How Criteo is turning LLMs into its next big advertising channel

"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."

"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."

Criteo is repositioning itself from ad retargeting toward powering AI-driven commerce by supplying its longitudinal, transaction-linked dataset to LLM agents. The company runs experiments with large language models and exposes structured relevance signals, trendiness metrics, and retailer-level performance through a Model Context Protocol server so agents can call Criteo's API during product recommendations. Criteo contends that generic web-crawl data lacks the fidelity required for high-quality commerce recommendations and that LLM-driven retail media will need richer, retailer-linked datasets. The company emphasizes deep learning that interprets scattered interactions to reveal recommendation opportunities and positions its nearly two-decade dataset as a competitive differentiator.

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