AI search optimization for ecommerce

The questions are already in your business. Support tickets, pre-sale chats, on-site search, the queries in your search console, the reasons attached to returns. We pull them together, group them, and rank them by how close each one sits to a purchase. A question that decides an order is worth answering properly. A question that is idle curiosity is not. This step also tells us which answers belong on a product page, which belong on a category page, and which deserve a page of their own.

Assistants quote passages, not pages, so each answer needs to stand on its own. We write a direct response in the first sentence, then the detail underneath, using the words a shopper would use rather than the internal name for the thing. No preamble, no hedging, no paragraph of positioning before the fact arrives. Where the honest answer is that it depends, we say what it depends on and give the cases. A page built this way reads better for people too, which is the point.

Numbers hidden in a product image, a spec sheet PDF or a video cannot be quoted. We move the details into text on the page: dimensions with units, weight, materials, capacity, compatibility, power, care instructions, what is in the box. Then the same values go into structured data so they can be parsed without guessing, and into your product feed so the shopping surfaces carry them too. Where a value genuinely varies by variant, the page has to show which variant it belongs to.

Assistants draw on more than your site. If your delivery time says three days on the product page, five days in the footer, and something else again on a marketplace listing, the model has no reason to trust any of them. We go through the places your details are published, your own pages, your listings, your profiles, your feed, and reconcile the ones that contradict each other. Returns windows, shipping costs, contact routes and product names are the ones that most often disagree.

Some stores block assistant crawlers by accident, through a broad rule in robots.txt, a firewall setting or a bot filter added during an attack. Others block them on purpose, which is a legitimate choice with a cost attached. We check what is currently allowed, explain the trade in plain terms, and make the decision yours. Content behind a login, inside a script that only runs in a browser, or rendered after a delay is effectively invisible either way, so we look for that too.

There is no ranking report for this. What you can do is run your own list of questions across the assistants your customers use, record what comes back, and note whether you are mentioned, whether the details are right, and who else appears. Doing that monthly shows drift, catches wrong information about your products, and tells you which answers still have no good source. We keep the log and share it with the reasoning, rather than presenting a score.