SAIZ funding news – Berlin-based SAIZ Raises €2.5 Million in Seed Funding

This investment follows Spread Ventures' previous year's capital round for the company, which was headed by EnjoyVenture and Salica Investments. Reputable European fashion labels including Oliver, Tom Tailor, Jack Wolfskin, and many more have partnered with the firm. The round was also attended by Houghton Street Ventures, which only makes investments in former LSE employees.

It's solutions take exact measurements of garments into account. SAIZ Recommender and SAIZ Studio, the company's key solutions, offer customers precise garment measuring enhancements and product optimisation.

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The system provides precise fit predictions, thorough analysis, and comprehensive reports for every product within e-commerce platforms using data collected by it Recommender. It is extending the range of products it offers till the end of 2024 by adding a comprehensive set of tools made to address fit and sizing issues in the fashion industry. According to the corporation, return rates have dropped by 7%–10%.

Marita, co-founder of SAIZ said, "For us, it has always been about transforming an industry and working with pioneers to solve the sizing and fit challenge once and for all, We aim to revolutionize the fashion industry by providing data-driven, precise fit solutions that enhance customer satisfaction and promote sustainability."

EnjoyVenture and Salica Investments (previously known as Hambro Perks), two of the best venture capital firms in Germany and the UK, led the round of oversubscribed fundraising.

Laura Noorani from Salica Investments remarked, “We’d been looking to invest in this space for a long time, but struggled to find a differentiated solution until we met Svenja and Marita, and saw what the team at SAIZ is building."

About SAIZ

SAIZ Recommender & SAIZ Studio: their AI and data-driven softwares, providing size recommendation for users, optimizing design with customer analytics and reducing e-commerce returns rates by aligning body and garment data.