Tapestry Collects Feedback from Thousands of Store Associates Using AWS
Tapestry· United StatesAmazon Bedrock · Amazon Transcribe · Amazon TranslateLuxury 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.
Overview
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.
This entry has 12 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
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.
The 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.
Reported 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.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other retail entries in the register.
John Keells Holdings scales retail advanced analytics with Azure Databricks
John Keells Holdings' OCTAVE Center of Excellence used Azure Databricks, Delta Lake and MLflow to unify siloed data across 70 companies and seven industry verticals, running over 40 advanced analytics use cases including a store-clustering supply chain optimization pilot for its Keells supermarket chain that improved dry-category revenue and cut the time for annual category review.
Omoda's generative AI stylist lifts conversion rate 2.5x for online fashion shoppers
Dutch fashion retailer Omoda built AI stylist, a virtual shopping assistant powered by Google Cloud's Gemini Pro 1.5 model with Vertex AI Multimodal Embeddings and Vector Search. AI stylist holds natural-language conversations with customers, understands their needs, and builds complete outfits combining clothing, shoes, and accessories rather than just matching similar products, with an Intelligent Brand Matching feature that factors in brand affinities. Customers who use AI stylist convert at a rate 2.5 times higher than average, and the tool also improves inventory management through better trend anticipation.
Furniture.com Transforms Online Search with Databricks
Furniture.com unifies over 60 retail partners and 1.5 million SKUs on the Databricks Data + AI Platform, using Delta Lake, MLflow and Unity Catalog to run its ML lifecycle. Its Find It AI product-discovery tool uses generative AI to create a synthetic image representing shopper intent, then matches it against the product catalog for image-based search. A Collections model uses LLMs to automatically group related products, finding more than 16,000 collections across 50+ partners with no human intervention. Users who interact with Find It AI show a click-through rate 8x higher than baseline and a return rate 3.2x higher than baseline.
Furniture.comDatabricks Data + AI Platform · Delta Lake · MLflow +3
How Walmart Achieved Enterprise Transformation Through Digital Experience Innovation
Working with implementation partner Nativa on LivePerson's technology, Walmart customized and integrated conversational technology into its WhatsApp channel, including a FAQ-Transactional Bot to streamline searches, inquiries and purchases. The solution delivered a 60% increase in productivity and a 10 percentage point improvement in CSAT/NPS, along with operational savings through automated customer service processes.
WalmartLivePerson · WhatsApp
Was this helpful?
Your feedback helps us improve our use case database