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Retail owner and entrepreneur Julie Bornstein sees generative artificial intelligence as a way to help people discover the fashion they want to buy.
Her new startup, Daydream, has raised $50 million in seed funding to build a platform that uses generative AI, machine learning, and computer vision to help people search and discover items. People can search using natural language prompts like “pastel dresses for a garden party in Charleston” or “luxury suits for a business conference in New York,” and Daydream will surface options from participating brands. The round was co-led by Forerunner Ventures and Index Ventures, with participation from Google Ventures and True Ventures.
“I’ve always been obsessed with search and personalization, and I couldn’t be more excited about the significant advances made in AI over the past year,” Bornstein said in a statement. “We’re finally building an intelligent online shopping platform that makes it easy and fun for consumers to discover the products they love from the world’s best brands and retailers.”
The company was co-founded by a group of executives with backgrounds at Microsoft, Google, Amazon, Nordstrom, Farfetch and Pinterest: Chief Technology Officer Matt Fischer, Chief Product Officer Dan Carey, Chief Commercial Officer Lisa Green and Chief Strategy Officer Richard Kim (Both Green and Kim worked at Bornstein’s previous fashion startup, The Yes).
Daydream aims to solve search problems, especially in the commercial sector. “Search experiences have become increasingly cluttered and inefficient. Search is essentially defined by ‘show me options,’ but the value proposition breaks when the options are infinite. Services, on the other hand, are defined by ‘show me the only best option for me,’ but their value grows exponentially when there are a plethora of options available,” said Kirsten Green, founder and managing partner at investment firm Forerunner. “It’s clear that advances in AI can achieve much better results. New service offerings, working with consumers, have the potential to significantly reduce their friction and redefine what context-aware search can be.”
