Coveo announced on March 23, 2026 that it is adding a new capability—Coveo Conversational Product Discovery—to its Coveo for Commerce platform. The feature lets shoppers interact with a product catalog using everyday language, while the system returns curated search results that remain anchored in the retailer’s inventory data. By embedding the dialogue directly into the search experience, Coveo aims to close the gap between free‑form conversation and the precise control merchants need over product presentation.
A hybrid approach to online shopping
Most AI‑driven chat solutions operate as separate layers that interrupt the conventional browsing flow. Coveo’s offering, by contrast, weaves conversational input into the core search engine. Shoppers can type or speak phrases like “lightweight running shoes for rainy weather” and receive a list of relevant items, complete with product images, specifications, and pricing—all without leaving the search results page. The system then allows follow‑up questions such as “show me a darker color” or “compare these two models,” keeping the interaction fluid and context‑aware.
Why the distinction matters
Retail analysts have long warned that standalone chatbots can create “dead‑end” experiences, where users are forced to jump between a conversational UI and a traditional catalog view. Coveo’s architecture sidesteps that pitfall by treating the conversation as an extension of the search query rather than a replacement. The result is a single, unified interface that preserves the speed and relevance of conventional search while adding the flexibility of natural‑language understanding.
Under the hood: agentic orchestration
Coveo describes the new module as built on its “agentic orchestration” framework. This architecture coordinates several AI models to interpret intent, retrieve items from the retailer’s catalog, and generate responses that respect pre‑defined merchandising rules. Unlike generic large‑language‑model outputs that can hallucinate products, the discovery agent grounds every suggestion in actual inventory data, ensuring that displayed items are truly available and priced correctly.
Core capabilities
- Natural‑language intent capture – Users can describe needs without adhering to keyword conventions.
- Instant curated results – The system pulls matching products from the catalog and presents them in a searchable list.
- Iterative refinement – Shoppers can ask follow‑up questions to narrow or broaden the selection.
- Side‑by‑side comparisons – The UI can juxtapose key attributes of multiple items for quick decision‑making.
- Bundle creation – Retailers can suggest complementary products, encouraging higher basket values.
Expected benefits for merchants
- Higher conversion from vague queries – By translating ambiguous shopper language into concrete product sets, retailers can capture traffic that would otherwise bounce.
- Preserved search performance – The conversational layer augments, rather than replaces, existing search algorithms, maintaining response times and relevance scores.
- Retained merchandising authority – Brands continue to dictate layout, ranking, and promotional rules through deterministic templates and policy‑driven AI execution.
- Dynamic result composition – Adaptive layouts can blend product listings, comparison tables, and instructional text into a single cohesive view.
- Reduced abandonment – Guided follow‑ups and next‑best suggestions help keep users engaged until a purchase is completed.
Executive perspective
Peter Curran, general manager of Commerce at Coveo, highlighted the shift in shopper behavior: “Shoppers don’t think in keywords,” he said. “Imagine if someone walked into a store and just said ‘shoes.’ Unfortunately, most digital stores can handle that only if they’re lucky.” Curran’s comment underscores the company’s belief that conversational inputs should be treated as a first‑class entry point rather than a niche add‑on.
Availability and pricing
Coveo Conversational Product Discovery is released as an add‑on to the existing Coveo for Commerce suite. The feature is available immediately to current customers, though pricing details were not disclosed in the announcement. Prospective buyers can request a demo through Coveo’s sales channel.
How it fits into the broader AI‑search landscape
The move reflects a growing trend among enterprise search vendors to incorporate generative AI while safeguarding data fidelity. Competitors such as Elastic and Algolia have introduced similar “search‑plus‑chat” pilots, but many still grapple with the challenge of keeping AI responses anchored to real inventory. Coveo’s emphasis on catalog grounding and merchant‑controlled layouts may set a benchmark for enterprises that cannot afford mismatched product listings or regulatory compliance issues.
Potential challenges and considerations
- Integration complexity – Retailers will need to map their product taxonomy into the conversational model to ensure accurate intent matching.
- Performance monitoring – Adding a language‑understanding layer introduces new latency variables that must be tracked to maintain a seamless shopper experience.
- Content governance – While the system respects merchandising policies, organizations must still define guardrails for AI‑generated copy to avoid brand inconsistency.
Looking ahead
Coveo’s announcement arrives at a time when AI‑driven personalization is becoming a baseline expectation for digital commerce. By blending conversational flexibility with the rigor of traditional search, the company positions itself to address both the exploratory and decisive phases of the buyer journey. If retailers can successfully integrate the feature without sacrificing speed or control, the model could become a template for future AI‑enhanced commerce platforms.
Forward‑looking statements in this article reflect Coveo’s public disclosures and are subject to the usual risks and uncertainties inherent in technology adoption.
Get in touch with our Adtech experts
