Custom-trained models, tailored for exceptional efficiency.
We deliver lower-cost, faster, stronger performance than general-purpose labs. One-click deployment makes cheaper experimentation and faster iteration than one-size-fits-all alternatives.
Train and deploy frontier models for sharper intelligence, cheaper experimentation, and a faster path between simulation and the real world — bringing physical AI within reach through efficiently continual learning, without the engineering burden.
We deliver lower-cost, faster, stronger performance than general-purpose labs. One-click deployment makes cheaper experimentation and faster iteration than one-size-fits-all alternatives.
We train models around the environments, behaviors, and outcomes that matter to you. Starting with expert guidance and evaluation-first development, it learns to understand, predict, and act in the world it was built for.
Deployment is only the beginning. Your models learn from feedback, outcomes, and operating signals, continuously improving without the cost and complexity of traditional retraining cycles.
Ph.D. in Computer Engineering focusing on AI/computing systems. Recipient of the NSF CAREER Award. Raised $1M+ in research funding as a principal investigator. Previously worked at Google and AWS AI Labs.
Ph.D. candidate in Computer Science working on generative and multimodal AI. Previously worked at DAMO Academy, Alibaba, Inc. as a staff research engineer. 10+ years of industrial experience in AI research and production development.
Ph.D. in Computer Science focusing on machine learning systems. Core founding member of CoCoPIE, which raised $6M. Previously worked at ByteDance. 10+ years of fundamental system optimization experience.
sunbow.ai is built for real-world challenges where better physical AI can meaningfully change outcomes. We combine frontier research with practical engineering to deliver systems customers can rely on.
Explore rolesFrom training to real-world deployment, launch a customized world model built for your need, faster than general-purpose models, continuously improving, and reliable at scale.
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