# Code is abundant. Judgment is scarce. We raised $143 million to help it scale. > Source: > Published: 2026-08-12 13:00:00+00:00 When we founded CodeRabbit, AI was already changing how software was written. We saw where the industry was going and there was one question that we knew would become increasingly important. Who would independently verify all of that code? That led us to create the first AI code review product. Over the last four years, CodeRabbit became the industry-leading independent AI code review layer. It understands your codebase, learns from your team, and gives developers evidence-based feedback they can trust and act on. Fast forward to today. We’re announcing two milestones. The first is that less than a year after raising our [Series B](https://www.businesswire.com/news/home/20250916401011/en/CodeRabbit-Raises-%2460M-Series-B-Following-Unprecedented-Growth-as-Vibe-Coding-Triggers-a-Need-for-New-Code-Quality-Standards), we raised $143 million in a Series C funding round at a $1.5 billion valuation. Atomico and Smash Capital co-led the round, with participation from new investors, including BMW i Ventures, Datadog, Hirtle Callaghan, SineWave Ventures, Scenic Management and our existing investors, including CRV, Scale Venture Partners, Flex Capital, Pelion Venture Partners, Harmony Partners and Engineering Capital. We’re also introducing a new product category we call Agentic Change Management, a platform that represents the next chapter for CodeRabbit. In conversations with thousands of teams trying to navigate the changes AI coding agents have made to their workflows, we realized that AI code review only solves part of the problem. Agentic Change Management requires a full suite of tools to help teams validate what should ship, prioritize where developer attention and time generate the most value, explain the intent and risk of the massive PRs AI generates, and keep their codebase healthy after merge in the face of increased AI security threats. ## Issue tracking is dead. And that’s changing everything. It used to be that implementation was expensive. Teams discussed what to build, established priorities, assigned the work, and then carefully invested scarce engineering time in turning an idea into code. The high cost of implementation forced planning and judgment to happen before the code existed. AI is reversing that sequence. A product requirement, support ticket, security finding, or production alert can now easily and quickly become a proposed code change with the rise of autofix features. Coding agents can work for hours, produce thousands of lines, and open pull requests with limited human involvement. Anyone on your team can initiate code changes, be they developers, product managers, designers, or even marketers. The shift is compounding. GitHub is on pace to record [14x](https://x.com/kdaigle/status/2040164759836778878) more commits this year. Among companies in the 90th percentile of coding-agent adoption, autonomous agents open [35%](https://jellyfish.co/state-of-ai-software-engineering/#:~:text=The%20clearest%20picture,rather%20than%20closing.) of PRs. Because of that, code increasingly exists before a team has decided whether the work is valuable, how it should be prioritized, or whether it deserves engineering attention. The backlog is moving from tickets to pull requests. And that shift fundamentally changes the role of the PR. The PR is set to become the auditable planning and decision point where the team determines what to ship and whether a change meets the quality bar, what risks it introduces, how it affects the larger system, and whether it is ready to ship. The problem teams now need to solve is the fact that human attention remains finite while the PR backlogs continue to grow. Large scale agent outputs exceed the time humans have to understand them. Every PR adds new relationships, dependencies, security exposure, and maintainability risk to the codebase. The new bottleneck is judgment. ## Introducing the Agentic Change Management platform The Agentic Change Management platform is the control layer for software changes created by humans and agents. It extends CodeRabbit’s independent AI code review into one connected system that validates changes, prioritizes attention, explains impact, and continuously monitors the shipped codebase. Proposed changes should come with evidence, a priority, and an explanation people can understand, and the codebase they land in should stay secure and governable as it evolves. Independent AI code review remains the foundation. CodeRabbit evaluates changes using multi-repository context, organizational standards, pre-merge checks, team knowledge, and evidence from isolated test environments. It challenges the assumptions behind a change and verifies suspected failures before a human reviewer invests time. And today, we are introducing three new capabilities: - CodeRabbit Triage - CodeRabbit Change Stack [CodeRabbit Security](/security) Together, they bring prioritization, code explainability, and continuous codebase monitoring into the same independent code review system. ## CodeRabbit Triage directs attention As change volume grows, the pull request queue becomes a decision system. Arrival order gives teams little information about where to spend their time. A production fix, a security change, an incomplete experiment, and a low-risk refactor should each follow a path that reflects its value, urgency, readiness, and risk. **CodeRabbit Triage:** - Scores and routes incoming changes using factors such as value, urgency, risk, dependencies, readiness, and reviewer fit. - Directs consequential work to human reviewers, and moves low-risk changes into automated workflows. - Filters duplicate, irrelevant, or unready work. This gives teams a clearer basis