A shopper asks an assistant which coffee grinder fits under a low kitchen shelf. Your grinder might be a good match. But if its height is missing from the product page, the assistant has little reason to include it.
That’s a useful place to start with AI search: look at the information a recommendation would need, then check whether your store actually provides it.
Being cited and getting a visit are different outcomes. An answer can mention your brand without sending much traffic. It can also link to a guide while recommending someone else’s product. Keep that distinction in mind when you decide what to measure.
How your store can become a source
AI answers can draw on retrieved web pages, product feeds and other information available to the service. The mix varies. It is too simple to say that every assistant just reads the top few Google results.
For Google’s AI features, the usual search requirements still apply. Useful content, crawl access and clear page structure matter. Google says you don’t need special AI markup or a new machine-readable file to be eligible.
Product discovery has another route too. OpenAI’s product-feed documentation describes structured catalog information used for product discovery in ChatGPT. That makes accurate catalog fields worth maintaining alongside the visible page.
Neither route gives you control over a particular answer.
Give a recommendation something to work with
Start with the questions that decide a purchase. For a grinder, that might be its height, capacity, power requirements, grind settings and replacement parts. For clothing, it may be measurements, fabric, care and fit.
Put those facts where a customer can find them. Use descriptive headings and short explanations. A useful opening might say who the product suits, followed by the details that let someone check.
Avoid claims you can’t substantiate. A temperature rating needs evidence; “built for winter” doesn’t become a tested specification just because it sounds plausible.
If support keeps answering the same question, add the answer to the relevant product page. Our product-page guide covers how to place that information without overwhelming the purchase area.
Keep the page and catalog in agreement
Check price, availability, variant names and specifications across the visible page, feeds and structured data. Contradictions can mislead both shoppers and the services reading your catalog.
Product markup should describe the product actually shown. Reviews should be genuine and tied to the right item. An FAQ can help a reader without needing a special schema block or a promise of AI visibility.
For a more focused store review, see our ChatGPT recommendations guide.
Look beyond your own website
Independent reviews and specialist comparisons can help people assess your products. They also give search and answer systems more material to draw on.
Try a few realistic customer questions and look at the sources cited. Are they detailed reviews, forums, retailers or manufacturer pages? That tells you more about the information available in your category than a generic list of “AI ranking factors.”
Earn coverage by making the product easy to evaluate. Supply accurate specifications, answer a reviewer’s questions and publish useful original findings when you have them. Don’t invent a study or buy empty mentions just to fill a report.
Keep a small, repeatable record
Choose five buying questions that matter to your store. Save the exact wording, date, assistant, products mentioned and sources cited. Repeat periodically, knowing that answers can vary even when the question doesn’t.
Alongside that, watch identifiable AI referrals, landing pages and purchases. Some journeys won’t retain a useful referrer, so the report will be incomplete.
The practical work is familiar: make the pages accessible, state the product facts clearly and resolve conflicting information. You can improve those things this week. No agency can guarantee which brand an assistant will name next.