Closets Closets Closets
Google I/O HackathonWe built a B2B AI styling copilot for retail businesses that helps store associates turn a customer’s vague preferences into personalized, catalog-grounded outfit recommendations. Rather than replacing associates, our system supports them as they converse with customers, apply their styling experience, and guide shoppers toward the look they are trying to find. Powered by Gemini 3.5 Flash, the copilot helps interpret customer conversations, reason over available products, and refine recommendations in real time. The system can also provide lightweight visual try-on support using NanoBanana to help customers imagine an outfit before committing. To keep the experience practical and trustworthy, recommendations are guardrailed around real catalog items, customer feedback, and associate review. We also use agent-based guardrails to detect and reject poor or misleading try-on generations before they are shown to the customer. For customers, this creates a more confident, personalized, and engaging in-store shopping experience. For retailers, it can improve conversion, increase basket size through better outfit recommendations, and help associates serve customers more efficiently. Our long-term vision is to connect store inventory with customer preferences and personal closet context, creating a human-centered AI platform for the future of retail.
