Korbit AI

Code review is where quality is won or lost, and also where senior engineers lose hours and pull requests go stale waiting. Korbit AI takes the first pass so humans arrive at a cleaner PR.

It is an automated reviewer that works inside GitHub, GitLab and Bitbucket, flagging bugs, security and design issues, explaining how to fix each one, and writing the PR description and summary. It sits in the same space as review and quality tools like Sourcery, but is built around the pull-request conversation specifically.

This review covers what Korbit does on every PR, where it pays off for a team, how its pricing tiers work, and when it adds little.

Korbit AI Code-Review Facts

Korbit AI, The Short Read

• Korbit AI is an automated code reviewer: it reviews every pull request inside GitHub, GitLab and Bitbucket before a person does.
• It explains, not just flags: it describes the issue, suggests the fix, and responds to your comments in the PR.
• It does the paperwork too: PR descriptions, review summaries and team insights like release notes and sprint recaps.
• Your code stays yours: it states it is SOC 2 Type 2 and never trains any model on your code, with free use for open source.

An Automated Reviewer That Lives In Your Pull Requests

Korbit's design choice is to meet code where review already happens: the pull request. Rather than a separate dashboard, it comments inside the PR, so its feedback lands in the same place a human reviewer's would.

That placement is why it fits real workflows. Engineers do not have to go anywhere new; on each PR Korbit posts issues with explanations before a teammate even opens it, which means the human reviewer spends their time on judgement, not catching the obvious.

The company leans on a credible proof point: it shows live sample findings from real open-source repositories, so you can see the kind of issue it catches rather than take the claim on trust. That is a more honest demo than a polished reel.

Korbit meets code where review already happens, the pull request, so the human reviewer spends their time on judgement, not on catching the obvious.

What Does Korbit AI Do On Every PR?

The value is in combining the review itself with the surrounding paperwork that usually gets skipped.

Finding Issues And Explaining The Fix

Korbit flags high-impact issues across bugs, security and design, and crucially explains how to fix each one rather than just naming it. It also responds to developer comments inside the PR, so it behaves more like a reviewer than a linter.

The explain-the-fix behaviour is what makes it a training aid as well as a gate, especially for junior developers. A dedicated security scanner like Snyk Code goes deeper on vulnerabilities specifically; Korbit's breadth across issue types is its own strength.

Summaries, Insights And Asking The Codebase

Beyond the review, Korbit writes PR descriptions and summaries for faster handoffs, and generates team insights, release notes, sprint recaps, 1:1 summaries, with no extra work. An 'Ask Korbit' feature lets you chat with your codebase.

That reporting layer is where it starts to resemble engineering-intelligence tools like CodeScene, though Korbit's insights are a by-product of the review rather than the main event. For a lead, the automatic summaries are a quiet but real time-saver.

Where Korbit AI Pays Off For A Team

Korbit earns its place for teams where review is a bottleneck: fast-moving startups, teams with more PRs than senior reviewers, or anywhere pull requests sit waiting. It gives every PR an immediate first pass.

It is also a genuine training tool. Junior developers get instant, explained feedback and learn from it, which several of Korbit's own customers single out as the real benefit, not just bugs caught but skills built.

It matters less for a solo developer or a tiny team where a human reviews everything quickly anyway, though even there the auto-generated PR descriptions and the no-training-on-your-code pledge may appeal.

How Korbit AI Tiers Its Pricing

Korbit uses per-user subscription tiers, with Pro and a higher Max tier that adds things like custom policies, plus an Enterprise plan with on-prem options, billed monthly or annually, and a free trial of the top tier. Open-source repositories are free. We do not publish prices; check the site.

The value question for a team is whether the reviewer time it frees, plus the bugs caught before production, justifies a per-seat cost. Korbit's own figures, like the share of its suggestions that get implemented, are vendor-stated and best validated on your repos.

Because it is free for open source and offers a trial, the low-risk path is to run it on real code and measure the signal-to-noise before committing a team.

A Practical Way To Trial Korbit AI

A review bot lives or dies on signal versus noise, so judge it on real pull requests, not a demo repo.

1. Connect Korbit to a real repository on GitHub, GitLab or Bitbucket.
2. Let it review several genuine PRs and read whether the issues are real and high-impact.
3. Check the false-positive rate, the thing that makes teams ignore a review bot.
4. Read a Korbit-written PR description and summary to judge the handoff quality.
5. Have a junior developer act on the explained fixes and see if they learn from them.
6. Pass/fail: Korbit is worth it if it catches real issues without burying them in noise.
7. Confirm the SOC 2 and no-training commitments satisfy your security team before rollout.

Korbit AI Code-Review Terms

Pull request (PR)

A proposed set of code changes submitted for review before being merged; the unit Korbit reviews.

False positive

A flagged issue that is not actually a problem; a high rate is what makes teams stop trusting a review bot.

Custom policy

A team-defined coding rule Korbit can enforce on reviews, available on its higher tier.

No-training pledge

Korbit's stated commitment that none of your code is used to train any model, ever.