We audited 12 top DTC stores: here's who the agents can read.
One product page per store, seventeen checks. The scores, the five most common gaps, and two cases every store can learn from.
Notes from the candidate set.
How AI shopping agents find, check and recommend products, and what to do about it. Written for e-commerce teams, with nothing you have to take on faith.
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How to get your products recommended by ChatGPT: a practical checklist.
Twelve steps in four stages: baseline, machine-readable product data, independent citations and measurement.
I asked ChatGPT for the best running shoes under $150. Here's who showed up.
Five brands, one clear winner, and the reasons the rest were left out. An illustrative walkthrough of a single shopping question.
llms.txt, schema and feeds: what AI shopping agents actually read.
Feeds, schema, robots.txt and llms.txt are not equally important. A plain guide to which agents read what, and the order to fix them in.
Why Muse reads your site like a customer, not a crawler.
What a browser agent sees when it opens your product page, where it gives up, and a ten-minute test for your own store.
The candidate set: the only metric that matters in agentic commerce.
Rankings are gone. Being considered at all is the new position one. What the candidate set is and how to measure it.
Looking for the numbers? The quarterly Agent Index ranks the brands AI agents recommend, by category, and the methodology explains how it is made.
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