Octogle was built around AI-native development. Every developer we employ or place has been through our AI bootcamp — intensive training on how AI coding tools work, what they produce, where their outputs are reliable and where they aren't, and how to leverage AI tooling to build better rather than faster at the expense of quality.

That background is specifically relevant to vibe code cleanup — because understanding how AI-generated code fails requires understanding how AI coding tools think. The failure patterns in vibe-coded codebases aren't random. They're predictable consequences of how large language models produce code: optimising for satisfying the immediate prompt rather than for the coherence of the system those prompts will eventually become.

Knowing those patterns means we find the problems faster, prioritise them more accurately, and fix them more cleanly than a generalist development team approaching AI-generated code without that specific context.

We've also built our own products using AI-native development workflows — WorkChats, BeeKey, Corpwise, TheCareApp, and others. We understand the difference between AI-assisted development that produces something robust and AI-assisted development that produces something that works in a demo and fails in production. We know where the line is, because we've built deliberately on the right side of it.

This is where every Octogle vibe code cleanup engagement starts. Without exception.

We don't open your codebase and start our process for fixing vibe code. We open your codebase and start understanding it — which in a vibe-coded system is a more substantial undertaking than it sounds, because the architecture is often implicit rather than documented and the component relationships are frequently opaque even to whoever built it.

Our audit produces five things:

A complete picture of what you actually have

‍Architecture, data flows, dependencies, component relationships. Drawn from the code itself rather than from your description of what the code should do, because these are frequently different things.

A security vulnerability report

‍Every significant vulnerability we find, categorised by severity, with clear explanation of what each one is, why it matters, and what fixing it involves. In our experience, this section of the audit report is the one that produces the most immediate concern — not because we're trying to alarm you, but because AI-generated code consistently carries security issues that aren't visible in normal use and become very visible in abnormal use.

A performance assessment

‍Where are the bottlenecks? Which queries are unoptimised? Where does the system slow down under load, and why?

A technical debt map

‍The structural problems: duplicate logic, monolithic components, inconsistent patterns, unclear responsibilities. Prioritised by the impact they're having on development velocity and system stability.

A prioritised cleanup roadmap

‍Not a wishlist of everything that could be improved — a ranked plan of what needs doing, in what order, based on risk and impact rather than on what's easiest or most technically interesting to fix.

The audit takes one to two weeks. It costs something — we'll discuss what that looks like for your specific codebase in the initial conversation. The cost is credited against the cleanup engagement if you proceed.

The audit report is yours. If you take it to another development team, use it to inform an internal engineering effort, or simply want to understand what you're dealing with before making any decisions — that's entirely legitimate and we'll produce it with the same thoroughness regardless.

The audit produces a prioritised list. Security is almost always at the top of it, and this is not a coincidence.

Security vulnerabilities in a live codebase are active risks. They don't wait politely while the structural refactoring gets done. A SQL injection vulnerability that exists in your codebase today is a vulnerability that exists while we're working on everything else — which is why security remediation is phase one of the active cleanup, not an item on the structural backlog.

In practice, in vibe-coded codebases, this means addressing:

SQL injection vulnerabilities

Unparameterised database queries that accept user input directly are one of the most consistent findings in AI-generated code. The fix is clear and implementable quickly — parameterised queries that separate the query structure from the data being inserted into it.

Hardcoded credentials

‍Passwords, API keys, and secrets placed directly in the codebase during development rather than managed through environment variables. These need to be removed from the code, rotated (on the assumption they've been exposed), and replaced with proper secrets management.

Authentication and authorisation gaps

‍Endpoints that should require authentication and don't. Resources that should be accessible only to their owner and aren't. Session management that's weaker than it should be. These gaps are often introduced because AI tools implement authentication when explicitly prompted but don't audit the full system for places where authentication is required and missing.

Input validation

‍User inputs being processed without adequate validation create attack vectors beyond SQL injection — cross-site scripting, path traversal, malformed data that breaks processing logic downstream.

Dependency vulnerabilities

‍Libraries with known security issues that have been added and not updated. We use automated scanning tools to identify these and work through remediation — updating where the update path is clean, replacing where it isn't.

