TLDR

• Korbit automatically reviews every PR in GitHub, GitLab, and Bitbucket and can be triggered on demand with /korbit-review. Reviews can run incrementally on new commits.
  
• Feedback arrives as categorized “Issues” with clear explanations and fixes. You can run Essential or Comprehensive modes and tune signal with Adaptive reviews.
  
• Korbit can auto-write PR descriptions that capture the “what” and “why,” and you can resolve all Korbit comments with a single command when you’re done.
  
• See comments in VS Code, Cursor, and JetBrains, plus suggested fixes where enabled. Korbit Docs
  
• Roll-up reporting with Insights and an exportable Issues view makes audits and leadership reporting easy.
  
• Policies (Max plan) let you encode your own standards and surface “Policy” violations right in PRs and the console. ‍
• Security: SOC 2 Type II, zero-day retention with OpenAI and Anthropic, encrypted in transit, secure review flow, and your code never trains our models. You keep ownership of your code and suggestions.

Why enterprises are standardizing on AI code reviews

Teams are shipping more code, not fewer bugs. The review queue becomes the bottleneck, security debt piles up, and audit evidence doesn’t write itself. AI review gives you a consistent first pass on every PR, so your senior engineers can focus on the hard calls rather than style nits. With Korbit, you install once for GitHub, GitLab, or Bitbucket, and reviews run automatically on open or updated PRs, or on demand with a simple command. 

Beyond linters and static analysis

Linters catch formatting and basic anti-patterns. AI review reasons about intent, risk, and behavior. Korbit flags issues across categories like Functionality, Security, Performance, Error Handling, Readability, Logging, and Design, and it explains what it found, where, and why it matters, with remediation suggestions your team can act on. 

You can also generate clear PR descriptions that capture the “what” and “why” of a change, which speeds up human review and onboarding. 

Minimize noise, keep the context

The knock on AI tools is “too noisy.” Korbit’s settings and learning loops directly address that. You can choose Essential or Comprehensive scope, adjust tone, and enable Adaptive reviews so the system uses your team’s thumbs-up, thumbs-down, and resolution patterns to surface what you actually care about over time. Incremental reviews on new commits keep feedback tight without flooding people. 

For especially tricky areas, use Policies to enforce team-specific rules that linters can’t, like architectural boundaries or API usage conventions, and see violations labeled as “Policy” alongside normal findings. 

Adoption signals and a simple pilot plan

You’re ready if you have active PR flow, CI, and an exec sponsor who wants measurable outcomes. A clean pilot looks like this: pick one busy repo, run Korbit for two weeks, and baseline three things, then compare after rollout:

1. PR cycle time
   
2. Issues per PR and resolution rate
   
3. Production regressions tied to reviewed areas
   
   

Korbit’s Insights reports make this painless, turning PR activity into clear dashboards for managers and execs. 

What to measure, and how

• Cycle time
  Track merge timestamp minus PR creation timestamp. Break down by PR size to see where AI helps most. Insights includes PR duration and trend views for exactly this use case.
  
• Escaped defects
  Watch post-release bugs per feature. Use Korbit’s Issues and export capability to support audits and retros.
  
• Consistency across repos and teams
  Measure patterns like recurring logging, error handling, or design issues. Essential vs Comprehensive modes plus Policies let you tune the signal.
  
  

Security and governance you should expect

This is table stakes for enterprise:

• SOC 2 Type II compliance, with report access in the Trust Center.
  
• Zero-day retention with OpenAI and Anthropic, TLS in transit, and a secure review flow where Korbit retrieves only the relevant files, posts comments back to your Git provider, then clears the code used for the review. Your code is never used to train models.
  
• Ownership stays with you, including suggestions generated during review.
  
  

Implementation blueprint for large teams

Plan success metrics in the console, set review scope and tone.
Integrate GitHub, GitLab, or Bitbucket in a few clicks.
Tune with review modes, Adaptive reviews, and Policies.
Measure with Insights dashboards and Issues exports.
Roll out from one repo to many, with commands and scheduling to fit your workflow.

Bonus quality-of-life: developers can trigger reviews with /korbit-review, generate or refresh PR descriptions, and even resolve all Korbit comments when a PR is ready to land. 

Language coverage

Korbit reviews about 30 languages today, including C, C++, C#, Java, JavaScript and TypeScript, Kotlin, Python, Ruby, Go, Swift, Rust, Vue, Shell, and more by default, plus experimental support for CSS, SQL, Terraform, Dockerfiles, HTML, and others you can toggle in Labs. 

Why Korbit leads on “context”

Korbit’s review engine analyzes diffs with surrounding context and project conventions, then posts actionable issues to your PR and console. The secure flow and proprietary issue-detection chain are built for enterprise expectations, not hobby repos. 

FAQ

What’s the best way to reduce code review errors without overwhelming devs?
Run AI as a consistent first pass, then let humans focus on high-judgment topics. Use Essential mode to keep the signal tight, enable Adaptive reviews, and consider Policies for the rules you debate every sprint. 

Can we enforce our own standards?
Yes. Policies encode your team’s guidelines into enforceable checks that show up in PRs as “Policy” issues, with examples and a schema validator to keep rules clean. 

Will this work in our IDEs?
Your developers can see Korbit’s comments right inside VS Code, Cursor, and JetBrains, and apply suggested fixes where enabled. 

How do we report results to leadership or auditors?
Use Insights for team and developer reports, and export Issues for audits or trend reviews. 

Does Korbit support our stack?
Most likely. Check the Supported Languages list, and toggle experimental languages in Labs as needed. If something’s missing, you can still review broader concerns like error handling, logging, and API usage with Policies. 

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