Minimist raises €1 million to transform second-hand retail with AI
Vienna-based startup Minimist has secured a €1 million pre-seed round to build an AI-driven infrastructure layer for the booming second-hand market. The company aims to become the “invisible backbone” of global resale by automating how retailers list, manage and sell pre-owned goods online.
The round was led by Tilia Impact Ventures, with support from InvestEU, alongside several angel investors and family offices. The financing is further backed by a Wien Lebensqualität grant from Wirtschaftsagentur Wien, underlining the city’s push to support sustainable innovation.
AI-powered backbone for the circular economy
Founded in 2024, Minimist is developing an AI-powered listing engine designed specifically for second-hand retailers. By automating product recognition, pricing suggestions, categorisation and description generation, the platform helps merchants digitise inventory at scale and sell across multiple online channels with minimal manual work.
CEO Stephan Hofmann said that rethinking inventory management and eCommerce for a truly circular economy is critical if countries are to meet their climate goals. He noted that the oversubscribed round gives the team the resources to unlock more value for existing and future customers worldwide.
Scaling second-hand shopping to feel like buying new
Minimist’s long-term mission is to make second-hand shopping “indistinguishable from buying new” in terms of speed, accuracy and user experience. Its technology is built to integrate behind the scenes with resale shops, marketplaces and logistics providers, turning fragmented offline stock into searchable, standardised online catalogues.
By reducing operational friction for retailers, the startup hopes to accelerate the shift away from linear consumption models. As regulators and consumers increasingly demand more sustainable options, AI-enabled resale infrastructure like that of Minimist is emerging as a key tool to extend product lifecycles and reduce waste at scale.