Fashion search was broken. We fixed it.

For all the sophistication the internet has brought to commerce, fashion search remained stubbornly unsolved. You could book a flight, find a doctor, or navigate a city in seconds — but try to locate a navy crew-neck cashmere sweater that wasn’t boxy, wasn’t overpriced, and was actually in stock in your size, and you’d find yourself forty-five minutes deep in a rabbit hole with nothing to show for it but browser fatigue.

That specific frustration belonged to Julie Bornstein, Daydream’s CEO, whose career spans leading e-commerce at Nordstrom, Sephora and Stitch Fix and who understood exactly why the infrastructure of fashion discovery failed to keep pace with how consumers actually shop. Lisa Yamner, our CBO, brought the same conviction from years at the intersection of fashion and technology at Google and Condé Nast. Maria Belousova, our CTO, joined with a vision for how to build what the other two had spent careers imagining — the technical architecture to finally make intelligent, personalized fashion discovery real.

The diagnosis they shared was precise: general-purpose search engines were built to retrieve information, not to understand the layered, contextual, highly personal nature of a style decision. What was needed wasn’t a better search bar but an entirely different model, one that could hold nuance, learn preferences, and respond to the way people actually think about their style. And exactly the right people to solve it.