Coveo Launches Conversational Product Discovery, Unifying Conversations and Commerce Search
Coveo, the leader in AI-powered relevance for digital experiences, announced the launch of Coveo Conversational Product Discovery, a new capability within Coveo for Commerce that allows shoppers to discover products through natural language conversations embedded directly within the search experience.
Unlike standalone AI chatbots that fragment shopping journeys, Coveo Conversational Product Discovery integrates conversation into the core of commerce search. Shoppers can describe what they need in their own words and receive curated product results grounded in catalog data all while retaining the speed, relevance, and merchandising control that is part of a traditional search experience.
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With Coveo Conversational Product Discovery, shoppers can:
- Describe shopping needs using natural language
- Receive curated product results quickly
- Refine results through follow-up questions
- Compare products and key attributes
- Build bundles of complementary products
By supporting context-aware conversational refinement and product education within search, the experience helps shoppers move from exploratory intent to confident purchase decisions faster.
“Shoppers don’t think in keywords,” said Peter Curran, general manager of Commerce at Coveo. “Imagine if someone walked into a store and just said ‘shoes’. Unfortunately, most digital stores can handle that only if they’re lucky.”
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Built on Coveo’s agentic orchestration architecture, the discovery agent coordinates multiple AI capabilities to interpret shopper intent, retrieve relevant products from a retailer’s catalog, and assemble responses that remain grounded in catalog data and merchandising rules. Retail teams maintain control through defined layouts, content guardrails, and merchandising directives that shape how results appear.
Key benefits include:
- Turning exploratory traffic into revenue by guiding shoppers from vague needs to curated product selections
- Preserving high-performing search experiences by augmenting search rather than replacing it with chatbots
- Maintaining merchandising control through deterministic layouts and policy-driven AI execution
- Dynamically structuring conversational responses through adaptive layouts that combine products, comparisons, and guidance in a cohesive results view
- Reducing dead-end experiences with guided refinements and next-best suggestions
