CoSupport AI
What is Ameru?
Ameru introduces an innovative approach to waste management through its AI-driven smart bin system. Designed for both office and public environments, these bins leverage artificial intelligence to automate the process of waste sorting, contributing significantly to sustainability goals and promoting a Zero-Waste future. The system aims to make recycling more efficient and accurate.
At its core, Ameru utilizes advanced technology, including an 8MP high-precision camera and processing powered by Nvidia chips, to achieve over 95% accuracy in AI sorting across more than 90 waste categories. Each unit features a large 10.1-inch responsive display providing user feedback and connects to the cloud via Wi-Fi for continuous updates and access to interactive waste analytics reports. A fast and precise pan-tilt dumping mechanism ensures waste is correctly allocated.
Features
- AI Sorting: Achieves over 95% accuracy in automated waste sorting.
- Waste Detection: Identifies 90+ distinct waste categories in real-time.
- High Precision Camera: Equipped with an 8MP camera for clear waste item recognition.
- Nvidia Powered Computing: Utilizes Nvidia Jetson Orin Nano for robust AI processing.
- Large Responsive Display: Features a 10.1' Full HD screen for user interaction and feedback.
- Cloud Connectivity: Always connected via Wi-Fi for updates and data transmission.
- Waste Analytics: Offers interactive web reports based on collected waste data.
- Fast & Precise Dumping Mechanism: Employs pan-tilt mechanics for efficient disposal.
Use Cases
- Automating waste sorting processes in corporate offices.
- Enhancing recycling efficiency in co-working spaces.
- Implementing advanced waste management solutions in public venues.
- Tracking and analyzing waste generation trends for businesses.
- Providing real-time guidance to users on proper waste disposal.
- Supporting corporate sustainability and Zero-Waste initiatives.
Helpful for people in the following professions
Ameru Uptime Monitor
Average Uptime
100%
Average Response Time
125.73 ms
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