Loopifyr's technology is designed to solve one core problem: making post-consumer textile data accurate, verifiable and actionable at scale.
Our system combines AI, image processing and data infrastructure to connect physical garments with digital intelligence.
From images to structured material intelligence
Loopifyr uses computer vision and deep learning models to analyze garments directly from consumer images. Our AI identifies garment type, material composition and condition, creating item-level insights before textiles enter physical collection or sorting systems. This infrastructure continuously improves through verified delivery feedback, ensuring increasing accuracy over time.
Turning fragmented signals into decision-ready data
Traditional collection systems generate volume, but not insight. Loopifyr transforms raw consumer interactions into structured datasets that can be measured, compared and reported. Brands gain visibility across locations, materials and outcomes — enabling compliance, optimization and strategic planning rather than estimations.
The right garment, to the right place
Using AI-driven matching, Loopifyr recommends the most suitable collection or recycling route for each garment based on material, condition and proximity. This reduces unnecessary transport, improves sorting efficiency and lowers both operational cost and carbon footprint.
Circularity fails without trust in data. Loopifyr's technology bridges the gap between digital interaction and physical reality — creating a reliable backbone for post-consumer circular systems.