We don't consider the security phase complete until we've addressed everything above severity threshold, confirmed the fixes are in place, and rerun the security scan to verify. The goal isn't a reduced vulnerability count — it's a clean result.

This is the phase that takes the longest and requires the most skill. It's also the phase that produces the most meaningful transformation in what the codebase is like to work with.

Our approach to structural refactoring is deliberate about the order and the granularity of changes. The temptation in a disordered codebase is to see everything that needs fixing at once and want to address it all simultaneously. This approach, in our experience, reliably makes things worse before they get better — and in a codebase that's already fragile, "worse before better" means production instability that your users experience.

We work in controlled stages. Each stage addresses a specific structural problem, in a dedicated branch, tested against the existing system's behaviour before being merged. The cleanup work happens in parallel with your active development rather than instead of it — we coordinate with your team to avoid conflicts, but your product stays live and your roadmap continues.

The structural problems we address, roughly in order of their impact on development velocity and system stability:

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Extracting duplicate logic

‍Finding the same operation implemented multiple times in multiple places and consolidating it into shared functions. This sounds simple and in some cases is. In vibe-coded codebases with significant duplication, it's a careful exercise in identifying which of the many implementations is the right one to keep, ensuring all callers are updated correctly, and verifying that the consolidated version handles all the edge cases that the individual implementations were handling differently.

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Decomposing monolithic components

‍The files and functions that have grown to enormous size by absorbing responsibility that should be distributed. We break these apart into focused components with clear, single responsibilities — which makes the system easier to understand, easier to test, and significantly easier to extend.

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Introducing consistent patterns

‍Where different parts of the codebase implement the same type of operation differently, we establish and apply consistent patterns. Not because consistency is aesthetically pleasing, but because inconsistency creates cognitive overhead for every developer who touches the codebase — they have to learn multiple ways of doing the same thing rather than learning one way and applying it everywhere.

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Clarifying component boundaries‍

Business logic that's wound into presentation components. Database operations scattered throughout the application rather than centralised in appropriate layers. Infrastructure concerns bleeding into application logic. We separate these — not by imposing a rigid architectural pattern for its own sake, but by organising the code in ways that reflect what each component is actually responsible for.

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Throughout this phase, we're writing tests alongside the refactoring — covering each component as it's cleaned up. This serves two purposes: it verifies that the refactored component behaves correctly, and it leaves the codebase with test coverage that didn't exist before.

Structural refactoring often surfaces performance issues that were previously obscured by the disorder — it's easier to see what's slow when the code is readable. Performance optimisation is where we address what the audit identified and what the refactoring revealed.

The most common findings:

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Database query problems

‍Queries that fetch entire tables when they need a handful of rows. Missing indexes on columns that are frequently filtered or sorted on. N+1 query patterns — where a loop executes a query for each iteration rather than a single query for all iterations — that are invisible in development with small datasets and painful in production with real ones. We address these systematically, starting with the queries that are on the critical path for user-facing operations.

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Missing caching

‍Expensive computations or database queries that are repeated on every request when their results could be cached. We implement caching at the appropriate layer — application-level, database query-level, or HTTP response-level — depending on the nature of the data and the staleness tolerance of the use case.

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Unnecessary work per request

‍API endpoints doing more processing than they need to before returning a response. Synchronous operations that should be handled asynchronously so the user isn't waiting. Missing pagination on endpoints that return unbounded result sets. Each of these is a straightforward fix once identified.

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We validate performance improvements with load testing — confirming that the system behaves as expected not just under normal conditions but under the kind of traffic spikes that reveal the difference between code that works and code that scales.

Vibe-coded codebases almost universally have inadequate test coverage, because AI coding tools generate functionality rather than tests. Adding that test coverage is one of the most valuable things a cleanup engagement produces — not because tests are interesting to write, but because they fundamentally change the risk profile of every subsequent development decision.

Without tests: every change is made against the backdrop of "I think this is right and I hope it doesn't break anything." With tests: every change is made with a mechanism that will immediately tell you if something's broken.

