German robotics startup Sereact raises $110M

The latest funding round follows its €25m Series A round last year.
German robotics startup Sereact raises $110M

German robotics startup Sereact has raised $110m in a Series B funding round, with the funding used to develop and scale its latest AI model and US expansion, it announced today.

The round was led by San Francisco and Berlin-based VC Headline, with Bullhound Capital, Felix Capital, and Daphni also investing. Returning investors Air Street Capital, Creandum, and Point Nine also participated.

It follows its €25m ($29m) Series A funding in January last year, which was led by Creandum. It has raised more than $140m in total.  

The Stuttgart-based company, which employs around 100 people. develops AI-powered robotics tech that is deployed across various industries and use cases for customers in the US and Europe. Its AI software equips robots with general-purpose visual and manipulation capabilities, enabling them to perceive their environment and devise intelligent strategies to perform a wide range of physical tasks.  

The bulk of the new funding will be used to develop its latest AI model, Cortex 2, which is launching today. The model trains a robot on different physical behaviours, helping it pick the one most likely to succeed.  

Ralf Guide, co-founder and CEO, said: "It takes Cortex out of the picking bin and into work where contact matters - assembly under tension, kitting, placement where every millimetre counts. We don't build robots. We don't sell services. We ship one thing: the model that runs on any robot. Single arm, dual arm, humanoid, fixed cell - same brain across all of it.”

The funding will also be used for US expansion, the 2021-founded startup said, with a new office in Boston, where it plans to staff up across engineering, commercial and other areas.

Sereact's customers include BMW, Daimler Truck, Bol and Active Ants, and the real-world deployment of its products creates a real-time data flywheel from which Sereact systems learn to continually improve far beyond systems trained primarily on synthetic data, it added.

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