Most AI code review tools I’ve seen hook into pull requests. That works fine if your workflow is strictly PR-based, but in fast-moving or solo founder setups, code often goes straight to main.



I wanted to see if it was possible to run AI checks *before deploy* without requiring a PR. The idea was simple, but the challenges weren’t:

* **Context is messy**: running on every push to prod meant capturing the right diff and linking it back to the product ticket. Without PR metadata, I had to figure out how to still give meaningful feedback.
* **Performance**: you can’t slow down deploys by 10 minutes just because an LLM is thinking. I had to cache analysis and run async steps while keeping the developer experience fast.
* **Signal vs noise**: LLMs love to over-report. Balancing useful warnings (edge cases, security risks, style inconsistencies) with avoiding “false alarm fatigue” took lots of trial and error.
* **Integration trade-offs**: building something that works in CI/CD pipelines vs directly on a local workflow raised interesting questions about where devs actually want feedback.

In the end, I got it working in a way that checks code right before deploy, surfaces issues, and doesn’t depend on PRs at all. It’s still rough around the edges, but the technical lessons alone were worth it.



Curious if anyone here has tried experimenting with AI-driven reviews outside the usual PR flow. What worked for you, and what didn’t?

What I learned building an AI code review tool that runs before deploy (without PRs)