{"slug":"tapestry-collects-feedback-from-thousands-of-store-associates-using-aws","url":"https://findausecase.com/use-cases/tapestry-collects-feedback-from-thousands-of-store-associates-using-aws","title":"Tapestry Collects Feedback from Thousands of Store Associates Using AWS","description":"Luxury retailer Tapestry built a generative AI engine on Amazon Bedrock, Amazon Transcribe and Amazon Translate to power Tell Rexy, a feedback app for store associates, and Ask Rexy, a chatbot for corporate analysts, collecting close to 30,000 feedback pieces in a year and speeding generative AI application development by 10 times.","company":"Tapestry","industry":"Retail","country":"United States","aiCapabilities":["Conversational AI","Generative AI"],"technology":["Amazon Bedrock","Amazon Transcribe","Amazon Translate"],"deployment":"Unknown","problemStatement":"Tapestry lacked an efficient way to capture and harness insights from thousands of frontline store associates who possess invaluable knowledge of customer preferences, product performance, and store operations; corporate store visits only yielded anecdotal information that could not be scaled or analyzed across the entire retail network.","solutionApproach":"Tapestry's engineering team used close to 20 AWS services, with Amazon Bedrock hosting the large language model, to build a generative AI engine powering two applications: Tell Rexy, a feedback collection app deployed on store devices that uses Amazon Transcribe to turn associates' spoken observations into text and Amazon Translate to translate feedback into English; and Ask Rexy, a chatbot that lets corporate analysts query and gain insights from the collected feedback data.","businessValue":"Tell Rexy is live across most North American Coach stores; it has been used by several thousand associates, gathering close to 30,000 feedback pieces in one year, helping Tapestry make systematic changes to its merchandising processes; the reusable generative AI engine has accelerated development of new AI-powered applications by 10 times.","evidence":{"band":"high"},"sourceUrl":"https://aws.amazon.com/solutions/case-studies/tapestry-generative-ai-case-study/","dates":{"publishedAt":"2026-10-01T05:49:36.359Z","publishedAtSource":"pipeline","updatedAt":"2026-10-01T05:49:36.359Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/tapestry-collects-feedback-from-thousands-of-store-associates-using-aws. Bulk republication requires permission."}