From reporting to interpreting: An AI agent for recycling
PolyPerception’s new AI‑agent platform marks an impressive evolution of its Waste Analyzer – an AI‑powered waste analytics solution that improves sorting performance through end‑to‑end material tracking. One of the most significant breakthroughs is the natural language interface. Operators can now 'chat' with their plant data in plain language, asking questions such as ‘How did changing the settings on the recovery line affect our purity?’. With AI as its core, the platform understands the context and provides immediate natural language answers accompanied by data breakdowns, removing the technical barrier between complex spreadsheets and operational decision-making.
While traditional AI tools in the industry are limited to 'reading' and reporting data, this platform also has 'writing' capabilities, enabling it to act like an agent within the plant. Rather than just observing material streams, it can actively create custom quality reports and set operational alerts in seconds based on its deep domain knowledge of the recycling process.
“With the introduction of our new agent-based platform, recycling plants now gain a new cognitive layer,” says Nicolas Braem, CEO and Co-Founder of PolyPerception. “Data is no longer just reported – it is interpreted, explained and transformed into relevant insights in a few seconds. Operators can interact naturally with their plant, ask questions, explore material behavior and receive clear, actionable answers in real time.”
Open data and advanced search features
This groundbreaking technology provides full transparency by allowing recyclers to integrate plant data directly into their existing management systems. This enables managers to query waste statistics or purity levels through their own dashboards without needing to log into a separate system.
The platform also introduces two powerful new search methods to help plants respond to changing material streams:
- Similarity search: Operators can right-click a problematic object, such as an electronic vape, to instantly identify every other visually similar item in the stream. This is critical for spotting fire hazards like batteries without the need to train a new AI model.
- Text and brand search: Users can search for specific brands or object types, such as 'filled refuse bags' or 'diapers', to see exactly what is passing through the facility in real time.
“AI has always been part of TOMRA’s DNA, but we are now entering an entirely new phase," says Lars Enge, EVP and Head of TOMRA Recycling. "With our acquisition of a majority stake in PolyPerception, we are moving beyond AI as a sorting tool to AI as a central intelligence for the recycling plant. By combining our advanced sorting systems and digital solutions with PolyPerception’s AI platform we are creating an end-to-end solution that doesn’t just optimize machines but fundamentally redefines how plants operate.”
