The surge in code written with AI has transformed software development pipelines, delivering unprecedented speed but also raising serious quality concerns. Code can be written, tested, and deployed faster than ever, but many outputs can contain hidden flaws like logic errors, security vulnerabilities, or inconsistent architectures that aren't immediately obvious at first glance.
"Leaders want the velocity promised by AI tools, but they can't afford to compromise on security, reliability, or compliance," explains Harjot Gill, co-founder and CEO of CodeRabbit, a code review platform that's working to close that gap. By using AI to review code directly inside existing workflows, CodeRabbit aims to turn what was once a development bottleneck into a safeguard for both speed and quality.
The Inefficiencies That Come with AI Coding
Teams in software development looking to move faster are increasingly turning to AI coding tools that have pushed output to levels unimaginable a decade ago. Powered by large language models trained on vast datasets, these systems can generate, test, and merge entire features in hours or even minutes.
But that speed comes at a cost. The faster code ships, the more strain falls on the processes meant to keep it safe and reliable. Senior engineers face the task of catching bugs, reviewing architecture decisions, and maintaining security guardrails at this breakneck pace.
"Most teams we work with aren't struggling to generate code anymore—they're drowning in it," says Gill.
Adding to this is the fact that traditional safeguards aren't built for this kind of speed. Security scanners and static analyzers each catch specific classes of problems, but they often run in isolation, disconnected from the larger development workflow. Reviewers, already pressed for time, tend to focus on superficial issues rather than tracing the full architectural and security implications of every change.
And while AI has accelerated delivery, it has also introduced new reliability risks. "AI-generated code has a tendency to be 'mostly correct'—a polite way of saying that it works until it doesn't," Gill says. "We see missing edge cases, inconsistent error handling, insecure defaults, and architectural drift. The tricky part is that these aren't always obvious in a pull request. They're subtle, context-dependent issues that can take down a system weeks later if they slip through."
The result is predictable: error-prone code slips through, technical debt grows, and teams find themselves reacting to production incidents rather than preventing them at the source. In fact, 59% of engineers report that the AI tools they work with are creating deployment errors in their code at least half the time, showing how widespread and consistent this problem has become.
Why Harjot Gill Says CodeRabbit Is Key to Balancing AI Speed with Software Quality
Faced with these challenges, engineering leaders are looking for a way to sustain the speed of AI-assisted development without sacrificing the confidence that every release meets the highest quality standards before production.
That's where CodeRabbit comes in. Purpose-built for today's mix of hybrid codebases (whether written by humans, AI, or both), CodeRabbit embeds directly into the tools developers already use so that reviews happen automatically in pull requests with no extra steps or dashboards.
"CodeRabbit runs inside the CLI, IDEs like VS Code and Cursor, and across Git platforms like GitHub, GitLab, Azure DevOps, and Bitbucket," Gill explains. "Reviews show up in the same places engineers already expect them. There's no new tool to log into and no extra step to manage—it just feels like a senior engineer is already in the loop."
At the core of CodeRabbit's approach is its ability to interpret context. First, the platform considers the intent behind each change (what the code is meant to achieve and the problem it seeks to solve), evaluates the environment through reading the structure of the broader system, file relationships, and dependencies that could be affected, and draws on the conversation surrounding the work, from project discussions to issue trackers and prior review comments.
Balancing these elements allows CodeRabbit to move beyond spotting syntax errors toward identifying deeper architectural inconsistencies, security gaps, and opportunities for design improvements that would otherwise demand significant human attention. "Because we pull in context from multiple sources (code graphs, historical PRs, design docs, Jira tickets, security policies), CodeRabbit can see patterns a human might miss in a single review," Gill says. "That includes things like a seemingly harmless change that introduces a circular dependency, or an update that silently bypasses authentication logic."
And what makes CodeRabbit even more impactful is its ability to learn continuously. As reviewers accept, reject, or correct its suggestions (whether in PR comments or via chat), it stores those learnings in an internal database tied to the team's organization. Over time, the platform adapts to team-specific preferences and coding conventions, making its advice more precise and relevant with each interaction.
Gill points to real-world results of the platform's reliability. Digital coupon provider Groupon, for example, cut its average review-to-production time from 86 hours down to just 39 minutes after adopting the platform. Across its entire customer base, CodeRabbit has also seen 4x faster merges, 50% more bugs caught pre-production, and 50% less time spent on reviews.
"It's not just about speed—it's about confidence," Gill claims. "Teams report fewer production incidents because issues are caught earlier, before they can impact users."
The Future of Software Delivery
As enterprises continue to embrace tools to speed up coding, the tension between speed and quality will only grow unless review processes keep pace. CodeRabbit points to a more practical path forward, where integrated tools fit naturally into existing workflows, deliver measurable improvements, and scale as demands increase.
As Harjot Gill himself puts it, "AI code generation isn't going away. If anything, it's accelerating. The only way to make that sustainable is to pair generation with rigorous, context-aware review. CodeRabbit is the infrastructure that makes sure teams can move fast without breaking trust or production."
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