The Problem Was Simple But Scary
A SaaS company reached out to us last year. Their product was live, customers were using it, but things kept breaking. Not in obvious ways. Random errors. Weird behavior in production that nobody could predict. The kind of stuff that makes you lose sleep.
Here's what happened. Their team had been moving fast. Really fast. They used AI tools to write code, which is fine. Lots of teams do it now. But here's the catch. About 70 to 80 percent of their codebase came straight from AI suggestions. The developers would get code from ChatGPT, Claude, Gemini or Copilot, copy it in, test it quickly, and ship it.
It worked. Until it didn't.
What We Found When We Looked Inside
We spent the first two weeks just reading their code. The application was complex. Multiple services talking to each other, database calls everywhere, API integrations with third party tools. On paper, everything should have worked fine.
But AI generated code has a pattern. It looks clean. The syntax is correct. It even has comments. But it misses the bigger picture. We found functions that did too many things. Database queries that weren't optimized. Error handling that only covered the happy path. Security checks that existed in some places but not others.
The team knew something was wrong. They just didn't know where to start fixing it.
Three Months of Actual Work
We didn't rewrite everything from scratch. That would have been a disaster. Instead, we worked with their team for three months. This wasn't a handoff project. We sat with them, reviewed code together, and fixed things step by step.
Month 1: Understanding and Planning
We mapped out the entire application. Which parts were critical. Which parts were causing the most problems. We created a priority list based on what was breaking in production and what posed security risks.
Month 2: Cleaning and Restructuring
This is where the real work happened. We took the messy parts and restructured them. Separated concerns. Made sure each function did one thing well. Updated outdated dependencies. Added proper error handling. Fixed the database queries that were slowing everything down.
The team worked alongside us. This was important. They needed to understand why we were making each change.
Month 3: Education and Maintenance Systems
The last month was about making sure this didn't happen again. We showed the team how to use AI tools properly. How to review AI generated code before shipping it. How to keep dependencies updated. How to write tests that actually catch problems.
We set up automated checks that would flag common issues. We created documentation that explained the new structure. We made sure everyone understood the why behind the changes, not just the what.
What Changed After We Were Done
The results were clear. The application stopped having random production issues. Performance improved because we optimized the slow queries and removed unnecessary calls. The team could add new features faster because the code was organized properly.
But the bigger change was in how the team worked. They still use AI to write code. That's not the problem. The problem was never the AI. It was the lack of review and structure. Now they know what to look for. They understand which AI suggestions are safe and which ones need human judgment.
Their codebase is also secure now. We fixed the security gaps that automated scanners miss. Updated everything to current versions. Closed the holes that could have led to data breaches or system failures.
The Real Lesson Here
AI tools are great. They speed up development. But code that goes into production needs human oversight. Not just a quick glance. Real review by someone who understands architecture and security.
If your team is building fast using AI tools, that's fine. Just make sure someone is actually reading what gets shipped. Because when things break in production, it costs money, time, and trust.
Most teams don't realize they have a problem until it's too late. Random bugs. Slow performance. Security issues that only come up under specific conditions. By then, fixing it costs more than doing it right the first time.
When You Should Think About Code Cleaning
You probably need to look at your code if any of these sound familiar:
- Production bugs that don't make sense
- Features that take way longer to build than they should
- Your best developers complaining about the codebase
- Security audits finding issues your tools missed
- Performance problems that keep getting worse
- Fear of updating dependencies because things might break
These aren't normal growing pains. They're signs that technical debt is piling up.
What Comes Next
If your application is running on mostly AI generated code, get it reviewed. Not next quarter. Now. Before a security issue becomes a news headline. Before performance problems lose you customers. Before your development costs spiral out of control.
The team we worked with caught their problems early enough to fix them without a complete rewrite. That saved them months of work and probably their business.
Your code is the foundation of everything you do. If that foundation has cracks, nothing built on top of it will be stable.
Need Help?
If this story sounds familiar, we should talk. We've done this before. We know how to fix these problems without disrupting your business or your team.
Get a consultation: Contact Bithost at bithost.in/code-architecture-cleaning
We'll review your code, show you what's actually broken, and give you a real plan to fix it. No sales pitch. Just honest assessment from people who've been in the trenches.
Your future self will thank you for dealing with this now instead of later.
