

A vibe code cleanup specialist doesn't open the codebase and start refactoring whatever catches their eye first. They begin by building a complete picture of what they're actually dealing with — which is a harder task than it sounds when the codebase was generated without coherent architectural intent.

The audit has several components:

Architecture mapping

‍What does the system actually look like? How do the components relate to each other? Where are the data flows? What talks to what, and how? In a well-built codebase, this is usually documented and reasonably consistent with the code itself. In a vibe-coded codebase, the architecture is often implicit — meaning you have to infer it from the code rather than read it from documentation that doesn't exist.

Dependency analysis

‍What third-party libraries and services does the codebase depend on? Are they up to date? Are there unused dependencies adding attack surface? Are there version conflicts waiting to surface as runtime errors? AI coding tools frequently add dependencies for convenience without checking whether they're necessary, whether they're maintained, or whether they interact safely with other dependencies already in the project.

Security scanning

‍Static analysis tools — code scanners that examine the codebase for known vulnerability patterns without executing the code — run across the entire codebase to surface security issues. This is the category where the results of an audit most often produce an uncomfortable reading. AI-generated code consistently shows elevated rates of security vulnerabilities: SQL injection exposure, hardcoded credentials, missing authentication checks, improper data sanitisation, exposed API keys, and inadequate input validation. These aren't subtle problems. They're standard vulnerabilities that a security-aware developer would catch and avoid, but that an AI optimising for a functional response to a prompt has no particular reason to consider.

Performance profiling

‍Where are the bottlenecks? Which database queries are unoptimised? What's causing slow response times under load? AI-generated database queries are frequently unindexed, over-fetching, or structured in ways that work fine for ten records and become painful at ten thousand.

Test coverage assessment

‍What exists in the way of automated tests? The answer in most vibe-coded codebases is: very little. AI coding tools generate code, not tests. The absence of tests isn't immediately visible in a demo, but it means every change to the codebase is made without a safety net — which is why features breaking other features is such a consistent complaint.

Technical debt mapping

‍Beyond the acute problems — security vulnerabilities, performance issues — what's the accumulated structural debt? Duplicate logic scattered across the codebase. Monolithic components that have grown beyond any reasonable size. Inconsistent patterns for the same operations in different parts of the system. Naming conventions that reflect the sequence of prompts that generated the code rather than any coherent vocabulary.

The output of a thorough audit is a prioritised plan — not a wishlist, but a ranked roadmap that distinguishes what needs immediate attention from what can be addressed in a later phase, and what the right sequence of fixes looks like given how the components depend on each other.

This plan is the most valuable thing a cleanup specialist produces, and it's why the audit is worth paying for even if you end up not proceeding with the full cleanup. Understanding what you actually have is useful information regardless of who fixes it.

The audit produces a prioritised list, and security is almost always at the top of it. Not because the other problems aren't important, but because security vulnerabilities create liability that doesn't wait politely while the refactoring gets done.

Here's what cleanup specialists are actually fixing in this phase:

SQL injection vulnerabilities

AI-generated database queries are frequently constructed by concatenating user input directly into query strings — a pattern that experienced developers learned to avoid a long time ago but that AI tools reproduce reliably. The fix is parameterised queries, which separate the query structure from the data being inserted into it. This is straightforward to implement, significant in impact, and one of the most common findings in AI-generated codebases.

Authentication and authorisation gaps

Did the AI implement proper session management? Are API endpoints protected by appropriate authentication checks? Can a user access resources that belong to another user? These gaps emerge because AI tools implement authentication when explicitly prompted to do so, but don't necessarily audit the entire codebase for endpoints that should be protected and aren't.

Hardcoded credentials

Database passwords, API keys, and secret tokens that were placed directly in the code during development rather than managed through environment variables or secrets management. In a demo built quickly over a weekend, this is the kind of thing that doesn't seem urgent. In a production system accessible to the internet, it's a material risk.

Input validation

User inputs that are accepted, processed, and stored without adequate validation create attack vectors that go beyond SQL injection — cross-site scripting, path traversal, malformed data that breaks processing logic. Cleanup involves adding validation at every point where external data enters the system.

