How Purina got its catalog ready to be recommended by AI agents

Overview

Purina is part of the Nestlé group and one of the most established names in pet nutrition, selling in more than ninety countries. Its product pages were already optimized for search. The question it came to us with was about the next intermediary.

People are starting to discover, compare and buy through AI assistants. That multiplies the number of places a product has to be legible, and an assistant does not read a page the way a search engine does.

The challenge

A page can rank and still be invisible to an agent. What an assistant needs is semantic structure, attributes that are actually there, and context it can reason over. Traditional product content, however well optimized for keywords, often carries none of that.

The second constraint was practical: close that gap without disturbing what already works for search, and without a replatform.

How we solved it

1. Structured data enrichment. Product pages carry semantic attributes, so a machine can tell what the product is, what it is for, what is in it and who it suits.
2. Metadata written for comprehension. Titles, descriptions and tags rewritten so a conversational platform can place the product in context rather than match a string.
3. One category at a time. Cat food and dog nutrition first, so discoverability inside AI environments could be measured before the pattern was rolled wider.

Three layers, not a replacement

Search is not going away, it is getting another two layers on top. Classic visibility is keywords, links and authority. Generative visibility is schema, structure and semantic comprehension. Answer visibility is whether an assistant trusts the product enough to include it in what it tells someone. Each layer stands on the one under it.

What changed

Purina’s products, cat food first, are now discoverable and recommended inside LLM-based platforms, which makes it one of the first brands of its size to have done the work before the channel matters.