CodeRabbit(opens in a new window) was launched in 2023 by a team of former engineering leaders who had felt the pain of slow, manual code reviews firsthand. While AI made it easier to write code, there weren’t tools to solve the biggest bottleneck: getting code shipped.
“You could generate a million lines of code,” says Sahil M. Bansal, Senior Product Manager at CodeRabbit. “But if your review process only supports 1,000 lines, that’s all you’re shipping.”
The insight was simple but powerful: the bottleneck in software development had shifted from code generation to code review. So the team focused on using OpenAI’s models not only to write code, but also to unlock the speed, accuracy, and intelligence required to review it. Over the last year, CodeRabbit has been used by more than 5,000 customers and 70,000 open-source projects.
## Shifting AI from code generation to code review
As engineering teams leaned into AI for code generation, the limitations of manual code reviews became more apparent. Reviews were slow, repetitive, and missed critical issues—particularly across large, distributed teams, unfamiliar codebases or edge cases that are hard to detect. Valuable developer time was spent reviewing instead of building, and costly bugs were escaping into production.
CodeRabbit’s team knew the problem intimately. “Even when teams used AI to generate more code, they weren’t shipping faster,” Bansal explains. “The code review cycle was the bottleneck.”
And while some companies attempted to solve this by encouraging reviews during development, CodeRabbit believed that the most effective check came just before shipping—once all the code came together, like tributaries feeding a river.
“We realized this was the most strategic moment to apply AI for code reviews,” says Aravind Putrevu, Director of Developer Marketing at CodeRabbit. “It’s when the risk is highest and the context is most complex.”
## Unblocking reviews with OpenAI models
To solve the problem, CodeRabbit built a powerful multi-step review system powered by OpenAI’s LLMs.
When a developer submits a pull request, CodeRabbit clones the repository into a sandboxed environment, enriches the diff with additional context that comes from code history, linters, code graph analysis, issue tickets, and developer conversations, before kicking off a multi-model analysis.
“We run recursive reviews using OpenAI models,” Putrevu says. “After enriching the context, we run multiple passes to ensure the comments are accurate, meaningful, and tailored to each team’s standards.”
CodeRabbit’s system uses a combination of OpenAI models for different tasks:
“We think of CodeRabbit as a senior engineer embedded in your workflow,” says Bansal. “It’s not just AI that reviews code. It’s a system that understands your codebase, your team’s habits, and your standards—powered by models with the reasoning depth to catch real issues.”
CodeRabbit also recently launched integration into Visual Studio (VS) Code, allowing developers to receive reviews in real time directly in their code editor. The tool now supports both code reviews in VS Code and in Pull Requests, ensuring flexibility for developers to review code individually right as they are coding, as well as collectively in their Pull Requests when all code commits come together.
## 50% more accurate suggestions and faster PR merges
Since adopting OpenAI’s o3 model, CodeRabbit has achieved measurable improvements:
CodeRabbit continues to benchmark OpenAI’s models against competitors like Sonnet 3.5 and Google Gemini, consistently finding OpenAI’s models to be effective for their use case. Looking ahead, they’re exploring further customization around o3‑mini and considering reinforcement fine-tuning to continue elevating their AI code review capabilities.
## Delivering 4x speed, 50% fewer bugs, and 60x ROI
Developers using CodeRabbit already shipping code more quickly, with fewer errors, and freeing up time to focus on harder engineering problems:
CodeRabbit is continuing to scale adoption and expand its in-IDE support, making reviews even more immediate and accessible. The team is also exploring reinforcement learning and deeper customization using OpenAI’s o3 models, with the goal of making reviews smarter and more adaptive across environments in addition to faster.
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