Dependency vulnerabilities

Third-party libraries with known security issues that have been added to the codebase and not updated. Automated tools like Dependabot or Snyk identify these; cleanup specialists address them — updating where possible, replacing where necessary.

Security remediation happens first and happens quickly, because these vulnerabilities are active risks while everything else is in progress. The structural refactoring can wait a few days. An exposed database password cannot.

This is the phase that takes the longest, requires the most skill, and produces the most visible transformation in the codebase.

AI coding tools generate code to satisfy individual prompts. They optimise for the immediate request without concern for how that code fits into the evolving system around it. The cumulative result is a codebase that grows through accretion rather than design — each piece added to satisfy a prompt, without reference to what came before or consideration of what comes next.

The structural problems this produces are remarkably consistent:

Duplicate logic

‍The same operation — calculating a total, formatting a date, validating an input — implemented multiple times in multiple places, each implementation slightly different because the AI generated fresh code for each prompt. When a requirement changes, every implementation needs to change. When a bug exists, it exists in all of them but gets fixed in one.

Monolithic components‍

Files and functions that have grown to encompass far more responsibility than they should — a single component handling data fetching, processing, state management, and rendering simultaneously, because the prompts that built it added responsibility incrementally without stopping to reorganise. Components that are hundreds or thousands of lines long, doing things that should be distributed across multiple smaller, focused pieces.

Unclear or absent separation of concerns

‍Business logic mixed into presentation components. Database operations scattered throughout the codebase rather than centralised in appropriate layers. Infrastructure concerns bleeding into application logic. The AI built what the prompt requested; nobody organised the result.

Inconsistent patterns

‍Different parts of the codebase implementing the same type of operation in different ways — not because there's a good reason for the variation, but because different prompts produced different implementations that were never reconciled.

‍

The refactoring process addresses these systematically, working through the prioritised list produced by the audit in controlled stages. The principle is surgical improvement rather than wholesale replacement — extracting duplicate logic into shared functions, decomposing monolithic components into focused ones, introducing consistent patterns for common operations.

Crucially, this happens in small, controlled changes rather than large-scale rewrites. Each change is made, tested against existing behaviour, and merged before the next begins. The goal is to improve the structure without breaking the functionality — which requires both technical care and discipline in resisting the temptation to "just fix everything at once."

The output of structural refactoring is a codebase that still does what it did before, but is organised in a way that makes sense to a human developer — where components have clear responsibilities, where logic is where you'd expect to find it, and where changing one thing doesn't require understanding and updating ten others.

Once the structure is cleaner, performance issues become easier to diagnose and address — partly because the code is more readable, and partly because refactoring often resolves performance issues that were downstream of structural problems. What's specifically addressed in this phase:

Database query optimisation

This is the most common source of performance problems in AI-generated codebases. Queries that fetch more data than needed. Queries without appropriate indexes that work acceptably with small datasets and become agonisingly slow at production scale. N+1 query patterns — where a loop executes a database query for each iteration rather than a single query for all — that look efficient in development and reveal themselves under real data volumes.

Caching strategy

What should be cached? Where? For how long? AI-generated code rarely has a coherent caching strategy because caching requires system-level thinking that individual prompts don't produce. Adding appropriate caching layers — for expensive database queries, for computed results, for static assets — can dramatically improve performance without changing the underlying logic.

API response times

Endpoints that are doing more work than necessary before returning a response. Synchronous operations that should be asynchronous. Missing pagination on endpoints that return arbitrarily large data sets. These are addressable once the code is structured well enough to make the issues visible.

Resource management

Connections that aren't closed. Memory that's allocated and not released. File handles that remain open. These are the kinds of issues that don't show up in a demo with a fresh environment but accumulate in a long-running production system.

This is the phase that pays dividends indefinitely, and it's also the one that most founders are least focused on when something is going wrong in production. Tests feel like slowing down. In a codebase that's already fragile, they're the opposite.

A vibe-coded codebase without tests is a codebase where every change is made blind. Nobody knows whether a new feature has broken an existing one until a user finds out. Developers slow down — not because they're incapable, but because they're moving carefully through a minefield without a map.

