We raised $143M to build the control layer for software change.Read more
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We raised $143M to build the control layer for software change.Read more
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CodeRabbit raised a $143 million Series C at a $1.5 billion valuation and is introducing Agentic Change Management, the control layer for software changes created by humans and agents.
CodeRabbit worked with NVIDIA and Baseten to post-train NVIDIA Nemotron 3.5 Lightning for one of CodeRabbit's highest-volume routing tasks, improving route agreement while reducing estimated inference cost.
Coding agents can expand change volume faster than teams can understand it. Review needs a shared path from intent to system behavior to code so people can keep shaping the system.
CodeRabbit Security traces suspected vulnerabilities across the codebase, evaluates reachability and the conditions required for exploitation against code evidence, and proposes reviewable fixes in the pull request workflow.
CodeRabbit raised a $143 million Series C at a $1.5 billion valuation and is introducing Agentic Change Management, the control layer for software changes created by humans and agents.
CodeRabbit worked with NVIDIA and Baseten to post-train NVIDIA Nemotron 3.5 Lightning for one of CodeRabbit's highest-volume routing tasks, improving route agreement while reducing estimated inference cost.
Reviewing every agent-generated change is becoming impossible. The next bottleneck is deciding what deserves attention, rebuilding context, and keeping code safe after merge.
Code is now plentiful, but judgment about what deserves to merge is not. Better models sharpen what agents write, but they don't make that call.
Opus 5 produced a cleaner actionable-comment stream, but caught fewer known issues and generated roughly four times the baseline's nitpicks. Here is where the model may fit — and where it does not.
Coding agents can expand change volume faster than teams can understand it. Review needs a shared path from intent to system behavior to code so people can keep shaping the system.
Post-Merge Actions use pull request context to handle changelogs, documentation, tickets, and other work that should happen after merge.
OpenAI’s GPT-5.6 family includes capability tiers: Sol as the flagship model, Terra as the lower-cost option, and Luna as the fastest, lowest-cost tier.
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