Almost a year after collecting a mega seed round of $ 50 million, the startup of electronic commerce veteran Julie Bornstein Dream He is launching his chatbot to ia to buy with an focus on fashion.
After trying the product with selected users, the company is launching today the chatbot to all users in a public beta version. People can register in the chatbot, which will ask for their name, date of birth, rank of prices they buy and brand preferences, if any.
You can write a query like “I want a wedding dress to be used this summer in Paris” or load an image and add more context through the text to find a clothing article.
Users can save any element in a collection that they believe or refine their search by writing the chatbot on the left side. If you like an element but want to modify some aspects, such as colors or style, you can touch the “Say more” button in any of the elements to edit the search.
According to the parameters that users provide during incorporation and feedback, the application receives from them, keeps different elements. Daydream creates a style passport for them that drives many suggestions. The web application also shows daily inspiration for elements and accessories that possibly coincide with your liking.
At this time, Daydream does not have an integrated payment flow, so when a user clicks on an element, a merchant to complete a transaction is redirected. The startup is taking a percentage cut of each sale. Daydream said that at launch, it has more than 8,000 brands on the platform, with the company incorporating new merchants at no cost.
Bornstein, who has held executive positions in companies such as Nordstrom, Urban Outfitters, Sephora and Stitch Fix, said that in the last year, the company was working in technology to bring a catalog of different brands to a place and a search for revolts to adapt to AI.

“Having worked in electronic commerce throughout my career, the search has always been the forgotten child, and it never worked very well. Somehow, people were trained to be very narrow in the way they look for anything in the fashion world. And even with my previous beginning, we could not get people to go beyond ‘a red dress’, Borstien told TechCrunch for a call.
“But once the Chat GPT threw himself, you know, consumers began to be trained on how to think about the potential to request. And what we are trying to do is help him ask what you want to ask, if I have a occasion, or I have this need, or I am looking for this kind of things so specific,” he said.
Maria Belusova, who joined the company this year as CTO, said Daydream worked hard to understand the nuances of the articles in the catalog. She said that the traditional search only showed items from buyers based on labels that coincide with their keywords, which do not work in today’s world, where customers request longer consultations.
“We are doing a lot to understand the details of the product, such as knowing stylistic attributes such as ornaments, silhouette or even social attributes such as who would wear this dress, such as a girlfriend or a guest at a wedding. We are also using visual recognition of images to satisfy detailed consultations where a customer describes the exact product they want, Belúsva said.

During the next year, Daydream will allow users to give the tool a more explicit feedback, as “do not show me four -inch heels.” It also plans to experiment with a characteristic that will allow users to request a good coincidence with an existing element with personalized suggestions. In addition, he wants to rely on the aspect of social exchange by allowing users to share their articles saved with friends and family for suggestions to buy. Another feature in which Daydream is thinking is to take an existing collection of another user and modify it for their own needs using AI.
While Daydream team has years of experience in electronic commerce and focuses on fashion, new companies such as Detft and Cherry are also building a multimodal shopping search. Meanwhile, technological giants such as Amazon and Google focus on characteristics that use AI to search for multiple sites and find the right element for users.
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