Criteo is betting that ChatGPT-style agents will become a major source of product discovery. Through experiments with LLMs, it wants to use its commerce data infrastructure to power recommendations that sit behind them.
The company, historically associated with ad retargeting, is attempting to reposition itself for an AI-driven commerce era, and its latest demos suggest the company now sees large language models — not just retailers or demand-side platforms — as the next major distribution channel for advertising. Far from treating LLMs as an existential risk to its performance business, Criteo is leaning into them, through early experiments with a (publicly unnamed) LLM to position its commerce dataset as the missing ingredient that can make product discovery inside tools like ChatGPT or Claude actually work.
The company has started piping structured signals — relevance, trendiness, retailer-level performance — into LLM environments via its Model Context Protocol server, effectively allowing any agent inside those models to hit Criteo’s API when recommending a product. The bet is that generic web-crawl data is simply not good enough for high-fidelity commerce recommendations. If LLMs want to play in retail media or product suggestions, they’ll need something closer to Criteo’s longitudinal, transaction-linked dataset.
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