We have developed an advanced computer vision system that transforms how e-waste is processed.

Using high-resolution cameras and powerful AI, our technology can identify and analyze tens of thousands of electronic devices and components—from smartphones and laptops to individual circuit boards—with speed and accuracy. 

This system provides humans with the data and insights needed to make faster, smarter decisions and significantly improve recycling efficiency, compliance and safety.

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Our advanced computer vision system provides a wealth of information about each item it detects, empowering operators to make smarter decisions and optimize their workflow.

Real-Time Material Intelligence

• Object Detection: The system instantly identifies specific devices and components, from smartphones to circuit boards, and can assign asset IDs to devices and even sub-components.

• Material Breakdowns: It provides a detailed list of materials within each device, including valuable metals and plastics, to help maximize recovery.

• Hazardous Material Identification: The system flags potential hazards like batteries, large capacitors, or chemical residues, ensuring the safety of the operator.

• Processing Guidance: If necessary, instructions for manual disassembly or recycling along with safety warnings for known hazards.

Advanced Automation & Efficiency

• Automated Sorting: The system can be paired with robotics to fully automate the sorting process.

• Auto Cut Line: It can make precise, data-driven decisions on what can be put into shredders, what could better processed manually and what might be fit for resale.

• Resale Revenue Potential: The system estimates the market value of many devices or their internal components to help maximize profit. Our system can reference pricing from popular resales channels and estimate cost to refurbish.

Data and Compliance Reporting

• Facility Data Warehouse: The system collects and reports on facility throughput and productivity, providing key operational insights.

• Predictive Analysis: It offers predictive reporting on carbon avoidance and the potential weight and value of recovered materials.

• Compliance & Auditing: The system can provide automated chain-of-custody & compliance reporting, such as asset-id level photo certifications of destruction, for audit purposes. 

  • • • Our system can help automate certain requirements under R2, E-Stewards, and NAID certifications.

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The Bottleneck: Manual Disassembly

Manual de-manufacturing is the primary bottleneck in e-waste recycling for many categories of devices, such as smartphones. While it is more safe and precise than fully automated methods, it is slow, labor-intensive, and poses risks to the worker. During this process, technicians may be exposed to hazardous chemicals like mercury, lead, and cadmium.

Mitigating Hazards and Fires

The risks of improper handling are severe. Attempting to shred a device with a lithium-ion battery can cause a dangerous explosion, while mishandling a large capacitor can lead to fires. To prevent these hazards, human operators must manually remove these materials before the rest of the device can be shredded.

Battery fires are the #1 driver of increasing insurance costs for e-waste processing facilities.

Our Automated Solution

Our system automates the most dangerous and time-consuming parts of this process. Using computer vision, our technology precisely identifies and maps the exact location of batteries and other hazards within a device.

This data then guides a precision tool to cut out hazards, such as batteries. This process is particularly effective for smartphones, laptops, and tablets, allowing for the quick and safe removal of batteries for separate processing. This process reduces the per-device disassembly cost for laptops and tablets by approximately 80%.

Our system is also learning how to strategically cut through fasteners and adhesives to enable rapid disassembly for a broader array of devices.

By automating this critical step, we can streamline the recycling process, making it safer, more efficient, and scalable.

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