Title
Tapestry Collects Feedback from Thousands of Store Associates Using AWS
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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.
we've gathered close to 30,000 feedback pieces in 1 year.
This extensible engine has accelerated Tapestry's ability to develop new AI-powered applications by 10 times
Company
Tapestry
…d the way for more informed decision-making across its business. About Tapestry Tapestry is a luxury fashion retailer based in New York that operates on a global scale. It is the parent company for the iconic brands Coach and Kate Spade New York. O…
Country
United States
…d the way for more informed decision-making across its business. About Tapestry Tapestry is a luxury fashion retailer based in New York that operates on a global scale. It is the parent company for the iconic brands Coach and Kate Spade New York. O…
Industry
Retail
…d the way for more informed decision-making across its business. About Tapestry Tapestry is a luxury fashion retailer based in New York that operates on a global scale. It is the parent company for the iconic brands Coach and Kate Spade New York. O…
Problem
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.
…into customer preferences, product performance, and store operations. However, Tapestry lacked an efficient way to capture and harness this wealth of information. Corporate teams regularly visited stores, but these visits only yielded anecdot…
Solution
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.
Tapestry's engineering team used close to 20 AWS services to develop a generative AI engine that can be used to build various applications
Business value
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.
It has been used by several thousand associates, and we've gathered close to 30,000 feedback pieces in 1 year.
AI capabilities
Conversational AI, Generative AI
…associates to share their observations and experiences throughout the workday. Ask Rexy is a chatbot that lets corporate analysts at Tapestry query and gain insights from the collected feedback data. The Tell Rexy app is deployed on store devices like tablets and point-of-sale s…
Technology
Amazon Bedrock, Amazon Transcribe, Amazon Translate
…epak Chandak Senior Director, Omni-Innovations and Product Management, Tapestry AWS Services Used Amazon Bedrock Amazon Bedrock is a fully managed service that offers a choice of high-performi…
Headline outcome
This extensible engine has accelerated Tapestry's ability to develop new AI-powered applications by 10 times
Use case type
Analytics augmentation
…associates to share their observations and experiences throughout the workday. Ask Rexy is a chatbot that lets corporate analysts at Tapestry query and gain insights from the collected feedback data. The Tell Rexy app is deployed on store devices like tablets and point-of-sale s…
Capture details
- Captured
- 28 Sept 2026, 07:02 UTC
- Extractor
- fetch-strip@1
- Snapshot hash
- 1654134a8408866590278a32c022f1023b684b853541caa83ab6075afd4710b1