How Brands are Leveraging AI in the Design Phase: Raspberry AI Founder & CEO Cheryl Liu

About every six months on Retailgentic, we step away from the front end of agentic commerce to look at how AI is changing a different part of the retail stack. This week it’s product creation. Raspberry AI helps fashion brands and other retailers visualize, test and sell products before a physical sample exists. It’s part founder story and part look at what happens downstream when making products gets that fast. Highlights : 𝟭𝟱% 𝗵𝗶𝗴𝗵𝗲𝗿 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻: Raspberry measured 15% higher conversion on AI-generated e-commerce PDP imagery than on traditional photo shoots, at 80% to 90% lower cost. Cheryl thinks video and personalized content could push that to 25% within a year. 𝟭,𝟰𝟬𝟬 𝗶𝗻, 𝟰𝟱 𝗼𝘂𝘁: Wholesale brands walk into a Nordstrom or Bloomingdale’s buy meeting with 1,400 products and get 45 adopted. “1,400 products did not need to be created at all.” 𝟮.𝟯𝘅 𝘁𝗵𝗲 𝗗𝗶𝗰𝗸’𝘀 𝗼𝗿𝗱𝗲𝗿: One of the world’s largest sportswear companies changed products and styling live in front of the CEO of Dick’s Sporting Goods and came away with 2.3x the order. Across wholesale customers, orders are up 1.5x to 2.5x. 𝟭𝟮–𝟭𝟴 𝗺𝗼𝗻𝘁𝗵𝘀 𝘃𝘀. 𝟯–𝟭𝟰 𝗱𝗮𝘆𝘀: Most fashion brands take 12 to 18 months to get from a designer’s idea to a product you can buy. The fastest players get that down to three to 14 days. 𝗜𝗺𝗮𝗴𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗜𝗘𝗢): After SEO and GEO, Cheryl’s next frontier is measuring which angles, models and lighting convert for each brand. For some men’s brands that’s flat lays. For women’s brands it’s on-body. 𝟰𝟲𝟬 𝗯𝗿𝗮𝗻𝗱𝘀 𝗶𝗻 𝟮.𝟱 𝘆𝗲𝗮𝗿𝘀: Raspberry has been selling for about two and a half years and works with 460 brands. 70% of clients use it in three or more departments, most often design, e-commerce and marketing. 𝗙𝗼𝘂𝗿 𝘄𝗲𝗲𝗸𝘀 𝗲𝗮𝗿𝗹𝘆: One of the world’s biggest retailers had never turned in private-label design specs on time in 20 years. With Raspberry, they turned them in four weeks early. 𝗕𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗰𝘂𝘁-𝗮𝗻𝗱-𝘀𝗲𝘄: Raspberry trains its own models for fabric, drape and physical feasibility, and generates the tech packs factories need. “At the end of the day, someone has to cut and sew that.” Cheryl calls it starting at the end: show the finished product before doing the work. We loved this one, and we’ll have her back once she’s built more of the future she described. Enjoy the conversation. Timestamps: 00:48 Welcome to Retailgentic 02:04 Scot's intro: AI in product creation 03:27 Welcome Cheryl Liu of Raspberry AI 03:55 Cheryl's background: KKR, AWS, DoorDash 06:50 The origin story at DoorDash 08:05 Seven fingers and a glimpse of the future 09:50 12–18 months vs. 3–14 days 11:05 Amazon's press release meets Shein 13:35 From Shopify 2.0 to product creation 16:05 Fundraising and the $23M a16z Series A 20:25 Landing the first customers 22:35 Product-market fit and the 1,400-to-45 problem 25:10 Case study: 2.3x the Dick's Sporting Goods order 27:40 Moving downstream to e-comm, marketing and stores 30:35 The ROI: speed, wholesale lift, 15% better conversion 33:05 Can consumers design their own products? 37:05 Training their own models and generating tech packs 40:25 Where Raspberry is headed: new categories 43:25 IEO: image engine optimization 44:20 How will AI agents see product images? 44:50 Wrap-up 👉 Connect with Cheryl: https://www.linkedin.com/in/cheryl-jiaping-liu/ 👉 Learn more about Raspberry AI: https://www.raspberry.ai/ 🧠 Want to stay ahead in AI commerce? Subscribe and follow along: 📰 Subscribe to the free Substack: retailgentic.ai 📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic