You shipped fast.
Now let's fix it.
You used AI tools to move quickly — and it worked. Now the codebase is tangled, brittle, and nobody wants to touch it. We audit, refactor, and get it production-ready without burning it down.
Codebase audit
We read your AI-generated code from top to bottom and document what's broken, what's redundant, what's dangerous, and what can actually be kept.
Architecture cleanup
Restructuring the folder layout, module boundaries, and data flow so the codebase has a shape that a human can navigate and extend.
Dead code removal
AI tools generate a lot of code that was tried and abandoned. We strip it out so the working logic isn't buried under layers of experiments.
Security hardening
Vibe-coded apps frequently have hardcoded secrets, missing input validation, and open endpoints. We find and fix these before they become incidents.
Test coverage
Adding a test suite to the parts of the codebase that matter — so future changes have a safety net and you're not deploying blind.
Documentation
A README and inline comments that explain what the code actually does — so your next developer (or future you) doesn't start from zero.
We read it before
we change anything.
The first thing we do is read the entire codebase without touching it. This sounds obvious but most contractors skip it and start "fixing" things they don't understand. We write down what we find, share it with you, and agree on what to fix before writing a single line.
We don't rewrite what doesn't need rewriting. The goal is a codebase your team can own and extend — not a fresh rewrite from us that creates a new set of problems. We make the smallest changes that achieve that goal.
- Written audit of the current codebase state
- Refactored codebase with improved structure
- Security issues identified and fixed
- Test suite for critical paths
- Deployment pipeline if not already in place
- Documentation and handoff guide
AI can write code faster than ever. Cleaning it up is still a human job.
The pace at which teams are shipping AI-generated code has outrun the tooling to evaluate it. LLMs write code that works — in the happy path, with the inputs they were designed for, on the day it was generated. What they don't do well is write code that's secure under adversarial input, performant at scale, maintainable by a team that didn't write it, or consistent with the architectural decisions made three weeks ago.
The result is codebases that are technically functional but fragile. A refactor that a human engineer would have flagged gets accepted because the tests pass. A security issue that's obvious to someone who knows OWASP gets missed because the model didn't have that context. A pattern that made sense for the first feature gets copy-pasted across the next fifteen, creating a maintenance burden that grows faster than the product does.
We come in as a second set of eyes — reading the codebase the way a senior engineer would on day one at a new job. What's inconsistent? What's slow? What's dangerous? What's going to break when traffic is 10× what it is today? We document what we find, prioritize the work, and either hand you a remediation plan or execute it ourselves.
Common questions
Everything you need to know before getting started.
Vibe coding is the practice of using AI tools — Cursor, GitHub Copilot, Claude Code, ChatGPT — to generate large amounts of code quickly with minimal manual review. The term has become shorthand for any AI-assisted codebase that was built fast and may have accumulated technical debt or quality issues as a result.
It doesn't need to be a disaster. We work with teams who just want a senior engineer's read on a codebase before it goes into production, all the way to teams who've shipped something and it's showing symptoms — slow response times, intermittent bugs, failed security review. Either case is worth a conversation.
Almost never. A full rewrite is usually the wrong answer — it takes longer than expected, the new code has different bugs, and you lose the institutional knowledge embedded in the old code. Our default approach is targeted refactoring: fix what's broken, extract what's reusable, document what's confusing, and leave the rest alone.
TypeScript, JavaScript (React, Next.js, Node.js), Python, Rust, Go, and SQL. We also work with Astro, SvelteKit, and most modern web frameworks. If your stack isn't on this list, ask — we'll tell you honestly whether we have the coverage to help.
A written audit report covering what we found — categorized by severity and type (security, performance, maintainability, architecture) — with specific file and line references. If we're doing remediation, you get the cleaned-up code plus documentation of what changed and why.
Ready to make your codebase ownable?
Share what you've got. We'll audit it and tell you honestly what it needs — no obligation.