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London-based edge AI infrastructure company Edgify raises €7.7 million to combat retail losses

Edgify, a London-based edge MLOps platform for physical retail providing the AI infrastructure to protect against in-store loss, has raised €7.7 million ($9 million) in Series A+ funding to accelerate the rollout of its platform. The round was backed by Rank Ventures and Mangrove Capital Partners, bringing the company’s total funding to €21.6 million ($25 […] The post London-based edge AI…

London-based edge AI infrastructure firm Edgify has secured €7.7 million (£7.7 million) in Series A+ funding to expand its platform, aimed at tackling retail losses. The round of investment was led by Rank Ventures and Mangrove Capital Partners, bringing the company's total funding to €21.6 million (£25 million). Edgify's CEO and Co-founder, Nadav Israel, stated that their mission is to bring intelligence to where data is created, allowing devices to learn together within a store without transmitting any data to the cloud.

Founded in 2019, Edgify's edge AI platform is designed to protect against in-store loss, improve operational efficiency, and enhance the customer experience. The company's technology connects, orchestrates, and trains AI models across various edge devices, such as self-checkouts, cameras, scales, and point-of-sale systems, converting them into a unified intelligence layer. This approach reduces cloud infrastructure costs, lowers latency, and keeps customer and operational data within the store premises.

Edgify's platform is hardware-agnostic and works seamlessly with in-store equipment from leading manufacturers like Zebra Technologies and Bizerba. The startup caters to a range of industries beyond physical retail, including transportation, logistics, manufacturing, and warehouse operations, offering a solution to common challenges such as reliance on legacy systems, cloud bandwidth constraints, and high costs associated with deploying on-premises servers.

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

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