Criteo Launches Agentic Commerce Recommendation Service to Enhance AI Shopping Assistants
Commerce media company Criteo has launched Agentic Commerce Recommendation Service, a feature designed to power AI shopping assistants with accurate and relevant product suggestions.
The Agentic Commerce Recommendation Service understands broad shopper intent, Criteo says, supporting both exploratory and product-specific queries. It then can deliver relevant product recommendations and expand them with complementary items when appropriate.
How the Agentic Commerce Recommendation Service works:
- A shopping request: A consumer asks the AI shopping assistant to recommend a product that matches their needs, preferences and budget.
- AI assistant query: The AI assistant asks Criteo's Agentic Commerce Recommendation Service to track down relevant products.
- Commerce intelligence-powered filtering: Criteo takes real-world shopping and purchase signals and uses them to filter and rank products based on what matters most to that individual consumer. It also takes into account nuances such as product popularity, availability and user intent.
- Curated results: Criteo provides a curated shortlist of product recommendations, rather than raw catalog data.
- Personalized, easy consumer experience: The AI assistant reviews Criteo's recommendations, presents results and compares options. It also can support add-to-cart or checkout within the agentic experience.
Criteo tested the service using data gleaned from 720 million daily shoppers, $1 trillion in annual transactions, and 4.5 billion product SKUs. The recommendation service ended up delivering up to a 60% improvement in relevant recommendations compared to third-party approaches based only on product descriptions, according to a Criteo media release.
The Agentic Commerce Recommendation Service is available through Criteo's Model Context Protocol (MCP), linking directly to AI-powered shopping assistants with merchant inventory. AI assistants apply real-world shopping and purchase signals that cannot be accessed through traditional crawls and deliver relevant products for individual shoppers.
As large language model (LLM) platforms continue to evolve into AI shopping assistants, retailers increasingly are developing their own AI chatbots. This influences how consumers discover, compare and buy products online. As these artificial intelligence-powered shopping experiences scale, Criteo says AI assistants need a commerce-grade infrastructure that taps into real shopping behavior to give customers tailored results.
Criteo first began to test a LLM platform in 2025, and has since expanded the testing to include additional LLM platforms, retailers and brands.
"The real competitive advantage in agentic commerce will come from access to high-quality commerce data at scale," Michael Komasinski, CEO of Criteo, said in the release. "This service brings that intelligence into AI-driven shopping experiences in a way that works for the entire ecosystem, delivering relevancy for consumers while respecting retailer data, brand integrity and platform trust."
