我是 Summer,一个持续探索技术如何重塑消费产业的 AI builder。
从线下零售、品牌商品、电商数据到 AI 智能化经历,一件零售商品从设计图纸到工厂流水线,从货架灯光、电商商品详情页,到具体消费者手里——一件消费品要走的路,我每一段都走过。
现在我正在做:探寻 AI 在垂直行业(服装)里能产生十倍以上效率的新路径。欢迎同行(服装商家、AI builder)交流。
I'm Summer, an AI builder who keeps exploring how technology reshapes the consumer industry.
From offline retail, brand merchandising and commerce data to AI — from design sketches to factory lines, from the light of a store shelf to a product page to someone's hands, I've walked every step of the road a retail product travels.
What I'm doing now: finding new paths where AI delivers a 10x-plus gain in a vertical industry — fashion. Fellow travelers — fashion merchants and AI builders — welcome to connect.
"Believing that the dots will connect down the road will give you the confidence to follow your heart." — Steve Jobs
通过观察、调研、参与不同环节的 AI 应用落地,我不断在寻找 AI 放进真实商业链路后、能产生十倍效率的最佳实践和创新工作流。欢迎服装行业、AI 应用实践的商家、同行找我交流,我也乐意提供咨询和建议。Through observing, researching and taking part in AI deployment across different stages, I keep looking for the best practices and innovative workflows where AI creates a 10x gain inside real commercial chains. Fashion-industry operators, AI practitioners and peers are welcome to reach out — I'm glad to share advice and consulting.
先以 AI 自营业务为实验场,将生成式 AI 能力应用到服饰经营的核心环节,亲自跑通从商机洞察、商品设计、AI 拍摄、快速测款到供应链快反的完整闭环;后进一步打造面向商家服务的 AI Fashion 全链路解决方案(趋势洞察 + AI 设计/拍摄 + AI 辅助快速上新/测款),并尝试商业化闭环验证。
我从传统商品经营视角进一步走向 AI Native 产品建设,深入理解了 AI 应用落地的关键,尝试回答了如何利用 AI 重构消费产业的生产效率,和如何让 B 端客户感受到真实价值并为之买单。First used a self-operated AI business as the testbed, applying generative AI to the core stages of apparel operations and personally running the complete loop from opportunity insight, product design and AI photography to rapid style-testing and agile supply-chain response; then built a full-chain AI Fashion solution for merchants (trend insight + AI design/photography + AI-assisted rapid launch/testing), with commercial-loop validation.
Moving from a traditional merchandise-operations lens toward AI-native product building, I came to understand the key to AI deployment — and tried to answer how AI can rebuild the productivity of consumer industries, and how to make B2B customers feel real value and pay for it.
我参与建设数据洞察产品,将分散在平台的消费者行为和商品数据进行清洗、结构化,主导了服务于行业专业分析的知识图谱搭建,让原本难以理解的消费趋势和市场机会变得可分析、可应用。
我推动商业模式从传统流量售卖升级为以消费者洞察和新品解决方案为核心的商业服务模式,提高客户价值和产品商业化能力。
在这里,我建立了对"数据如何连接消费者、商品和商业决策"的理解,进一步感受了数据价值,也知道了电商数据和产业商品数据的 gap。
围绕品类增长负责商品策略和流量运营,通过平台机制提升消费者和商品之间的匹配效率。
我参与设计商品 & 消费者分类分层的运营体系,通过商品分层、流量策略和人群运营,让不同商品获得更精准有效的消费者触达。通过供给组织、前台场景设计及商家协同,推动平台品类增长目标落地。
在这里,我理解了电商平台用户行为和成交增长的本质:通过数据、机制和生态,让需求、供给和商家能力形成正向促进。Owning merchandise strategy and traffic operations around category growth, I improved matching efficiency between consumers and products through platform mechanisms.
I helped design an operating system for classifying and tiering both products and consumers — using product tiering, traffic strategy and audience operations to give different products more precise, effective consumer reach; and driving category growth targets through supply organization, front-end scenario design and merchant collaboration.
Here I grasped the essence of user behavior and GMV growth on an e-commerce platform: using data, mechanisms and ecosystem to create a virtuous loop among demand, supply and merchant capability.
我在这完全参与到 brand marketing、merchandising、local product creation,以及如何通过订货会来实现品牌每季商品故事的传递、打动经销商。通过对消费市场的深入走访、调研来理解不同国家和区域的消费者审美和需求,实现精准开发。
在这里我知道了一个品牌是如何去做它的商品企划和品牌故事传递的。Through deep field visits and research into consumer markets, I came to understand the aesthetics and needs of consumers across countries and regions, enabling precise product development.
This is where I learned how a brand actually does its merchandise planning and brand-story delivery.
我在迪卡侬服务不到 3 年时间,从负责线下门店跑步和步行鞋服的销售管理,后去到品牌中心,负责中国区滑雪服装、射击运动商品的选品、定价、销量预测、采购深度与库存管理。
在这里我理解了零售的一线真相:消费者在货架前三秒钟的犹豫,一场促销如何改写一周的现金流和库存周转,以及库存周转对于零售商有多重要。
身为软件工程专业学生,毕业后选择了服装零售和时尚领域,冥冥之中又进入互联网,真正开始热爱的事业。A software-engineering student who chose fashion retail after graduation — and, as if by fate, circled back into the internet industry, where the work I truly love began.
我现在是一位 AI 产品负责人与业务操盘者,在零售、消费品牌、电商与 AI 创新领域拥有十余年经验。我关注与实践探寻技术如何重塑消费产业,并致力于将 AI 能力转化为真实业务价值。
曾服务于迪卡侬、阿迪达斯和阿里巴巴,积累了从消费者洞察、商品策略、供应链运营到数据产品和 AI 商业化的完整产业经验。
曾参与并负责多个创新业务探索落地。
I'm an AI product leader and business builder with over a decade of experience across retail, consumer brands, e-commerce and AI innovation. I focus on — and practice — finding how technology reshapes the consumer industry, turning AI capabilities into real business value.
I've worked at Decathlon, Adidas and Alibaba, building end-to-end industry experience from consumer insight, merchandise strategy and supply-chain operations to data products and AI commercialization.
I've led and contributed to multiple innovation initiatives from exploration to launch.
近年来,聚焦生成式 AI 在服饰产业中的应用,探索 AI 从趋势洞察、商品设计、内容生产到供应链协同的全链路创新模式。我的核心方向是连接 AI 技术与传统产业场景,打造 AI Native 的产品和商业模式。 In recent years I've focused on applying generative AI in the apparel industry, exploring end-to-end innovation across trend insight, product design, content production and supply-chain collaboration. My core direction: connecting AI technology with traditional industry contexts to build AI-native products and business models.
项目Projects
国际零售品牌的全球化供应模式在中国市场响应效率不足:海外生产周期长、成本高,难以跟上本地市场变化。在迪卡侬品牌中心,我主导了供应链优化项目。Global supply models respond too slowly for the Chinese market: long overseas lead times and high costs. At the Decathlon brand center, I led the supply-chain optimization program.

