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Jaipur Robotics raises €4.3M to bring computer vision to waste plants

Swiss industrial AI companyJaipur Robotics has raised €4.3 million in seed funding to expand its computervision and automation technology for waste-to-energy and other industrialplants. The round was ...

Jaipur Robotics raises €4.3M to bring computer vision to waste plants

Swiss industrial AI firm Jaipur Robotics has secured €4.3 million in seed funding to bolster its computer vision and automation technology for waste-to-energy and other industrial plants. The investment round was spearheaded by EquityPitcher Ventures and High-Tech Gründerfonds (HTGF). Established in 2024 and based in Manno, Switzerland, Jaipur Robotics specializes in AI systems aimed at enhancing the safety, efficiency, and automation of industrial facilities, with a current focus on waste-to-energy (WtE) plants.

The company identifies manual monitoring and analogue processes as challenges faced by more than 3,100 waste-to-energy plants globally. By employing computer vision to analyze waste as it enters and moves through a facility, Jaipur Robotics' platform transforms visual data into actionable insights. These insights aid in detecting hazardous materials, optimizing waste mixing, and facilitating automated crane operations.

The system, trained on a dataset of over 50 million labeled images from European waste-to-energy operations, processes more than five million tonnes of waste each year and boasts a 99 percent accuracy rate in hazardous material detection. Jaipur Robotics' technology has reportedly cut unplanned shutdowns at individual plants by more than 80 percent.

Additionally, its calorific-value mapping supports waste mixing and combustion processes, while predictive guidance enhances crane operations. The company intends to leverage the new funding to expand into new markets and further refine its technology, including enhancing its capabilities for waste-to-energy and other industrial applications.

Written by urgent.news from Tech.eu's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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