AIRECO offers an AI-driven ecosystem that optimizes recycling operations through advanced computer vision and robotic automation. Their platform enhances sorting accuracy and processing throughput, transforming waste into higher-value assets for recycling facilities.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional recycling processes are hindered by labor shortages, hazardous working conditions, and the inherent inefficiencies of manual sorting. These factors limit sorting purity, processing capacity, and overall profitability, preventing the realization of a truly circular economy.
Solution
AIRECO provides an AI-driven ecosystem designed to optimize the entire recycling value chain. The platform integrates advanced computer vision and robotic automation to enhance sorting accuracy and processing throughput, transforming waste into a higher-value asset. By leveraging data science and machine learning, AIRECO enables recycling facilities to improve operational efficiency, reduce labor dependency, and increase profitability while contributing to sustainable resource management. The system offers end-to-end visibility and control, facilitating a more dynamic and profitable circular economy.
Target Audience
The primary target audience includes recycling facility operators, waste management companies, and manufacturers seeking to optimize their resource recovery processes and enhance the economic value of recycled materials.
Features
- RECO Vista: AI-powered computer vision system for precise quality inspection and high-purity sorting of recyclables using deep learning models.
- RECO Delta: High-speed robotic sorting arm designed for stable sorting speed and precision, adaptable to existing facility layouts.
- RECO Cognita: Data-science-based intelligent facility management system providing visualized data for operational decision-making and performance optimization.
- Synthetic data generation capabilities using NVIDIA Omniverse and Replicator to create diverse and high-quality training datasets for AI models, addressing data imbalance and labeling limitations.
- Continuous model training and data collection to adapt to evolving recyclable compositions and improve recognition accuracy.
- IoT and edge computing integration for real-time factory monitoring and maximized equipment performance.
- Web-based dashboard for cross-platform access to real-time data monitoring and report generation.