Turning organic waste into insight with AI

Organic waste streams often contain unexpected contaminants – from plastics to hazardous items – that can disrupt processing and compromise sustainability goals. Our solution empowers operators to:

• Identify contaminants in real time, preventing costly downtime and equipment damage.
• Improve recycling purity, supporting circular economy objectives.
• Enhance safety and compliance, reducing operational risks.
• Generate detailed quality reports per supplier and delivery.

• AI-Powered Detection: Deep learning models trained to recognise contaminants in moving organic waste (vegetable, fruit, and garden waste) streams.
• Robust Hardware Integration: High-speed cameras and edge AI systems installed on drop zones and bunkers for instant analysis.
• Scalable Architecture: Designed for expansion across multiple facilities and adaptable to evolving waste profiles.
• Continuous Optimisation: Retraining workflows and intuitive dashboards for ongoing performance improvements.
• Fully automated workflow: This project turned into a fully automated workflow, where the incoming delivery is identified by license plate cameras, the cameras start and stop capturing automatically when the load is dumped into the bunker. The AI model decides the quality of the material and instructs the operator by means of light indicators what to do with it.
• Quality report & integration: The quality report is stored and delivered to the customer data storage where it is linked to the delivery in the ERP system.

This project demonstrates how AI can transform waste management, delivering efficiency, safety, and sustainability at scale. By combining operational expertise with cutting-edge technology, we’ve helped our customer set a new standard for smart waste processing.