How AWS is helping retailers build their own AI-powered shopping experiences

Key takeaways Agentic Shopping Assistant (ASA) on AWS was created together with the AWS Generative AI Innovation Center—a solution that packages the architecture, starter code, and expert guidance inspired by learnings from Amazon's Alexa for Shopping.

Retail customers can combine this foundation with their own data, business rules, and brand voice to create conversational shopping assistants tailored to their needs. Kate Spade is already using ASA on AWS to build its own AI shopping experience, with additional retailers currently in testing.

As Amazon has continually iterated to create a leading AI shopping assistant, it gave us valuable insights about what capabilities, tools, and features matter most to the over 300 million customers who used it last year. Amazon’s AI shopping assistant drove nearly $12 billion in incremental sales for Amazon last year alone.

Last week, Amazon announced Alexa for Shopping, an even more capable next iteration bringing together Rufus and Alexa+. Today, AWS announces a new AI retail solution based on Alexa for Shopping that for the first time enables retailers outside Amazon to tap into the technology and learnings directly.

To help other retailers better engage with their customers, the AWS Generative AI Innovation Center—a global team of experts that helps customers design, build, and launch AI solutions—is packaging those learnings into the Agentic Shopping Assistant on AWS.

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From Amazon's success to retail industry solution ASA on AWS uses the same foundation as Alexa for Shopping but with different ingredients that are tailored to each retailer's specific catalog, customer base, and shopping environment. Each deployment is customized to match the retailer's brand voice and domain expertise.

Retailers get the technical foundation from architecture guidance, starter code, and support from AWS experts and system integrator partners, allowing them to launch their own conversational shopping experiences in weeks—rather than the years it would take starting from scratch.

Why retailers need their own AI shopping presence Chat agents can surface recommendations based on shoppers' needs, making it easier for customers to navigate retailers' catalogs.

As AI agents become the primary interface for shopping decisions, retailers face a critical choice: build their own AI presence or risk becoming dependent on general-purpose answer engines that don't serve their brand or customers. The business case is compelling: conversational shopping sessions convert at 3. 5 times the rate of traditional keyword search.

Retailers already possess deep vertical knowledge about their products, customers, and categories that no general-purpose AI can match. A specialty retailer knows its offerings better than any intermediary.

Restaurant chains understand their menus and customer preferences in ways no platform can replicate, and no one knows their products better than consumer packaged goods (CPG) brands. ASA on AWS gives retailers the proven technology foundation to act on that knowledge while maintaining direct customer relationships.

Amazon Connect expands into a set of agentic AI solutions New AI solutions for business decisions, hiring, customer experience, and health care draw on decades of Amazon's operational expertise. How Kate Spade built an AI shopping assistant with AWS Kate Spade used ASA on AWS to create its own conversational shopping experience.

On April 13, Tapestry launched the Kate Spade AI Gift Concierge, the first production-ready retail AI assistant built with Amazon Bedrock AgentCore, purpose-built for the moment in shopping when emotions run high but confidence runs low: gift buying.

Recognizing that 53% of shoppers report stress during gift purchases, the agent engages shoppers in natural dialogue about occasion, recipient, and style to translate uncertain intent into curated, confident product recommendations.

The experience was grounded in how people shop for gifts—informed by real shopper behavior and insights drawn from the questions customers asked Amazon's Alexa for Shopping and the answers that drove successful outcomes.

The result is an interaction model that feels less like search and more like talking to someone who knows the brand and knows how to give a great gift. Built on Anthropic's Haiku 4. 5 model with Amazon Bedrock providing observability, authentication, and evaluations, the team moved directly into production and completed roughly 2.

5 months of rigorous testing before becoming customer-facing. As Yang Lu, chief information and digital officer at Tapestry, shared at launch: "We are excited about the possibilities agentic comm