How Daydream raised $50m and launched AI-powered fashion search

Daydream, the fashion search startup founded by former Stitch Fix chief operating officer Julie Bornstein, launched in June 2025 with $50 million in seed funding. The platform debuted with more than 2 million products from 8,000 brands, using conversational and visual search powered by artificial intelligence to help consumers find items across multiple retailers.

Who is Julie Bornstein and why did she launch Daydream?

Bornstein previously led e-commerce at Sephora, Urban Outfitters and Stitch Fix, giving her decades of experience in digital retail. She identified fragmentation in online fashion shopping as the core problem Daydream aims to solve, noting that consumers must visit multiple sites to find what they want. The platform emerged from Bornstein's observation that existing search tools struggle to understand nuanced style preferences and intent.

Daydream positions itself as a shopping companion that helps users discover products through natural language queries and image uploads. Rather than simply returning keyword matches, the technology interprets context and style attributes to surface relevant items from its catalogue of brands.

How much funding did Daydream raise and who invested?

The $50 million seed round was led by Forerunner Ventures and Index Ventures. Other investors included Spark Capital and several angel investors from the retail and technology sectors. The size of the seed round was notably large, reflecting investor confidence in Bornstein's track record and the market opportunity for AI-driven fashion discovery.

Forerunner Ventures partner Eurie Kim joined Daydream's board, bringing experience from the firm's previous investments in fashion and retail technology companies. The funding was earmarked for product development, brand partnerships and building out the technology infrastructure to support conversational and visual search at scale.

What features does Daydream offer to shoppers?

Daydream's core functionality centres on two search modalities: conversational queries and visual uploads. Users can type natural language requests describing what they are looking for, including specific style attributes, occasions or fit preferences. The platform also accepts image uploads, allowing shoppers to find similar items based on a photograph.

The system uses AI to understand the intent behind searches and match users with products across the 2 million items in its catalogue. When a user finds a product they want to purchase, Daydream directs them to the brand or retailer's site to complete the transaction. The company earns revenue through affiliate commissions when users make purchases through these referrals.

Key features include:

• Conversational search that interprets natural language queries about style, fit, occasion and price
• Visual search allowing users to upload photos and find similar items
• Aggregation of more than 2 million products from 8,000 brands in a single interface
• Personalised recommendations based on search history and preferences
• Direct links to retailer sites for checkout and fulfilment

How does Daydream position itself differently from competitors?

In coverage by Glossy, Bornstein emphasised that Daydream is not trying to be a marketplace or retailer, but rather a discovery tool. This positioning distinguishes it from platforms that hold inventory or manage transactions end to end. Instead, Daydream focuses on solving the search and discovery problem, then hands off the customer to the brand or retailer.

The company also differentiates itself from traditional affiliate sites by emphasising the AI-powered conversational interface rather than static links or editorial curation. Bornstein has stated that the goal is to create a more intuitive and personalised shopping experience that feels like having a knowledgeable assistant.

The June 2025 launch came at a time when generative AI was rapidly being adopted across retail, but few fashion-specific applications had achieved significant scale. Daydream's approach of aggregating products from thousands of brands, rather than focusing on a single retailer, aimed to capture consumers who wanted choice without the friction of visiting multiple sites.

What challenges does Daydream face in the market?

The fashion search and discovery space is crowded, with established players including Google Shopping, Pinterest, Lyst and various retailer-specific tools. Daydream must convince both consumers and brands that its AI-powered approach delivers superior results. Building brand partnerships at scale requires ongoing relationship management and technical integration to keep product catalogues current.

The affiliate revenue model also presents challenges. Commission rates in fashion are typically modest, meaning Daydream needs high transaction volume to generate significant revenue. The company must balance user experience with monetisation, ensuring that search results prioritise relevance over affiliate payouts.

Data quality and freshness are critical. With more than 2 million products from 8,000 brands, maintaining accurate information on pricing, availability and product attributes requires robust data pipelines. Shoppers who click through to a retailer only to find an item is out of stock or priced differently may lose trust in the platform.

Finally, consumer behaviour change takes time. Many shoppers have established habits, whether starting searches on Google, browsing Instagram or going directly to favourite retailer sites. Daydream must build awareness and demonstrate clear value to shift these patterns and become a regular part of the shopping journey.