AI SQL Troubleshooter

Debugging long, unfamiliar SQL is slow, and it lands on a handful of people who know the codebase. Everyone else waits on them, so every broken query became a queue behind two or three experts. The bottleneck was not the SQL. It was that only a few people could safely touch it.

I wanted to widen the door without lowering the standard. The tool had to do more than spot a problem. It had to explain the fix clearly enough that a non-expert could apply it and trust the result.

So I built an AI-assisted reviewer that reads thousands of lines of SQL, points to the likely errors, and proposes concrete corrections with the reasoning attached. The reasoning is the point: a fix you cannot understand is one you cannot ship with confidence.

A reviewer that ingests a large SQL codebase, surfaces the probable errors, and returns specific corrections alongside an explanation of what was wrong and why the change fixes it. I built it for the team's real codebases and keep refining it against the cases that come up in practice, so the output stays useful rather than theoretical.

I built it around explanation, not just detection. Flagging an error is easy; the value is in returning a correction a non-expert can read, understand, and apply safely. Every choice about how results are presented followed from that bar.