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Nile Blog & Research articles filed under product-context.
Four AI agents said an in-stock product was backordered. Fixing the facts stopped the wrong answers, but it did not make the product recommended. A conversation with ecommerce practitioner Leo Nguyen on contested data, hallucinated policies, and the gap between being readable and being chosen.
We launched Nile with a small cohort of merchants willing to be early. Here are our honest findings from that first cohort — some of them surprised us.
Most product catalogs are built for two audiences: the human shopper scanning for something they recognize, and the Google crawler indexing keywords. Neither is an AI agent evaluating your product.
Most merchants treat product titles as labels and descriptions as marketing copy. AI agents don't scan. They retrieve, parse, and match. The structure of your text is part of the signal.
When a shopper asks ChatGPT for a product recommendation, the agent doesn't browse your store. It retrieves structured, contextual information. Here's what's missing from most pages.
If you've asked ChatGPT for a product recommendation and wondered why it named what it named, you're asking the right question. The answer is not random.
There's a common assumption among merchants: if the catalog is complete and structured, the hard part is done. It isn't. AI agents need two distinct things from your product information.
