How long does AI code refactoring typically take?

Most projects run 4 to 12 weeks of focused engineering work. A small SaaS app with one core service might be 4 to 6 weeks; a more complex app with multiple integrations and significant security issues can run 10 to 12. We commit to a specific scope and timeline after the audit, not before.

Can you fix the code without rewriting the whole product?

Yes - that's the default approach. We isolate what's broken from what's working, then refactor the broken parts in priority order. Full rewrites are rare and we recommend against them unless the architecture is fundamentally unsuitable for what the product needs to do.

Can AI tools fix code that other AI tools generated?

Tools that try to fix code AI generated do exist - some IDE extensions and linters suggest improvements automatically. They catch surface-level issues like style problems or obvious bugs. What they can't catch is structural problems, missing tests, security gaps in business logic, and decisions about what to refactor versus leave alone. Those require engineers who understand both your codebase and what production looks like.

Do you work with code from specific AI tools like Cursor, Lovable, Replit, or v0?

Yes, we've worked with output from all of them. The platform doesn't matter much - the issues we find are similar across tools because they all draw on similar training data and tend to produce similar patterns.

What does the audit cost, and what do we get?

Pricing is scoped to each project and shared after a short intake call. The audit deliverable is a written report covering security findings, architectural issues, test coverage gaps, deployment concerns, and a prioritized remediation plan with effort estimates.

Can you take over development entirely after the cleanup?

Yes - many clients move from cleanup into an ongoing engineering engagement. But we're not insistent about it. If you want us to fix the code and hand it back to your team, that works too.

How do we know we're a good fit for you?

Send us a short description of your situation through the form below. If we're not the right team for what you need, we'll say so and where useful suggest someone who might be.

AI Code Refactoring for Vibe-Coded Apps

What Code
Refactoring Means

In software engineering, the code refactoring meaning is straightforward: restructuring existing code without changing what it does from the outside. The user-facing behavior stays the same; the internal structure becomes cleaner, more efficient, and easier to maintain. The refactoring code meaning is identical - whether you say "refactoring code" or "code refactoring," it's the same engineering practice of reorganizing how code is written so that future changes are faster, safer, and less likely to break things.

AI code refactoring applies this discipline to code generated by AI tools like Cursor, Claude Code, Lovable, Replit, or v0. The challenge is specific: AI-generated code tends to "work" in a narrow sense - it runs, it returns expected output for typical inputs - while hiding structural problems that show up later under real usage. Refactoring that code means identifying those problems and reshaping the codebase into something a team can maintain and scale without firefighting every release.

You shipped something with AI. It works, sort of. Users find bugs you can't explain, the app slows down when more than a handful of people use it, and your developers spend more time fighting fires than building features. You don't need someone to tell you there's a problem - you need someone who can fix it without making things worse.

This page covers what AI code refactoring and cleanup actually involves, what we typically find in vibe-coded codebases, what changes after the work is done, and how to get from "this is a mess" to "this is something we can build on." If you're past the point of wondering whether your code has issues and you're looking for a concrete path forward, you're in the right place.