C
Awaira Score
70
Out of 100
Valuation
N/A
Post-money
Total Raised
$222M
All rounds
Awaira Score
70/100
Founded
2017
100-500 employees
What They Build
March 2026Covariant builds an AI robotic picking and automation platform that enables warehouse and fulfillment robots to handle the enormous variety of product shapes, sizes, and packaging types encountered in real-world logistics operations. The platform is built on RFM-1, a foundation model for robotics trained on one of the largest robotics datasets ever assembled, enabling generalized manipulation capa…
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StageSeries C
Employees100-500
Country🇺🇸 United States
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Funding Rounds
Series C · No public funding round data available yet.
Founded Same Year (2017)
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View all alternatives to Covariant →Frequently Asked Questions
What is Covariant's valuation?▾
Covariant's valuation is not publicly disclosed.
Who invested in Covariant?▾
Investor information for Covariant is not publicly available at this time.
When did Covariant last raise funding?▾
No public funding round data is currently available for Covariant.
How many employees does Covariant have?▾
Covariant has approximately 100-500 employees.
What does Covariant do?▾
Covariant builds an AI robotic picking and automation platform that enables warehouse and fulfillment robots to handle the enormous variety of product shapes, sizes, and packaging types encountered in real-world logistics operations. The platform is built on RFM-1, a foundation model for robotics trained on one of the largest robotics datasets ever assembled, enabling generalized manipulation capabilities across new product types without task-specific retraining.\n\nThe company raised approximately 222 million USD and has deployed its AI in warehouse environments at major retailers and logistics operators in North America and Europe, with robots handling millions of picks per day across diverse SKU catalogs. Covariant was founded by researchers from UC Berkeley with foundational backgrounds in deep reinforcement learning for robotic manipulation.\n\nIntelligent robotic picking remains one of the hardest unsolved problems in warehouse automation, as the combinatorial variety of product types encountered in e-commerce fulfillment exceeds what rule-based vision systems can handle reliably. Covariant approach of training a generalist manipulation model on large-scale real-world robotics data parallels the approach that made large language models broadly capable, and represents one of the most technically credible attempts to bring general robot AI to industrial deployment at scale.