What makes AI engines recommend a brand?

Google's guide to generative AI search covers which pages are eligible to appear and what kind of content tends to do well. It also says outside tools can't see inside Google's ranking or AI systems. We'd treat any outside list of weighted "recommendation factors" for Google as an estimate for that reason.

The guide does describe how an answer gets assembled, and that's enough to reason from.

Suppose two buyers ask an assistant for a CRM. The first runs a six-person accounting firm and wants something the team can set up in a week without a consultant. The second runs a 200-person sales team and needs territory rules and forecasting. A good answer weighs products against each request. The two answers can overlap, and they can also name different products, because "the best CRM" isn't what either buyer asked for.

Google's guide says its systems generate related queries around the original question and fetch results for each. For the first buyer, those narrower searches would plausibly cover setup time and pricing for small teams. The answer is then written from the pages those searches bring back.

That has a practical consequence for a brand. A page that says how long setup takes and what six users pay gives the engine a fact to weigh against the first buyer's request. A page that says "the CRM for teams of every size" gives it less useful detail for either one. This is our reading of the mechanism Google describes.

Access comes first. For Google's AI features, a page has to be indexed and able to appear with a snippet, and the site has to be included in Search generative AI features in Search Console. For ChatGPT, OpenAI's crawler documentation says a site that opts out of OAI-SearchBot won't be shown in search answers, though it can still appear as a navigational link.

Blocking a crawler doesn't keep a fact about you out of answers altogether, since the fact can come from another source. The block stops that crawler from fetching your own page.

Then comes how specific the page is. Lean Labs' AEO service page is a useful example of stating facts plainly. It names the buyers it serves, which are B2B startups and scaleups on HubSpot. It lists prices on the page: $9,000 as a one-time setup for the on-site package, and $5,000 a month for ten months for the off-site program.

Those are checkable facts. A reader of that page, human or machine, can tell which platform the service is built for and what it costs without inferring either. That's the kind of detail that lets a page be weighed against a specific request. It isn't a prediction about which agency an engine will name.

First-hand material can help too. Google's guide says a view drawn from your own experience can stand out among the sources its systems read.

A recommendation question invites outside opinion. Google's guide says its AI features can reflect what people elsewhere on the web say about a product, on other sites and in forums. So reviews and independent coverage are part of what an engine can draw on when it describes you.

The guide also discourages manufacturing that coverage, and notes that its AI features rely on Google's spam systems. We'd keep off-site work authentic for that reason, though authentic coverage isn't a guarantee either.

Lean Labs describes its off-site work as placing expert articles on other publications. Its on-site package includes a pipeline that collects customer reviews and sends them to G2, Clutch and Google. We wouldn't set a target count for either. Independent sources can fairly disagree with a vendor, so we'd test outside coverage for accuracy and leave opinions alone. A wrong price or an old product name on someone else's site is worth asking them to correct.

An answer that compares options names several brands, and they don't all get the same treatment. One is suggested for the buyer's case. Another is listed with a caveat, or as the choice for a different kind of buyer. Your page can also be cited as a source in an answer that recommends someone else.

So when you check how you appear, read the sentence your name sits in. A count of mentions hides the difference between those cases.

Check each tool separately as well. Google says AI Overviews and AI Mode can use different models and techniques, so the two can show different responses and links. Those are two features inside one search engine, and the other AI platforms worth targeting are separate products again. We'd also ask the question several ways, each describing a different buyer need, and repeat the checks over time. One result tells you little, which is why tracking AI answer engines works from a fixed list of prompts.

Since the request decides the fit, start with the requests you most want to win. Write down the buyer situations you serve best. For each one, make sure a page you control states the facts that buyer would ask about, such as price, setup time, the platform you work on and who you've done it for.

Then compare what outside sources say with those pages, and ask for corrections where a fact is wrong. If your buyers use Google's assistant, the same preparation applies to getting recommended in Google Gemini.

Finish by asking an assistant the question the way each of those buyers would. Read what it says about you, and check whether your own site would let a stranger confirm it.