Скачать или смотреть ‘Zara Meets Netflix’—The Fashion House Where AI Removes Designers And

Overstock in the fashion industry is a major barrier to sustainability due to decades-old buying strategies based on high volumes and low unit prices. As a result, many fashion companies are not openly discussing overstock, as their bulging inventory often ends up being incinerated. On the face of it, it may seem a difficult problem to solve. One startup has built a radically innovative business model, replacing designers and buyers with data, to avoid speculative order volumes. In doing so, the brand is also redefining fashion design and production models and, crucially, the value proposition for its customers. Described by its founder Ramin Ahmari as “Zara meets Netflix NFLX ,” FINESSE creates the products their Gen Z target audience want when they want them, and in the quantities they demand. Armed with proprietary deep tech, FINESSE eliminates the subjective style-tendencies of individual designers and speculative order quantities of buyers, thereby slashing the traditional 5 month lead time to just 25 days and eliminating overstock. FINESSE officially launches today and I interviewed Ahmari, a recent computer science graduate with investment banking experience, to find out how (and why) he raised $4.54 million in pre-seed and seed funding to grow the first AI-led fashion house. Ahmari’s idea for FINESSE came from his propensity to solve inefficiencies with data and a personal relationship with fashion. As a queer non-binary person of color navigating uniquely different identities, Ahmari has used fashion as both a suit of armor and a tool for self-expression. During a Zoom interview, he explained how investment banking had given him crucial insight into how financial institutions use natural language processing algorithms to analyze large amounts of online data from sites including Twitter, to predict market and stock price shifts. Applying this concept to fashion, he explained that the Gen Z community's online interactions have notable language traits. These can be analyzed to determine preference for certain products and other key factors defining the next fashion trends. Whilst data analysis for trends is not new, FINESSE is operating using deep tech in an unparalleled way, combining time series analysis, natural language processing, and computer vision within its machine learning algorithms, indicating the fashion trends Gen Z most want at any given moment. The philosophy behind this fashion house is also radically different.  Ahmari says: “Fashion’s birthplace has moved from the catwalks of New York, Paris to social media.” Drawing on the style and language of Gen Z and their favored influencers as the cultural and aesthetic driver for FINESSE, he explained that the notion of a single muse endlessly inspiring fashion collections “borders on human arrogance.


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