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Accelerating the transition to a circular economy with real-time AI analysis.

Now Live in Providence

Waste Sorting,
Reimagined by AI.

Sortacle uses advanced computer vision and robotics to segregate waste at the source.

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Recyclable Items
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Non-Recyclable Items

Computer Vision

Our proprietary model identifies materials (PET, HDPE, Paper) in under 120ms with 98% accuracy.

Robotic Sorting

Automated flippers divert waste into the correct bin instantly, removing human error entirely.

Real-Time Data

We track every item sorted, providing universities and cities with actionable waste insights.

Geospatial Waste Density

Live inference nodes detecting high-density plastic waste clusters.

Community Impact

Trees Saved 3.10
Energy (kWh) 834
CO₂ Avoided 556.3kg

Live Composition

Avg. across active bins

Sorting History

Total items processed

Jan 26 - Feb 1

The Global Challenge

Global recycling rates are stalled below 9%. The primary culprit is contamination—when non-recyclable items are mixed into recycling bins, causing entire batches to be sent to landfills. Manual sorting is dangerous, expensive, and inefficient.

The Sortacle Solution

Sortacle uses Computer Vision and low-cost hardware (Raspberry Pi) to automate waste segregation at the source. By identifying materials the moment they enter the bin, we prevent contamination before it happens.

  • Autonomous Sorting: No human intervention required.
  • Real-Time Data: Live tracking of waste generation.
  • Scalable Design: Built on affordable, open-source hardware.

Our Goal

To prove that smart infrastructure can increase recycling purity to 95%+ while reducing operational costs.

User Experience

The user simply drops an item into the bin. The system creates a seamless "drop-and-go" experience.