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AI Funding Database

SiMa.ai concludes successful funding round

By Advanced AI EditorAugust 4, 2025No Comments2 Mins Read
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The latest round was led by Maverick Capital, with continued participation from existing investors and new investor StepStone Group.

According to SiMa.ai, this new funding will be used to support global expansion and accelerate the scale-up of its Physical AI platform, including increased investment in software innovation, go-to-market operations, customer success, and strengthening the automotive roadmap.

“This new funding further validates our leadership in the Physical AI space and the growing demand for Physical AI solutions,” said Krishna Rangasayee, Founder and CEO at SiMa.ai. “With strong support from new and existing investors, we’re scaling quickly to meet global customer demand across robotics, automotive, industrial automation, aerospace and defence, smart vision, and healthcare.”

SiMa.ai’s full-stack Physical AI platform, the SiMa.ai ONE, is an integrated, power-efficient solution that combines purpose-built silicon and a software-centric approach to simplify deployment and improve performance.

The platform features Modalix, its second-gen multimodal MLSoC now shipping, and Palette, a comprehensive software suite with both SDK and Edgematic, a no-code visual development tool.

Supporting all major ML frameworks, including vision, transformers, and generative AI, on a unified architecture, SiMa.ai enables fast and efficient AI adoption across a wide range of real-world use cases.

“SiMa.ai is redefining what’s possible at the edge by combining world-class silicon with a uniquely software-centric approach to Physical AI. Their ability to deliver high-performance, low-power solutions with frictionless deployment positions them to lead in a market that’s scaling fast,” said Andrew Homan, Managing Partner at Maverick Capital.

As demand surges for AI at the edge, where power efficiency, performance, and simplicity are seen as critical, SiMa.ai has taken a different approach by addressing the full system problem on a single chip and pairing it with a software stack that is optimised for the developer.



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