Increased recycling rates through AI object recognition 

  • Recognizing individual data classes of waste using deep learning methods

  • (weight |  size  |  shape  |  object type  | usage origin  |  material type  |  damage  |  colour)

  • Real-time process data visualized on intuitive dashboards

  • Inexpensive hardware for various points in the processes of your sorting system

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Real-time process data with intuitive dashboards

  • Process monitoring and alerts

  • Data-based process optimization  

  • Digital quality control and automated reports

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Protect your assets from hazardous objects

Preventive maintenance and cost reduction through the detection of hazardous objects damaging sorting machines and recycling-plants

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Increase throughput and sorting quality through the optimized control of inventory systems

  • Control of the sorting modules depending on fluctuating material flow properties (input quantity, suction level, threshold values, etc.)

  • Networking with legacy systems

We are working on our vision - the AI sorting machine of tomorrow

  • Significantly cleaner sorting with high throughput

  • Only 1 machine for 4 different material flows

  • New material flows possible (e.g. food & non-food)

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1 Material and object properties are recognized by visual sensors and AI

2 Reinforcement learning controls the nozzles to direct objects into the correct channel

3 Sensor technology in the channel sends feedback to the algorithm for self-learning optimization

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WeSort.AI GmbH

Hublandplatz 1

97074 Würzburg

 

+49 1578 9362996

 

contact@wesort.ai

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