Artificial Intelligence (AI) has reshaped numerous industries in the past three years, driving rapid advancements and sparking both innovation and controversy. Initial concerns centered around copyright infringement and the proliferation of fake AI-generated images.
However, many companies, from media giants to the beauty industry, have embraced AI with a more strategic and integrated approach. For instance, the Financial Times (FT) announced a partnership and licensing agreement with OpenAI to enhance ChatGPT's capabilities by integrating attributed content from FT journalism. Similarly, Estée Lauder Companies adopted ChatGPT Enterprise to optimize its operations.
The fashion industry has also shown significant interest in leveraging AI, though applications vary widely. While some brands experimented with AI-powered advertising, fashion retailers are adopting AI tools to update products rapidly, align with social media trends, and meet evolving consumer expectations.
Enter Raspberry AI, a generative AI platform tailored specifically for fashion creatives (yes, do not confuse it with the computer module Raspberry Pi, two different things, even though the platform's name definitely evokes the computer module name...).
Founded by Cheryl Liu, a former private equity analyst at KKR who later worked with Amazon and DoorDash, Raspberry AI offers an end-to-end design solution for brands. The platform enables users to create lifestyle photography, product images, technical drawings and prints using images or simple text prompts.
With the support of the platform designers can therefore carry out a variety of tasks, from product testing to generating CAD files in just a few minutes, reducing the time and cost associated with traditional design workflows, including costly photo shoots.
Unlike general AI tools like Midjourney, DALL-E, and Adobe Firefly, Raspberry AI is trained to understand the nuances of fashion-specific and manufacturing terminology. For example, Liu highlights in press releases that when describing a "fuzzy sweater," Raspberry AI accurately interprets the underlying design and material details that other platforms might overlook.
The platform also offers enhanced functionality: designers can generate images directly from sketches, a feature that sets Raspberry apart and caters specifically to the needs of fashion professionals.
Tutorial videos on Raspberry AI's YouTube channel offer a clear glimpse into the platform's capabilities. Tutorials show how to transform sketches into photorealistic renderings that capture the minutest details of how products will look or how accessories can be visualized in new patterns, fabrics, or even transformed using not just a pattern or a fabric but unconventional materials, such as a transparent plastic cube. The platform also enables users to blend garments like blazers and outfits to create entirely new designs.
This transformation in workflow doesn't just save time on repetitive tasks, but mainly helps designers playing around and refine certain products, coming up with new ones based on a company's best-sellers. By accelerating design cycles, the platform enables more SKUs (stock-keeping units), faster time-to-market, and reduces the risk of overstock, a persistent challenge in the fashion industry.
Since its launch in 2022, Raspberry AI has quickly become integral to the workflows of leading companies, including Boston Proper, Gruppo Teddy (Terranova, Rinascimento, Calliope and QB24), Li & Fung, MCM Worldwide, and Under Armour. So far Raspberry AI has attracted 70 customers.
The company's rapid growth has also drawn significant investment. Raspberry AI has indeed just secured $24 million in Series A funding, led by Andreessen Horowitz, a firm specializing in technology-driven companies across AI, healthcare, consumer technology, and more. Other participants included existing investors such as Greycroft, Correlation Ventures, and MVP Ventures. This follows a $4.5 million seed round completed just 10 months earlier.
The use of Raspberry AI presents clear advantages. By saving time and reducing costs, the platform allows companies to bypass lengthy physical sample orders. Designers can instantly visualize products that would typically take weeks to produce and explore hundreds of potential combinations something that would be prohibitively expensive and time-consuming in a traditional setting.
However, these benefits come with significant drawbacks. The focus on rapid production and countless iterations could exacerbate the problem of overproduction, emphasizing quantity over quality (and talking about quality, it must still be improved in some features of the Raspberry AI platform, especially when it comes to lifestyle photography...). In a world already saturated with trends, the true value lies indeed in creating timeless style over fleeting fads. As Vivienne Westwood famously championed, "Buy Less, Choose Well, Make it Last," a principle many in the industry struggle to uphold.
Besides, while reducing production times and costs can enhance profitability (yet again costs may rise as you may end up handling a larger number of SKUs, that can complicate storage, tracking, and replenishment....), it may also lead to job cuts and contribute to unemployment. The push for faster output also risks feeding consumer overconsumption rather than addressing deeper questions about sustainability and empowerment through design. And, last but not least, designers may grow reliant on the platform, potentially diminishing traditional skills like sketching or manual design processes.
Yet maybe Raspberry AI's potential extends beyond these functions. The company's machine learning team collaborates with enterprises to custom-train AI models that learn a brand's unique DNA, aesthetic, collections, and fabric libraries. This feature offers exciting opportunities, transforming the platform into a personalized archive that can be endlessly remixed, something ideal for large fashion houses looking to maintain consistency while exploring innovation.
In addition to design applications, Raspberry AI could revolutionize the way consumers engage with fashion in other ways. Imagine interactive installations at exhibitions where visitors could play around with archives or experiment with designs, or trade show environments where companies and buyers collaborate to customize catalogue items in real-time to provide more unique selections.
Time will tell if this will happen for Raspberry AI. In the meantime, the company plans to leverage its recent funding to recruit talent across engineering, sales, and marketing, while expanding into home decor, furniture, and cosmetics product design, aiming to showcase the platform's versatility and its potential to transform multiple industries.
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