RhodeCode now brings AI-assisted code review directly into your development workflow, giving enterprise teams the ability to connect to ChatGPT, Gemini, Claude, or a custom AI model and receive automatic analysis of pull requests inside RhodeCode.

This new capability allows teams working with Git, Mercurial, or SVN to improve review speed, detect issues earlier, and raise code quality while keeping all reviews within existing access rules and permissions.

Enterprise users can configure the feature in the Settings panel and connect the AI reviewer using their own API keys. Teams can assign an AI reviewer to a pull request, let it analyze diffs, generate contextual comments, and customize review prompts for specialized cases.

What are AI Code Reviews?

AI code reviews are code analysis using ML and NLP. The models study large data sets and can find common errors, complex logical failures, and security issues. The goal is simple: to improve code quality, increase security, and reduce the time developers spend on routine checks. AI for code review tools helps teams reduce review time and achieve consistent results without unnecessary routine tasks.

The tool analyzes code in different languages, considers the context, compares it with best practices, and provides a detailed review. This helps avoid recurring issues, reduces the number of issues, and makes development predictable. Teams get faster feedback, and developers can focus on logic and functionality. AI review code is suitable for both small projects and enterprise-level projects. It helps reduce the risk of security issues, optimizes performance, and allows you to open pull requests faster.

Key steps in AI Code Reviews

The AI code review process consists of clear steps. Each adds value and forms a complete picture of code quality. First, the tool analyzes the codebase and creates a structure for further work. Then, the model studies patterns and finds deviations from development standards. In the final stage, the code audit tool prepares feedback and recommendations.

The main steps are as follows:

1. Initial analysis of the code, broken down into blocks.
2. Search for patterns and comparison with standards.
3. Identification of errors and issues.
4. Formulation of recommendations and possible changes.
5. Improvement of the model thanks to new reviews.

The AI powered code review format makes it possible to analyze large code fragments and find problems even before opening a pull request. After completing these steps, the developer receives a clear list of problems, explanations of the causes, and possible ways to improve. This speeds up work on tasks and reduces the load on the team.

Key capabilities of AI code review tools

Modern AI code review tools cover a wide range of tasks. They help control errors, handle security risks, and enhance software performance. Thanks to integrations, automation, and real-time feedback, AI tools are becoming part of the daily workflow. Before using them, it is important to understand the key capabilities that make AI code review useful.

Integrations and platform support

AI code review tools connect to IDE, CI, and issue tracking systems. Many teams actively use AI code review GitHub because it easily integrates into their usual workflow. Some companies combine on-premises processes with cloud services, such as AWS AI Code Review, to extend automation and analytics.

This creates a unified workflow where all reviews, issues, and fixes happen in one place. Developers receive feedback without having to change context and open third-party platforms. After integration, the user gets the following benefits:

• review directly in the IDE;
• automatic issues for serious errors;
• direct links to problematic lines of code;
• quick access to context and edit history.

This approach speeds up development and makes work more stable.

Real-time and quick hints

When the code AI reviews tool works directly within the editor, developers see feedback immediately. This saves time and minimizes errors. The tool can:

• find typos and syntax errors;
• suggest optimal constructions;
• evaluate code quality;
• give advice according to the style of the project.

The advantage is obvious: less time spent searching for problems and more attention to important tasks. If I need to quickly check my code, AI tools can perform an express check and show the main risks.

Contextual analysis

AI code analysis allows systems to take context into account. They understand the logic of the project and are not limited to fixed rules. The tool evaluates changes from the perspective of structure, architecture, performance, and security. This reduces the number of false positives and enhances the overall quality of the review.

Support for different languages

AI based code review works with numerous programming languages and can adapt to the specific features of any codebase. This applies to both popular technologies and more niche tools used in specialized projects. This versatility helps teams with different stack directions to work in a single space without unnecessary restrictions.

Security and risk reduction

AI is good at finding security bugs in the early stages. The system checks for:

• incorrect data validation;
• authorization issues;
• unsafe data usage;
• configuration errors;
• outdated components.

The earlier such issues are detected, the fewer risks there are in production.

RhodeCode's unique opportunity

RhodeCode stands out by offering maximum flexibility and full control. RhodeCode's main advantage over its competitors is complete freedom in choosing an AI tool for code. This allows the team to adapt the tool specifically to their project, combine different models, and control how AI analyzes code in any working environment.

Our code review company can work with ChatGPT, Gemini, Claude, or their own model. If necessary, you can add an internal model or create a custom integration. Advantages of RhodeCode:

• flexibility in choosing AI models;
• support for enterprise requirements;
• the ability to deploy locally;
• control over how AI conducts reviews;
• reduced security and privacy risks.

This allows you to build a reliable workflow and use AI that fixes code in your own environment without third-party access.

How to Get Started With AI Code Review in RhodeCode

1. Open Settings → AI Code Review
2. Add your API key for ChatGPT, Gemini, Claude, or your custom endpoint
3. Assign the AI reviewer to a pull request
4. (Optional) Customize the AI prompt
5. Start your first AI-powered review

You can also watch the demo video showing how the feature works step-by-step.

F.A.Q. about AI-powered code review

Can I integrate AI code review with my existing infrastructure?

Yes. Modern AI code review tools offer flexible integrations with standard software development tools. For platforms like RhodeCode, which handles Git, Mercurial, and SVN, the AI integrates with the pull/merge request workflow, ensuring the AI review code process respects your existing branching and development models. This is key for enterprise adoption and maintaining security compliance.

What are the main problems AI can find in my code?

AI can find a wide range of issues, including: security vulnerabilities (injections, weak cryptography), performance bottlenecks, resource leaks, logic errors, maintainability problems, and adherence to internal style guides. It acts as a proactive code audit tool.

Can I get an AI code review for free?

Many providers offer a limited free AI website code review or a trial of their basic features. For smaller open source projects or individual developers, services like GitHub AI code review or extensions to IDEs often provide basic checks for free. However, enterprise-grade solutions, especially those designed for massive scale and security requirements like RhodeCode, offer more comprehensive integrations and advanced features on a paid platform.

Conclusion

AI code reviews have become an essential tool for teams looking to maintain similar code quality, decrease mistakes, and work quicker. For example, GitHub AI code review tools provide quick connection and automatic analysis of each pull request. The mix of analysis, contextual awareness, and automated suggestions offers developers an undeniable advantage.

AI solutions allow you to eliminate regular activities, focus on development, and close issues faster, making your workflow more predictable.

RhodeCode stands out in particular. With the ability to choose between ChatGPT, Gemini, Claude, or proprietary models, the team gains control over the technology and can build the review process according to internal requirements. This approach allows AI to be used without compromising security, performance, or privacy.

AI code reviews do not replace people, but they significantly enhance their work. It is a tool that helps developers move forward with confidence, find solutions faster, and build quality products. And when the technology adapts to the team, as in the case of RhodeCode, the result becomes even more effective.

If a team wants consistent results, faster development, and complete control over the review process, combining AI tools with RhodeCode's capabilities is a beneficial step for any project.