Services

AI-generated code cleanup

Turn a vibe-coded project into software you can safely ship and maintain.

I analyse, repair and clean up code produced through AI-assisted vibe coding—removing duplicated and contradictory implementations, fixing fragile behaviour, restoring clear structure and adding the verification needed to keep developing safely.

Initial scope: 20–30 focused hours* Typical delivery window: 2–4 weeks Scope confirmed after discovery Europe · International remote

* Typical range for an initial cleanup scope. Final effort and delivery window depend on codebase size, condition, access and critical flows, and are agreed before work begins. You pay for the defined cleanup and result—not for full-time weeks or an open-ended rewrite.

Describe the problem

When this is useful

For vibe-coded products that work—but are becoming difficult to trust or change

01

The demo works, changes do not

New prompts fix one behaviour but break another because the generated code has unclear boundaries and hidden dependencies.

02

The same logic exists several times

Repeated AI sessions have created duplicated services, competing abstractions, inconsistent patterns and dead code.

03

Production risk is unknown

Authentication, authorization, validation, data integrity, error handling and deployment assumptions have not been systematically reviewed.

04

There is no reliable safety net

Critical user flows lack meaningful automated tests, so neither a green build nor another AI-generated patch proves the product still works.

05

Nobody confidently owns the code

The founder or team can keep prompting changes, but cannot explain the system well enough to maintain, scale or hand it to another engineer.

Scope

What I examine and deliver

This is hands-on engineering work inside the codebase—not a style report. I first establish how the product actually behaves, then repair and clean the highest-value areas within an agreed scope.

  • Codebase, dependency and critical-flow analysis
  • Repaired defects in the agreed business-critical paths
  • Removal or consolidation of duplicated, dead and contradictory code
  • Refactored boundaries and repeatable implementation patterns
  • Automated tests around the behaviour most likely to break
  • Security, data-integrity, configuration and dependency corrections
  • Written handover and repository rules for safer future AI-assisted changes

Process

A focused path from evidence to action

  1. 01

    Reproduce and baseline

    Build and run the project, identify critical flows and establish what currently works, fails or cannot be verified.

  2. 02

    Trace and prioritize

    Map the generated structure and rank defects, duplication and risk by business consequence.

  3. 03

    Repair and clean

    Fix the agreed problems, consolidate code and introduce clear boundaries without rewriting useful working parts.

  4. 04

    Verify and hand over

    Add targeted tests, run the delivery gates and document how the project should be changed safely from this point.

Expected outcomes

Keep the useful product. Remove the vibe-coding debt.

Working critical flows

The agreed defects are repaired and the most important behaviour is protected by verification.

Cleaner, coherent code

Duplicated paths and accidental abstractions are reduced in favour of clear, repeatable patterns.

Safer continued development

The team can keep using AI without repeatedly losing control of architecture, quality and behaviour.

500+ tests

Repeated automated suites exceeding 500 tests used as delivery gates in AI-augmented engineering workflows, alongside repository research and human review.

Frequently asked questions

Practical questions before we start

Direct answers about fit, access, scope, delivery and working arrangements.

Do you only review the project, or do you actually fix the code?

I work directly in the codebase. The engagement includes analysis, an agreed set of repairs and cleanup, targeted tests and a verified handover. If the project is too large for one sprint, I first isolate the highest-risk scope instead of pretending everything can be cleaned at once.

Does it matter which AI tool created the code?

No. The service applies to projects created with tools such as Cursor, Claude Code, Codex, GitHub Copilot or similar agents. I evaluate the resulting system and behaviour rather than blaming a particular tool.

Will you rewrite the entire project?

Usually not. The goal is to preserve useful working behaviour, repair defects and simplify the parts that create risk. Replacement is limited to areas where cleanup would be less safe or more expensive than a focused rewrite.

Which projects are the best fit?

The strongest fit is a web, .NET, backend or cloud MVP with real business value that was built quickly through vibe coding and now needs to become stable enough for customers, further development or transfer to an engineering team.

Can we continue using AI after the cleanup?

Yes. The objective is controlled AI-assisted development, not removing AI. I leave clearer boundaries, tests and repository conventions so future agent-generated changes can be reviewed and verified.

Start with the real constraint

Your vibe-coded project does not need to stay a prototype.

Describe what the product does, what is currently breaking and what it needs to be ready for. I will suggest the smallest useful cleanup and stabilization scope.

Describe your engineering problem I usually reply within one business day.