What Must AI Know to Recommend Your Products?

A catalog is excellent at telling a storefront what a product is called, what it costs and which images to show. A shopper often needs a different answer: will this product work for my use, fit what I already own or solve the problem I described? AI can help with that decision only when the underlying product knowledge is specific, current and controlled by the merchant.

The answer is not to replace your PIM. Your existing catalog remains the source of record. The task is to find the context that is missing, verify it and make the approved answer reusable where shoppers or merchant teams need it.

Start with decisions, not fields

Choose a product family where customers regularly ask for advice. Gather ten real questions from sales, support, search or an on-site advisor. Group them by the decision they express:

• Fit: size, compatibility, ingredients, materials or technical requirements.
• Use: situations in which the product helps, and situations in which it does not.
• Comparison: differences between adjacent models or variants.
• Constraints: availability, geography, care, safety or warranty conditions that affect the choice.

This exercise tells you which product facts need more context. It is more useful than adding another generic paragraph to every SKU.

Check the source behind each answer

For every proposed answer, record the exact product or variant, the supporting source, its date and the person authorized to approve it. A manufacturer's specification may support a measurement. A review may reveal a question customers ask, but it does not establish a technical fact. If two sources conflict, the answer needs review rather than a confident guess.

Consider a compatibility question. A broad statement such as “works with most models” is not enough. A useful answer identifies supported models, exclusions and the source of that compatibility information. If the evidence does not exist, saying so is better than manufacturing certainty.

Keep enrichment separate from publication

AI Product Data Platform works alongside the merchant's existing catalog. Sourcing Agent collects candidate information from permitted sources, and Product Engine structures it into enrichment proposals. Merchant Console is the place to review, edit or reject proposals, while the merchant-specific Mirror Catalog holds knowledge approved for the relevant use.

That separation matters. Finding a possible answer is not the same as authorizing a claim. Nor does an approved answer automatically belong in every channel. A product specialist's internal note, an on-site buying answer and public information for AI discovery may each have different permissions.

Prepare for distribution without assuming placement

Clean URLs, accessible product pages, consistent identifiers and appropriate structured data help other systems interpret a catalog. Brand Discovery builds on that foundation. It is still important to check what each destination can actually receive or retrieve. No feed or markup can guarantee that ChatGPT, Gemini, Perplexity or Claude will cite or recommend a particular product.

Start with a small set of products, test the questions that matter and inspect the answers across the channels you intend to use. Then expand the approved knowledge, not just the volume of text.

For a working example based on your store, request a demo or explore AI Product Data Platform.

Related reading

• Merchant Console: from product questions to better knowledge
• GEO for ecommerce and AI search