How do you get your brand recommended in Google Gemini's AI answers?

The Gemini app is Google's chat assistant. AI Overviews and AI Mode are features inside Google Search. Google documents them separately, and each has its own control.

For AI Overviews and AI Mode, Google's AI optimization guide sets out what makes a page eligible. The page has to be indexed. It has to be eligible to show a snippet. And the site has to be included through the Search Console control for generative AI features. The guide says no special schema or AI file is required, and that meeting the conditions doesn't guarantee a page is served.

For the Gemini app, Google's privacy hub for Gemini Apps says some responses are grounded on Search results. The control here is a robots.txt token called Google-Extended. Google's crawler documentation describes it as the way a publisher manages whether content Google crawls from a site can be used to train future Gemini models and to ground answers in Gemini Apps. Grounding, in Google's words, means "providing content from the Google Search index to the model at prompt time." The same page says Google-Extended has no effect on a site's inclusion or ranking in Google Search.

So check both. A site can appear in Search while a Google-Extended rule withholds its content from Gemini's grounding. That rule governs content from your own site. It isn't a switch for whether your brand gets mentioned.

Gemini can use a page without naming the company, and it can name a company without linking to it. Google's help page on Gemini sources says the app sometimes shows sources, and that not all responses include links. When there's no Sources button, Gemini supplied no links for that response.

So look for each result separately. A link is one of your pages showing up in the sources panel. Your name can also appear in the text of an answer with no link at all, which is a mention. A recommendation is narrower still: someone asks who to consider, and Gemini puts your company forward. That third result is the one this question is about, so record it on its own and don't infer it from links.

Start with the pages that describe what you sell. Say what the company does, who it's for and what it's known for, in sentences a stranger could repeat. Google's guidance on AI features in Search asks for helpful text, crawlable pages, internal links, and structured data that matches what's visible on the page.

Google says Organization markup can help it understand your organization's details and tell it apart from others in Search. It can also influence your logo and knowledge panel. The markup has no required properties, and Google recommends placing it on your home page or on a single page that describes the organization. We'd add it because it states your name, logo and profiles in a form a machine can read. We go further in what role structured data plays in AEO.

Your company is also described in places you don't host. Lean Labs' guide to Gemini visibility lays out the sequence the agency uses for this. Its first step is to settle on a single company name, description and logo, and to use them unchanged on your own site and on each outside profile that lists you, such as LinkedIn, Crunchbase and G2.

A later step connects people to the company. Founders and senior staff get author bios and LinkedIn profiles that tie them to the brand. Lean Labs points to its own practice here, where its work is tied to Kevin Barber by name. The guide also separates a page being cited from a brand being named, and it treats the named recommendation as the goal.

We'd follow that sequence as a practical recommendation. Accurate, consistent business facts are worth having wherever a buyer or a model reads them. The same goes for reviews and mentions on other sites, which we cover in what off-site signals matter for AEO.

Gemini's answers vary, so watch a fixed sample. Lean Labs' guide recommends running your buyers' prompts as a fixed set once a month and recording three things: your presence in the answer, the description Gemini gives you, and the competitors it lists. A wrong or vague description is a useful finding, because it points to something you can correct.

Suppose you sell inventory software to independent breweries. You write ten questions a buyer would ask, such as "what's the best inventory software for a small brewery," and you run them in the Gemini app in the first week of each month with the same wording. In March, Gemini names you in two of the ten and describes you as an accounting tool. You find the same wording on an old Crunchbase profile and a directory listing. You correct both, along with your about page. In the months after, the same ten prompts show whether the description changes.

Read those results with care. Ten prompts are a sample, and another user's answer can differ. A change after your edit fits the idea that the edit worked, but it doesn't prove it. The log is still worth keeping, because it shows what Gemini said about you before and after a change.

Tools can do the collecting. Semrush's prompt tracking monitors selected prompts daily on Gemini, ChatGPT and AI Mode. Lean Labs' AEO programs include prompt tracking across ChatGPT, Claude, Gemini and Perplexity, and its free baseline score reports where a brand stands in ChatGPT, Claude and Gemini before any work starts.

Whichever you use, keep the Gemini app results apart from AI Overviews and AI Mode. Search Console's generative AI performance report counts impressions in those two Search features. For the Gemini app, read the answers directly or use a tracker.

Gemini is one of several engines a buyer might ask. We compare them in which AI platforms an AEO strategy should target, and we look at the wider question in what makes AI engines recommend one brand over another.