Cleanup specialists add test coverage in layers:

Unit tests

‍Testing individual functions and components in isolation. Does this function produce the right output for a given input? Does it handle edge cases correctly? Does it fail appropriately when it receives invalid data? Unit tests are fast to run, fast to write when the code is well-structured, and provide immediate feedback when a change breaks something.

Integration tests

‍Testing how components work together — does the user creation flow work end-to-end? Does the payment processing integration behave correctly? Integration tests catch the problems that unit tests miss because they test the boundaries between components rather than the components themselves.

End-to-end tests

‍For user-facing flows, automated tests that simulate real user behaviour — visiting a page, filling in a form, completing a purchase. These are slower to run and more brittle than unit tests, but they provide confidence that the product works for real users in real scenarios.

‍

The test coverage isn't added all at once — it's added alongside the refactoring, covering each component as it's cleaned up. This approach means the refactoring and the tests evolve together, producing a codebase where the tests reflect the actual structure rather than the pre-refactoring state.

This is the phase that future engineers will thank someone for, and that nobody is ever enthusiastic about at the time.

A vibe-coded codebase is documented in the sense that the prompts that generated it could theoretically be reconstructed. This is not useful documentation. Useful documentation is what allows a developer who didn't write the code to understand it, modify it, and extend it without requiring the original developer to explain every component.

What cleanup specialists produce in the documentation phase:

‍

Architecture documentation

‍A clear description of how the system is structured — what the major components are, how they relate to each other, where the data flows, and what the key architectural decisions were and why. This is the thing that makes onboarding a new developer a week-long process rather than a month-long one.

‍

API documentation

‍Every endpoint: what it does, what inputs it expects, what it returns, what errors it can produce. This is especially important for systems that other systems integrate with, and it's almost universally absent from vibe-coded codebases.

‍

Deployment documentation

‍How does this get from development to production? What environment variables are required? What infrastructure does it depend on? What's the deployment process? This knowledge often lives only in the head of the person who set it up — which is fine until that person isn't available.

‍

Code comments

‍Not comments that describe what the code is doing (that should be readable from the code itself) but comments that explain why — why this approach was chosen over an alternative, what the constraint was that produced a particular implementation, what the non-obvious implication of a change would be.

A thoroughly cleaned-up codebase is not a different product. It's the same product — doing the same things, behaving the same way for users — built on a foundation that can actually support what comes next.

Practically, that means:

Developers can add features without spending the first week understanding why the codebase is structured the way it is. Changes to one part of the system have predictable effects — rather than mysterious consequences in components that shouldn't be connected. Performance holds under real load rather than degrading past the point of usability as traffic grows. Security reviews don't produce uncomfortable conversations. New engineers can be onboarded in days rather than months. And the product can be demonstrated to investors or enterprise clients without the underlying infrastructure being a liability in the conversation.

None of that is glamorous. It doesn't make for an exciting launch post. But it's the difference between a product that survives and one that collapses under its own momentum — which is, increasingly, one of the most important distinctions in the startup landscape.

We work with founders and engineering teams who've built fast with AI coding tools (as well as low-code tools) and need to make what they've built production-ready.

Our cleanup process follows the structure described here: audit first, always; security remediation before structural changes; refactoring in controlled stages that don't halt active development; test coverage alongside the refactoring; documentation at the end. We don't recommend a full rewrite unless the audit genuinely shows it's necessary — and we'll tell you clearly which applies and why.

The developers who do this work have been through our AI bootcamp — which means they understand how AI coding tools work, what the patterns of AI-generated code look like, and where the problems reliably appear. That context makes the audit faster, the refactoring more targeted, and the overall process more efficient than it would be for a team approaching AI-generated code without that specific experience.

If you're sitting on a vibe-coded codebase that's getting complicated — the useful first step is a conversation about what you have, followed by an audit that tells you what you actually need.

‍

Octogle Technologies works with founders and engineering teams to turn AI-built prototypes into production-ready products — through structured audit, security remediation, refactoring, and testing. Tell us what you have and we'll tell you what it needs.