Recycleye: Intelligent Automation for Circular Waste Management

Recycleye: Intelligent Automation for Circular Waste Management

In traditional recycling plants, the chore of separating millions of mixed waste items remains labour-intensive, inconsistent, and costly. Recycleye approaches this challenge by blending advanced artificial intelligence, computer vision, and robotics to automate and enhance recycling processes that have long relied on manual sorting. Recycleye is training powerful machines to bring total transparency, traceability and accountability to the waste management (resource) industry. The company has developed a computer vision system that replicates the power of human vision to identify every item in entire waste streams. The startup is headquartered in London, United Kingdom, and since its founding in 2019, has expanded deployments across Europe and into the United States, partnering with major waste management firms and technology backers.

Technology and Product

By merging computer vision with advanced software and deployable hardware, Recycleye has developed an ultra-low-cost device that is able to create 24/7 waste item passport logs at a material, object, and even brand level. Recycleye’s vision system is enabling MRFs to optimise their plant operations by leveraging live performance knowledge. Sorting is performed by robotic solutions such as Recycleye QualiBot®, which automates manual picking using AI-guided robotic arms.

Designed to retrofit into existing plants, the system supports continuous operation and provides visibility into material composition and process performance. The company is now also merging its computer vision algorithms with affordable robotics to develop the world’s first fully automated material recovery facility, eliminating the industry’s reliance on manual labour for waste sorting.

Recycleye QualiBot® aims to reduce costs by automating manual picking operations (© Recycleye)

Industrial Fit and Applications

Recycleye’s technology is designed for materials recovery facilities processing dry mixed recyclables—one of the most challenging waste streams due to contamination and material diversity. Deployments focus on improving the purity of sorted outputs and identifying materials that conventional systems often miss. Operators can gain clearer insights into waste composition as every item passing through the line is classified and recorded. This data-driven approach supports process optimisation, quality control, and improved recycling yields.

Recycleye AI can identify both single-material objects and multi-material items, such as batteries. (© Recycleye)

Founding Team

Founded in 2019, Recycleye was spun out of CEO Victor Dewulf’s PhD research at Imperial College London. According to Recycleye, the company is working with some of the largest players in the waste industry, using its technologies to digitise and decentralise existing waste sorting infrastructure.