Ruoxuan Pan, Junjie Tang and Huixiang Chen 的作品 Closet AI 转售电商平台

设计师 Ruoxuan Pan, Junjie Tang and Huixiang Chen 的作品 Closet AI 转售电商平台荣获了极具声望的 2026 A' Design Award 铁奖，竞赛类别为移动技术、应用与软件设计。

铁移动· 2026

Closet AI 转售电商平台

设计师： Ruoxuan Pan, Junjie Tang and Huixiang Chen

移动类别A' Design Award 铁奖获奖者

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关于 Closet AI 转售电商平台

Closet AI 是一款面向二手服饰的移动端转售平台。用户上传服装图片后,应用会依据转售数据自动生成品类归类、商品描述与建议售价。在议价环节,系统会草拟回复建议,用户可自行编辑或确认采纳,最终决定权始终掌握在卖家手中。平台还设有奖励机制,通过任务、徽章与里程碑记录用户的进阶历程。该平台基于对转售行为的研究开发而成,旨在减少阻碍人们参与循环时尚的时间与精力成本。

荣誉认可

关于A' Design Award 铁奖

Closet AI 转售电商平台是 A' Design Award 铁奖得主，这是授予满足人们对设计精良的创作所期待的专业与工业要求的优良设计的声望认可。铁奖得主展现出其创作者的高超技艺、高度专业化、专家级的理解力和创造能力。预计Closet AI 转售电商平台将成为一件备受推崇、令人钦佩且被珍爱的创作，既实用又创新。这件赢得 A' Design Award 铁奖的设计整合了行业最佳实践，并拥有与专业知识和才华相结合的称职技术特性。Closet AI 转售电商平台因其严谨而受到尊重，旨在提供有助于让世界变得更美好的生活品质改善。

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由设计师提供的致谢、团队成员及技术详情。

独特属性: Closet AI is an AI powered resale platform designed to remove friction from circular fashion. It automates listing creation, pricing suggestions and negotiation support, transforming second hand selling into a streamlined experience. By integrating intelligent automation with a reward system that tracks impact and milestones, Closet AI reduces emotional barriers and encourages sustainable participation, making fashion circulation measurable and efficient.
尺寸与包装: Closet AI is a mobile first digital platform prototyped in Figma at 402 x 874 pixels aligned with iPhone interface standards. The system follows a modular and scalable architecture supporting real time feedback and adaptive interaction flows. A structured design system ensures visual consistency accessibility compliance and responsive layout behavior across future multi platform extensions.
标签: AI powered resale platform, UX/UI design, circular fashion system, sustainable commerce design, intelligent negotiation support, automated listing generation, user centered interaction design, behavioral incentive system, mobile marketplace experience, fashion sustainability innovation
生产技术: Closet AI was developed through a human centered design process informed by user research and resale behavior analysis. Insights into pricing uncertainty and negotiation stress shaped the interaction framework. Computer vision, natural language generation and data driven recommendations were integrated as supportive technologies. Iterative prototyping refined the system into an assisted workflow that reduces cognitive load while preserving user agency.
设计挑战: The major challenge was balancing automation with user autonomy. Fully automated systems risk removing agency, while manual systems maintain friction. Closet AI introduces assisted intelligence, where AI drafts and recommends actions but users retain final control. Another challenge was integrating sustainability incentives without overwhelming users. The solution implements subtle impact indicators and milestone badges that reinforce behavior without creating gamified complexity.
研究: Type of Research: Qualitative behavioral research and workflow analysis. Research Objectives: To identify barriers preventing users from participating in clothing resale despite sustainability awareness. Methodology: User interviews journey mapping and competitive benchmarking. Data Collection: Structured interviews usability testing and prototyping. Participants: Gen Z and Millennial users. Results: Identified negotiation fatigue and listing complexity as key friction points. Insights and Impact: Automation reduces participation barriers and supports circular fashion adoption.
灵感: Closet AI was inspired by the gap between sustainable intention and practical behavior. While many Gen Z and Millennial users want to reduce waste, resale platforms often demand time and effort. Listing creation, price negotiation and cross platform management introduce friction that discourages participation. The project explores how artificial intelligence can reduce this friction and connect resale with meaningful environmental impact.
项目周期: The project began in Dec 2025 in Austin and is currently in advanced prototype development.
操作流程: Users upload clothing images to generate automatic categories, descriptions, and smart pricing suggestions based on resale data. They publish listings, track engagement insights, and receive recommendations such as price adjustments or repost timing. Users complete quests, earn badges, and unlock milestones as they list, optimize, and close deals. During negotiations, AI drafts reply suggestions that users can edit or approve, keeping full control.
团队成员: Ruoxuan Pan
Junjie Tang
Huixiang Chen
图片版权: Main Image: Creator Junjie Tang, Huixiang Chen, Closet AI Home page and Swipe flow, 2026
Image #1: Creator Huixiang Chen, Item Scan, Item Swipe and Select, Item Editing, and Item Posting page, 2026
Image #2: Creator Ruoxuan Pan, AI listing and message assistant flow, 2026
Image #3: Creator Junjie Tang, reward and badge pages, 2026
Image #4: Creator Ruoxuan Pan, Huixiang Chen, Junjie Tang, Closet AI interface overview, 2026