for deciding what should move first, what can wait, and where human judgment will have the greatest impact. The queue begins to reflect organizational priorities instead of the order in which pull requests arrived. The goal is to make the team more effective as the volume of proposed work grows. The most valuable agents will contribute to the team’s priorities and help direct limited attention toward the changes that create the greatest value or carry the greatest consequence. ## CodeRabbit Change Stack explains the change A diff shows what changed. Change Stack shows what it means. The traditional GitHub PR interface puts the diff at the center of review. That worked when people wrote the code and changes arrived at a pace reviewers could follow. Agent-generated changes break that assumption. As their volume and scope increase, line-by-line review becomes the wrong starting point. CodeRabbit’s review agent can handle the details in the diff. Change Stack helps humans understand the change itself: how the pieces connect, which contracts and behaviors it affects, whether it introduces a migration or changes an integration, how far the impact reaches, and who needs to weigh in. **CodeRabbit Change Stack:** - Helps you understand a massive agent-generated diff in minutes. - Organizes the agent-generated changes into semantic layers, grouping related modifications into a guided explanation of how the change works. - Surfaces the purpose and risk and shows how they affect the broader system. - Helps reviewers quickly identify the work that can move forward and the areas that require deeper judgment. We believe explainability will become a foundational part of software development as AI generation becomes more complex. Production observability gives teams a way to understand a running system. Code explainability gives them a way to understand the decisions and behavior entering that system. It preserves the shared understanding teams need as code output accelerates. ## CodeRabbit Security protects what has shipped Every merge changes a living system. Dependencies evolve. New vulnerabilities emerge. Data flows change. Architectural assumptions age. Code that appeared safe in one context can become risky through its interaction with the rest of the codebase. Large language models are also lowering the cost and expertise required to discover software vulnerabilities. Defenders and attackers can examine more code and test more attack paths at greater speed and scale. - Applies the same codebase-wide understanding and independent reasoning used during review to code already running in production. - Analyzes relationships across files, services, data flows, authorization boundaries, and trust boundaries. - Identifies complex security and business-logic risks that fixed rules and file-level pattern matching can miss. Each finding is grounded in code evidence. CodeRabbit Security verifies whether a vulnerable path is real and reachable, prioritizes remediation, and sends proposed fixes back through the pull request workflow. The same control layer that evaluates incoming changes continues protecting the code after it ships. Over time, continuous maintenance will also address drift, duplication, technical debt, and the broader entropy that accumulates across a changing codebase. The objective is to keep the system healthy as thousands of human- and agent-generated changes become part of it. ## AI that keeps humans in the loop A proposed change affects everyone who interacts with the code in an organization including authors, reviewers, maintainers, security teams, and everyone who inherits the system. Its value depends on whether the organization can understand it, trust it, govern it, operate it, and maintain it. AI can do a lot of things well. But people remain responsible for intent, architecture, product judgment, acceptable risk, and the consequences of what ultimately ships. Our job is to help that judgment scale by making it much simpler for humans to remain in the loop. And we’re doing that by drawing on the industry-leading context we gather about your codebase when doing your code reviews. CodeRabbit develops a semantic understanding of how the codebase fits together, learns the team's standards, and accumulates knowledge with every review. The same context supports validation, prioritization, and continuous monitoring, so it verifies findings in isolated environments, applies policies consistently across repositories, and gives developers and agents a shared view of each change. Organizations will use many coding agents, and people will work across different tools and workflows. CodeRabbit gives them one independent quality and governance system across those agents, repositories, and development environments. ## Why we’re raising now Over the past year, CodeRabbit’s revenue grew more than 5x and the platform now conducts more than 2 million code reviews each week. Over 17,000 customers and 150,000 open-source projects trust CodeRabbit to improve the quality and reliability of their software. The Series C funding gives us the resources to accelerate international expansion and invest more deeply in the research, infrastructure, and product capabilities required to make the Agentic Change Management platform work at a global scale. We also plan to invest more than $10 million to keep AI code review and agent capabilities free for open source projects and maintainers over the next 12 months. CodeRabbit will also continue building the context, verification, prioritization, explainability, security, and collaboration systems that organizations need as software change accelerates. Thank you for joining us on our journey as we built the first AI code review tool and transformed how people reviewed code. We’re excited for the next chapter.