See how a leading omnichannel sleep retailer used an AI retail search assistant to make online discovery feel as guided as an in-store consultation. In this case study, you’ll learn how Grid Dynamics designed a triage-first discovery platform that blends fast facet search with a conversational shopping assistant running on Google Cloud’s Vertex AI Search for Commerce, Gemini models, Redis, and Elasticsearch.
The download walks through the full architecture, including the Discovery Service, Natural Language Service, caching, and data pipelines that keep product and content metadata in sync.
You’ll see how the AI retail search assistant interprets complex, conversational queries, pairs products with educational content, and personalizes results across search, browse, and recommendations.
You’ll also get concrete performance outcomes: a 17.5% conversion lift, 2% increase in average order value, and 18% growth in sales share for a strategic brand driven by better intent understanding and guided recommendations. For technology leaders, the case study details how to control latency, manage AI inference costs, and scale on GKE with enterprise-grade security.
Download the case study to explore a real-world blueprint for deploying an AI-powered retail search and shopping assistant that drives measurable revenue impact.
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