We add test coverage in layers:

1. Unit tests cover individual functions and components in isolation — verifying that each piece does what it's supposed to do with the inputs it's supposed to receive, and handles edge cases correctly.

2. Integration tests cover how components work together — testing the boundaries between pieces of the system rather than the pieces themselves. These catch problems that unit tests miss because the problems exist at the interfaces.

3. End-to-end tests for the core user journeys — the flows that matter most to your product. Automated tests that simulate real user behaviour and verify that the critical paths work end-to-end.

We don't aim for 100% test coverage as a numerical target — that's an expensive way to produce a lot of tests for code that doesn't need them. We aim for meaningful coverage of the critical paths and the highest-risk areas, with the discipline to add tests for new functionality as development continues.

Documentation is the last thing anyone wants to write and the first thing everyone needs when they're trying to understand a codebase they didn't build. In a vibe-coded system, where the architecture was never documented because it was generated rather than designed, this gap is particularly significant.

We produce:

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Architecture documentation

‍A clear description of the system — what the major components are, how they relate, where the data flows, and what the key decisions were. This is what makes onboarding a new developer a reasonable process rather than an archaeological expedition.

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API documentation

‍Every endpoint: what it does, what it expects, what it returns, what errors it can produce. Essential for internal use and for any external integrations.

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Inline code comments

‍Not describing what the code does — that should be readable from well-written code. Documenting the why: the constraints that produced a particular implementation, the non-obvious implications of a change, the decisions that were made deliberately rather than incidentally.

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Deployment and operations documentation

‍How the system gets from development to production, what infrastructure it depends on, what environment variables are required, and what the monitoring and alerting setup looks like.

A codebase that does everything it did before — same functionality, same user experience — on a foundation that can actually support what comes next.

Practically: your developers can add features with confidence that the test suite will catch unintended consequences. New team members can be onboarded in days rather than months. Security reviews don't produce uncomfortable conversations. The system holds under real traffic. Changes to one part of the codebase have predictable effects rather than mysterious ripple effects elsewhere.

And an honest assessment of what the ongoing engineering practice should look like to prevent the same problems from accumulating again — because a cleaned-up codebase is only a cleaned-up codebase if the practices that produced the mess in the first place are replaced with ones that don't.

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Initial conversation

‍We talk about what you've built, what's happening that shouldn't be, and what you need the codebase to be able to do. No commitment required — just enough context for us to tell you whether we're the right fit.

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Scoped audit

‍We agree the scope of the audit, confirm the access we need, and begin. One to two weeks. You receive a full report: security findings, performance assessment, technical debt map, and prioritised cleanup roadmap.

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Decision point

‍You review the audit report. You decide whether to proceed with the cleanup, to address specific high-priority items, or to take the report elsewhere. If you proceed with us, the audit cost is credited against the engagement.

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Cleanup engagement

‍Fixed scope, fixed price, defined deliverables. We work through the roadmap in stages, coordinating with your team to avoid disrupting active development. You have visibility throughout — regular updates, access to the branch we're working on, and the ability to review progress at each stage before we move to the next.

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Handover

‍We walk you through what was done and why, what the codebase looks like now versus before, what the test suite covers, and what the documentation contains. We're available for questions after handover — not abandoning you at the point of delivery.

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Optional ongoing support

‍For teams who want continued help managing technical debt as the product grows, we offer a retainer model — a defined number of engineering hours per month, applied to whatever the codebase needs at that point.

If you've built something, or are thinking about building something, with AI coding tools and you're at the point of knowing it needs professional attention — the sensible starting place is showing us what you've got.

We'll have an initial conversation. If it looks like an audit makes sense, we'll scope that and get started. You'll have a clear picture of what you're dealing with and a prioritised plan for addressing it before any significant commitment is made.

Get in touch and tell us what you've built. We'll take it from there.

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Octogle Technologies provides vibe code cleanup and AI-generated code remediation for founders and engineering teams in the UK, UAE, and US. We start with the audit. We fix what needs fixing. We leave you with something you can actually build on. Tell us what you've built.