Catecut is an apparel retail tech startup whose mission is to help apparel retailers and online marketplaces around the world address the global returns problem and better serve their online shoppers.

Our approach is to provide solutions that help online shoppers find the clothing that will makee them look and feel their best, while eliminating the #1 cause of clothing returns: poor fit.

While e-commerce has opened up exponential sales opportunities, it's also significantly increased returns, deepening the financial loss and materials waste from fashion retail.

By creating ecommerce solutions that eliminate the incidents of poor fit, online shoppers can be happier and more confident with their purchases, keeping more of what they buy, and curtailing the practices of bracketing and returns.

Heiðrún Ósk Sigfúsdóttir is Founder and CEO of Catecut. She has a proven track record in establishing and managing companies, notably founding the sustainable fashion brand Dimmblá in 2013, which achieved both domestic and international success. She also worked with Magasin du Nord in Denmark to study consumer behavior. Heidrun is adept in business administration, marketing, retail operations, and finance. Before her foray into entrepreneurship, Heiðrún managed several departments and projects within the development division of Össur, a leading global orthopedics manufacturer. Heiðrún holds a Masters in Business Administration from the University of Iceland.

Anna Friðrikka Gunnarsdóttir is Founder, Chief Stylist. She is a professional wardrobe stylist by trade, with over 20 years of experience. She founded and operated the successful styling school, "Anna and The Look," attracting hundreds of students worldwide. Anna has had a roster of style and presentation clients, including companies, senior executives, politicians, and presidential candidates. For Catecut, Anna collaborates closely with our product and development team to ensure garment attribute and matching accuracy from her extensive styling experience is at the forefront of our AI models and algorithms.

We have developed our own AI models, trained to identify and categorize clothing and style attributes from a retailer's product imagery. The deep tagging enriches each product description with complete garment attribute tags, improving the retailer's existing search functionality and SEO.

Our AI and algorithms, collectively the Matching solution, are trained on the principles of professional wardrobe styling to understand which garments' cuts and fits from a retailer's apparel inventory, best match each individual shopper's body structure.

Our Matching solution is embedded into the retailer's online store, for seamless integration, and makes recommendations based on each shopper's unique body structure, during their online shopping experience.

Behind all of it is an actionable data and metrics platform to track trends, and help retailers make cross-functional decisions on inventory and customer engagement gaps.