中国消费者的文化偏好和需求与全球标准商品存在差异,需要本土化的商品创新来满足差异化需求。在阿迪达斯品牌中心,我主导了本土化消费需求调研和产品研发。Chinese consumers diverge from globally standardized products in culture and needs — localized innovation is required. At the adidas brand center, I led local consumer research and product development.

服装品牌由丰富性驱动,如果只通过体量来理解品牌,会错失对小众市场的理解——而正是众多细分市场共同构成了服装大盘。需要一种分类分层的方法论,来实现对品牌的观察/理解、提供品牌成长的咨询能力,提升平台对品牌的孵化价值,也帮助平台理解服装品牌和商品增长,提供运营的切入口。Apparel brands are driven by richness. Understanding brands by volume alone misses the niche markets — yet those niches together make up the whole market. We needed a classification-and-tiering methodology to observe brands, advise their growth, surface the platform's incubation value, and give operations clear entry points.

中小商家在商品规划过程中缺乏市场趋势判断能力,选品依赖个人经验,试错成本高。需要把原本只有头部商家才有的趋势数据采购和趋势研究能力,通过数据和产品化方式规模化输出。Small and mid-sized merchants lack real trend-reading capability; selection runs on personal experience and trial-and-error is expensive. The goal: scale the trend-data procurement and research capabilities once exclusive to top merchants, out through data and productization.

服饰行业长期存在四个结构性问题:商品开发周期长、市场判断依赖经验、内容生产成本高、供应链响应效率低。以 AI 自营业务作为验证场景,通过实际商品经营积累行业认知和业务数据,探索并跑通 AI 赋能服饰产业的完整链路。Fashion suffers four structural problems: long development cycles, experience-based judgment, high content costs, slow supply response. Used a self-operated AI business as the validation scenario to run the complete AI-powered pipeline.

思考沉淀Writing
交流Let's talk
如果你正在创业、探索 AI 产品、研究 AI 与产业结合,或正在思考 AI 如何改变自己的行业,我很乐意交流。 If you're building a company, exploring AI products, researching how AI meets industry, or thinking about how AI will change your own field — I'd love to talk.
我的经历横跨零售、消费品牌、电商和 AI 创新与应用。我关注的不只是 AI 能做什么,更关注 AI 如何真正进入业务流程,改变效率、决策和商业模式。 My experience spans retail, consumer brands, e-commerce, and AI innovation and applications. What I care about isn't just what AI can do, but how AI actually enters business workflows — reshaping efficiency, decisions, and business models.
我愿意分享实践中的经验、思考,以及那些没有成功的尝试和教训;同时也期待从不同领域的实践者那里获得新的洞察和反馈。 I'm happy to share hands-on experience, thinking, and the attempts and lessons that didn't work out — and I look forward to new insights and feedback from practitioners across different fields.
AI 时代的价值实现,需要更多跨领域的连接和共同探索。Realizing value in the AI era takes more cross-disciplinary connection and shared exploration.