CodeRabbit Raises $60M to Tackle AI-Era Code Review

AI code generation creates a new choke point — CodeRabbit scales the fix

When Harjot Gill watched his remote team adopt GitHub Copilot, he saw more than faster drafts — he saw a looming bottleneck. AI was accelerating code creation, but much of that output needed careful human correction. The result: code review became the gatekeeper of velocity.

In early 2023 Gill launched CodeRabbit, folding in his observability startup FlexNinja. CodeRabbit uses codebase-aware AI to flag bugs, suggest fixes, and act like an informed reviewer rather than a generic assistant. The promise: reclaim developer time and reduce repetitive review work.

The market is responding. CodeRabbit reports 20% month-over-month growth and more than $15 million in annual recurring revenue. This week the startup raised a $60 million Series B at a $550 million valuation, bringing total funding to $88 million. The round was led by Scale Venture Partners with participation from NVentures, Nvidia’s VC arm, and returning investors including CRV.

Customers include Chegg, Groupon, Mercury and over 8,000 individual developers. Gill says teams using CodeRabbit can cut the number of humans needed for code review by roughly half — a meaningful productivity boost when AI is both an accelerator and a source of buggy, "unusable" code.

Competition is already building. Graphite raised a $52 million Series B led by Accel, and Greptile is reportedly in talks for a $30 million Series A. General AI coding assistants like Anthropic’s Claude Code and Cursor also provide review features — but CodeRabbit’s bet is on a dedicated, codebase-aware product with deeper technical breadth.

The rise of AI-generated code has even spawned a new role inside engineering orgs: the "vibe code cleanup specialist" — senior devs acting as AI babysitters to clean up and validate generated code. That signals two things: AI changes workflows, and human oversight remains critical.

What this means for engineering leaders

AI coding assistants will keep improving, but adoption without guardrails creates risk: technical debt, insecure patterns, and longer review cycles. Teams must treat AI-generated code as a new artifact type with measurable quality gates and observability.

• Audit where AI output lands in your pipeline and quantify review time per PR.
• Set policy-driven checks — linting, security scans, and context-aware tests — before human review.
• Measure ROI of AI code-review tools by tracking defect escape rate and developer cycle time.
• Plan role evolution — upskill reviewers to focus on architecture, security, and system-level correctness.

QuarkyByte’s perspective: successful adoption blends tools, metrics and governance. Rather than treating AI review as a drop-in feature, organizations should instrument review workflows, validate model outputs against real tests, and build feedback loops that improve both the AI and human reviewers. That approach turns code-review from a choke point into a performance lever.

CodeRabbit’s funding and traction show there’s demand for deeper, code-aware review tools. Whether standalone platforms or integrated assistants win long term, the immediate priority for engineering and product leaders is practical: preserve velocity without losing quality. The tools are evolving — your review processes should, too.