CodeRabbit

AI-powered code review platform that provides automated, context-aware pull-request reviews across GitHub, GitLab, and Bitbucket with multi-layered analysis and line-by-line suggestions. Rapid adoption among AI-heavy dev teams; integrates with major Git platforms; one of the leading AI code review tools alongside Qodo; strong developer community and open-source support.

CodeRabbit addresses the 'Review Gap' created by AI code generation — when developers use AI to write more code faster, human reviewers cannot keep up without AI-assisted review.

coderabbit.ai AI Code Review

United Statesprivate

At a glance

Total raised

$76M

last round series b

Headcount

null

approximate · public range

Headquarters

Walnut Creek

United States

Founded

2023

3 years active

Status

Private

AI Code Review

Latest valuation

$550M

Series B · 2025

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Funding history

Total raised

$76M

Rounds tracked

Latest stage

Series B

Latest valuation

$550M

Date	Stage	Amount	Valuation	Lead investors
Sep 16, 2025	Series B	$60M	$550M	Scale Venture Partners
Aug 15, 2024	Series A	$16M	—	CRV

Founder backgrounds

Guritfaq Singh

Co-founder & COO

Prior to CodeRabbit, spent over a decade at Alegeus in a development role from December 2011 to July 2023 before co-founding the company in August 2023. Attended Guru Nanak Dev University in Amritsar. Based in the San Francisco Bay Area.

Harjot Gill

Co-founder & CEO

Prior to CodeRabbit, was co-founder and CEO of Netsil, an observability startup acquired by Nutanix in 2018 for up to $74M in stock. Earlier, co-founded FluxNinja (later Aporia), an AI-powered cloud observability platform. Started a career as a software engineer at Alcatel-Lucent. Holds advanced degrees in computer science from the University of Pennsylvania. The operational-observability lens from those ventures — automated monitoring, analytics, and guardrails at scale — directly informed CodeRabbit's approach: treating code review not as a manual bottleneck but as a programmable, AI-driven quality gate, analogous to what Datadog did for infrastructure monitoring.

Vishu Kaur

Co-founder

Prior to co-founding CodeRabbit, was GTM at Netsil, an application observability startup. Netsil was later acquired — and both Netsil and CodeRabbit share co-founder/CEO Harjot Gill, indicating a longstanding working relationship. Holds a Certificate in Advanced Management from Babson College's F.W. Olin Graduate School of Business. Publishes regularly on AI-driven code review practices, contributing to the developer community discourse that CodeRabbit's product is built around.

AI bull / bear

Bull case

The AI code generation boom creates CodeRabbit's demand in real time. As Copilot, Cursor, and similar tools flood pull requests with machine-written code, human reviewers are the bottleneck — exactly the "Review Gap" CodeRabbit exploits. The $60M Series B at a $550M valuation, led by Scale Venture Partners, provides ample runway to capture a market that could become as essential as CI/CD pipelines. Multi-platform support across GitHub, GitLab, and Bitbucket means the product slots into nearly any engineering org without migration friction, and bottoms-up developer adoption — including strong open-source community traction — gives it a classic PLG wedge. If AI-generated code becomes the norm, AI-assisted review becomes non-negotional infrastructure, and CodeRabbit is the early category leader with the capital and integrations to define it before incumbents react.

Bear case

Code review is a feature, not a platform — and GitHub has every incentive to absorb it. Microsoft's Copilot already owns the code-generation side of the workflow; adding native AI review inside GitHub pull requests would cut CodeRabbit off at the knees with zero switching cost for the vast majority of its addressable market. The $550M valuation on $76M total raised implies revenue multiples that remain unvalidated without disclosed ARR, and the underlying technology is increasingly commodity — context-aware LLM-based review is replicable by any well-funded competitor or internal platform team. Developer love doesn't reliably translate to procurement budgets, and enterprise sales cycles for dev tools are punishing. If GitHub ships a "good enough" review agent, or if Qodo consolidates the space first, CodeRabbit's window narrows fast.

Generated by deepseek-v4-pro · May 2, 2026Refreshes quarterly · see disclosures

In the news

CodeRabbit · last 30 days

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Leadership

Harjot Gill

Co-founder & CEO

in 𝕏

Guritfaq Singh

Co-founder & COO

in 𝕏

Vishu Kaur

Co-founder

—

Products

CodeRabbit AI Code Review
CodeRabbit is an AI-powered code review platform that integrates directly with GitHub, GitLab, and Bitbucket to provide automated, context-aware pull-request reviews. It performs multi-layered analysis including code quality, security vulnerability detection, performance optimization, and best-practice enforcement, delivering line-by-line suggestions directly in the PR workflow. Trusted by over 10,000 organizations including The Economist, Life360, and Mercury, it claims to catch 50% more bugs before production and reduce manual review time by half. The platform is SOC 2 Type 2 and GDPR certified and does not train on customer proprietary code.
CodeRabbit CLI
A command-line interface that enables developers to run AI-powered code reviews directly from their terminal or integrate them into custom CI/CD pipelines. The CLI brings CodeRabbit's context-aware analysis capabilities outside the traditional PR workflow, allowing teams to review code at any stage of development and catch issues earlier in the development lifecycle.
CodeRabbit Agent for Slack
An AI agent integrated into Slack that acts as a team's second brain for code-related discussions. It enables developers to ask questions about code, get PR summaries, request reviews, and receive notifications about code quality issues directly within their messaging platform, reducing context-switching and keeping teams aligned without leaving Slack.

Most recent patents

CodeRabbit Inc.·2025-11-04
Systems And Methods For Networked Microservices Flow Control
US12463909
Abstract
Systems And Methods For Networked Microservices Flow Control
US12463909
Abstract Described are platforms, systems, media, and methods for performing flow control as a service in a networked microservice-based application comprising an aperture controller comprising a circuit architecture configuration user interface and a policy control loop and a plurality of aperture agents, each aperture agent comprising a metrics service, a flow classification service, and a flow control service.
View on Google Patents

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