• Kodus is an AI-powered code review platform aimed at enhancing software quality and streamlining development processes.
• The main product, Kody, functions as an intelligent code review agent integrated into platforms like GitHub, GitLab, Bitbucket, and Azure DevOps.
• It utilizes program analysis in conjunction with large language models to comprehend code context, architecture, and team standards.
• The platform provides actionable feedback on areas including performance, security, maintainability, and adherence to best practices.
• Developers can create custom review rules, incorporate external tools for better context, and choose preferred AI models.
• Kodus assists teams in monitoring technical debt, enforcing engineering standards, and ensuring project consistency.
• It offers flexible deployment options, either in the cloud or self-hosted.
AI-powered pull request review with contextual analysis
Integration with Git-based workflows and developer tools
Custom rule creation aligned with team coding standards
Support for multiple LLM providers and model selection
Technical debt tracking and issue generation automation
Architecture-aware feedback using contextual understanding
Security, performance, and code quality recommendations
Metrics dashboard for engineering productivity insights
Self-hosted and cloud deployment flexibility
Support for multiple programming languages and repositories
What is Kodus used for?
Kodus is an AI-driven platform that automates and enhances code reviews, helping teams improve code quality, detect issues earlier, and accelerate development workflows.
How does Kodus differ from traditional static analysis tools?
It combines program analysis with LLM-based semantic understanding, enabling context-aware feedback rather than purely rule-based checks.
Can Kodus integrate with existing development tools?
Yes, it integrates with Git-based platforms and developer tools such as GitHub, GitLab, Bitbucket, and Azure DevOps.
Does Kodus train its AI models using customer data?
No, customer code is processed securely and is not used for model training or retraining.
Can teams customize how code reviews are performed?
Yes, teams can define custom rules, select AI models, and adjust review behavior to match their standards and workflows.