AI-powered food waste tracking for commercial kitchens
Winnow builds vision-based AI and IoT tools to measure and reduce food waste in large-scale food service operations. The tech stack—Java, Python, Go, Spring Boot, Kafka, and AWS—reflects an engineering-first, data-driven architecture designed to process real-time kitchen sensor streams and video. Active projects center on waste-tracking accuracy and simulation, while hiring remains manager-heavy across engineering and sales, suggesting a scaling phase focused on both product depth and market expansion.
Winnow develops software for contract catering, hospitality, and foodservice operations to cut food waste and improve kitchen profitability. The platform combines computer vision AI (Winnow Vision), IoT sensors, and data analytics to track waste in real time and surface operational improvements. Founded in 2013 and based in London, the company operates at 51–200 employees with active hiring across engineering, sales, support, and data teams spanning the UK, Mexico, Romania, and the US. Core pain points center on scaling globally, improving tracking accuracy, and expanding sales reach into new verticals.
Winnow combines AI vision technology, IoT sensors, and data analytics on a Java and Python backend, running on AWS with Kafka for real-time data streaming and PostgreSQL/Redshift for storage.
Winnow has active hiring in the United Kingdom, Mexico, Romania, and the United States, reflecting a multi-region engineering and sales footprint.
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