We think AI Code Review is about to hit a wall. Are we wrong?
Over the past few months, my co-founder and I have been building an AI-powered code review platform.
During development, we kept running into the same question:
Why are we trusting a single LLM to approve code that will eventually run in production?
Today, most AI code review tools rely on one model to evaluate everything:
- Security
- Performance
- Architecture
- Code quality
- Maintainability
- Best practices
That feels increasingly risky.
Human teams don't work like that. We rely on specialists, peer reviews, and different perspectives before merging critical code.
So we started experimenting with a different approach.
Instead of asking one AI to review a Pull Request, we orchestrate multiple specialized AI reviewers, each responsible for a different domain, and then aggregate their findings into a single review.
The hypothesis is simple:
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We're not trying to build another coding assistant.
We're trying to answer a different question:
How do we know AI-generated code is actually safe to merge?
I'd genuinely love feedback from experienced engineers.
Some questions I'm curious about:
- Do you trust AI reviews enough to merge without reading the code?
- Have you seen AI reviewers miss obvious issues?
- Would multiple specialized reviewers provide more confidence, or just more noise?
- Where do you think AI code review is heading over the next few years?
If anyone is interested, we've documented our thinking and the architecture behind what we're building here:
I'd really appreciate honest criticism. If we're wrong, I'd rather learn now than six months from now.