Humai, Catchoom and Slyce merge to create Partium, an image recognition tech for industry and retail

Partium tech.eu

Two European providers of image recognition and visual search technology are combining with an American counterpart to create a one-stop solution to help industrial companies and retail warehouses get a bit more organised. Austrian startup Humai, Barcelona-based Catchoom and US-based Slyce will merge to become Partium, a technology that can recognise, identify and locate spare parts.

“Our companies each developed compelling and complementary offerings for part recognition,” said Humai CEO Philipp Descovich, who will become CEO of Partium.

“Whether you’re trying to identify a part in a warehouse or a store or a machine, we’ll have the end-to-end solution,” added Catchoom CEO and co-founder David Marimon. He’ll become the new Chief Product Office.

Partium will build on Humai’s part recognition solutions, which combine computer vision with ‘Delta;, a proprietary spare part AI. Customers including Bosch, Daimler and Deutsche Bahn already use this software in mobile apps to identify installed parts as well as loose parts in a warehouse.

Spain’s Catchoom specialises in large-scale image recognition and using AI to enrich product metadata. Philadelphia-based Slyce brings experience with automotive and home improvement retailers, such as the Home Depot. The startup developed an in-store “part finder kiosk” that shoppers could use to locate what they need.

The new company will also continue to provide products to other sectors. These include the Slyce SDK for visual search in fashion, grocery, and home decor; CraftAR SaaS for image recognition and AR; and MagicLens for AR renderings in sales materials, trade shows, and presentations. 

Partium will be headquartered in Vienna but retain outposts in Barcelona, Philadelphia and Nova Scotia. The combined team will have 90 employees, including what it claims is the “largest group of seasoned engineers and PhDs in the world that is solely focused on solving the spare part search problem”.

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