Rovenso completes a CHF 2.3 million funding round

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12.12.2019
Rovenso robot on patrol

The EPFL spinoff building robots to protect workers has raised CHF 2.3 million in a financing round led by the Nivalis Group with the contribution from the Sean O’Sullivan Ventures. The funds will allow Rovenso to accelerate technology developments and expand its team.

Founded in 2016, Rovenso designs develops and builds autonomous robots for security monitoring, safety assessment and anomaly detection both inside and outside industrial buildings as well as in harsh and challenging environments.

Rovenso has participated in numerous programs including the HAX accelerator in Shenzhen, China where it met Sean O’Sullivan Ventures (SOSV), a venture capital firm with $700M assets under management and runs various global accelerator programs. Cyril Ebersweiler, General Partner at SOSV says, “We saw the potential of ROVENSO, when Lucian Cucu, Beat Geissmann and Thomas Estier had nothing but their energy and a small 3D printed robot that was simply climbing stairs."

To support Rovenso’s next development phase, SOSV together with the Nivalis Group the lead investor, have contributed CHF 2.3 million.in the company’s seed investment round. The new funds will allow the startup to accelerate the development of its technology, enhance prototypes and multiply pilot projects with industrial partners.

Nicolas Corsi, CEO Nivalis Group says, “We see Rovenso’s current offer as a platform to invent new services and business models in the emerging market of fully autonomous security and safety robots.”

Future plans include doubling the R&D team
In 2020 Rovenso plans to double the size of its R&D team which is based at the Technology Park Le Vivier, where it will run intensive tests with the robots patrolling 24/7 in a real environment. Moreover, as acknowledged by Rovenso’s CTO Lucian Cucu, the company plans to increase its R&D efforts to improve its multimodal anomaly detection system which combines precise volumetry, acoustic analysis, as well as thermal and visual features extraction.

(Press release/ran)

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