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Greyparrot Raises $27M Series B for AI-Powered Recycling Plant Systems

🔄 Updated 2h ago
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Key points

  • Greyparrot raised $27 million in Series B funding.
  • The company uses AI and computer vision in recycling plants.
  • Systems identify materials on conveyor belts to improve sorting.
  • Funding will support expansion in North America and Europe.

Funding for Recycling AI

Greyparrot, a London-based startup, announced a 20.3 million ($27 million) Series B funding round led by technology investor Omar Mir. This investment will support the company's growth and operational expansion.

AI in Recycling Operations

Greyparrot installs AI-powered camera systems above conveyor belts in recycling facilities. These systems utilize computer vision to identify various materials, products, and brands in real time. The data collected helps operators enhance material recovery, increase sorting efficiency, and meet recycling regulations.

Current Reach and Impact

Greyparrot's systems are deployed in over 20 countries and have analyzed more than 1 trillion waste objects. Major waste-processing companies like Waste Management and Veolia are among its clients. The company also offers the Deepnest platform, which provides data to consumer brands such as Unilever, L'Oréal, and Kenvue to inform packaging redesigns and comply with Extended Producer Responsibility rules.

Expansion and Future Goals

The new funding will facilitate Greyparrot's expansion into North America and Europe. The company's objective is to prevent over 1 million tons of waste by 2030. This funding round reflects a broader investor interest in applying AI in physical environments, with the physical AI sector raising nearly $47.3 billion in the first half of 2026, an 80% increase year-over-year.

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Reporting from

London-based startup Greyparrot secured a 20.3 million ($27 million) Series B funding round to expand its AI-powered camera systems for recycling plants. The technology uses computer vision to identify materials on conveyor belts, aiming to improve sorting efficiency and material recovery.