Periodic Reporting for period 1 - SAIZ (AI and machine learning for less overproduction and more sustainable consumption in the fashion industry - a deep learning approach to understand products and customers better.)
Periodo di rendicontazione: 2023-06-01 al 2024-05-31
Before initiating the project, SAIZ had already launched two MVPs – SAIZ Recommender and SAIZ Studio - in the market. SAIZ Recommender offers accurate size recommendations to online shoppers through advanced machine learning algorithms. SAIZ Studio gives apparel brands the opportunity to learn more about their customers, products, and the product performance and provides insights for product optimization.
One primary objective within the Horizon Europe Programme was to ensure product-readiness for mass scaling. Additionally, we aimed to expedite the scaling and go-to-market strategy of SAIZ, ensuring broader reach and impact. Thereby, our focus remains on continuously improving our offerings to deliver maximum value to both online shoppers and apparel brands.
On the technical side, we have prioritized initiatives to facilitate self-onboarding for customers, focusing on implementing essential elements such as automated testing protocols, scalable database management systems and a robust infrastructure tailored to machine learning and data storage. By strategically investing in these areas, we have strengthened our platform to seamlessly support client onboarding processes. These concerted efforts have not only improved our operational efficiency, but also enabled us to effectively serve a growing user base. A remarkable result is that we now serve 5 million users per month with our SAIZ Recommender, with a loading time of under 200ms, highlighting the great strides we have made in delivering an exceptional user experience.
To ensure the realization of this goal, key needs include substantial investments in research and development, securing necessary funding, and advocating for a supportive regulatory framework that incentivizes apparel brands to embrace data-driven approaches in product design and innovation.