Catecut is an apparel retail tech startup whose mission is to help apparel retailers and online marketplaces around the world better serve their online customers while they shop.

Our approach is to provide product info solutions that help online shoppers find the clothing that will make them look and feel their best, while eliminating the causes of clothing returns: poor fit, and limited product information to make informed buying decisions.

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

Likewise, retailers are struggling to deliver product information in a timely and cost effective way, cutting into profits and impacting the customer experience. This leads to ill-informed and frustrated shoppers, increased returns and cart abandonment.

By creating e-commerce solutions that accurately describe garment design elements such as structure, shape, attributes, material stretch, and accurate color tones, online shoppers can make more informed buying decisions, find the best clothing for their body and style, and be happier and more confident with their purchases. They'll keep more of what they buy, curtailing the practices of bracketing and returns.

Heidrun Osk Sigfusdottir is Founder and CEO of Catecut. She has a proven track record in scaling product information organizations. She also notably founded the sustainable fashion brand Dimmblá in 2013, which achieved both domestic and international success, where her inventory was sold on marketplaces throughout Europe. Prior to this Heidrun worked with Magasin du Nord in Denmark to study consumer shopping behavior.

Heidrun is adept in business administration, marketing, retail operations, and finance. Before her foray into entrepreneurship, she managed the product information department, and projects within the development division of Össur, a leading global orthopedics manufacturer. Heidrun holds a Masters in Business Administration from the University of Iceland.

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 Product CommOps, 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 solution sends product information to the retailer's online store backend, enriching product pages with important product information, metadata and tags, that greatly improve search and discoverability during the 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.