Be the brand
Gemini
recommends.

What is Gemini optimization?

Gemini optimization is the work of getting Google's Gemini to cite your pages and recommend your brand in its answers. Gemini grounds answers in Google Search, so the work starts with pages Google can crawl, index and show as a snippet. Then it moves to what Gemini weighs: clear answers, trusted third-party sources and one consistent description of your brand.

SEO earns a position in a list of links. Gemini optimization earns a place in the answer itself. Google says its AI features need no extra technical requirements [1], so the difference is in what you publish and where you are mentioned, not in special markup.

Where Gemini shows up

All three draw on the same index, but they do not always name the same brands. A buyer can see you in AI Overviews and miss you in the Gemini app for the same question. That is why we treat them as three places to measure, not one.

Why it matters for B2B buyers

B2B buyers now ask AI to build their shortlist before they visit a single vendor site. They ask which tool fits a team of their size, how two products compare, whether a product works with their stack and what it costs to switch. Gemini answers with a handful of names and a reason for each. If you are not one of them, you are not on the list the buyer brings to the next meeting.

How does Gemini pick its sources?

Gemini answers by searching Google, reading the pages it retrieves and writing a reply that cites some of them. Your brand gets named when the pages Gemini reads describe you clearly and credibly for that question. You influence what it retrieves and how much it trusts what it finds, not the wording of the final answer.

Think of it as a researcher with a deadline. Gemini takes the buyer's question, runs searches for it and for related sub-questions, opens the most useful results and writes a short answer from what they agree on. Google describes this fan-out for AI Overviews and AI Mode: one question becomes several searches across subtopics, and the answer can cite a wider set of pages than a classic results page shows [1].

It starts in Google's index

A page that is not indexed, or not allowed to show a snippet, cannot be used as a source in Google's AI features [1]. So the first checks are plain ones. Can Google crawl the page? Is it indexed? Does it carry a nosnippet or a very short max-snippet rule that hides the text Gemini would quote [3]? Is the main content in the HTML, or only drawn in by JavaScript?

Then it weighs the whole web, not just your site

Ranking a page is necessary, but it is not enough. For a buying question, Gemini reads review sites, comparison articles, community threads, videos and directories alongside your own pages. If those sources describe you as a small-team add-on while your site says enterprise platform, Gemini tends to repeat the version the web agrees on.

What about Google-Extended?

Google-Extended is a robots.txt token. It lets you choose whether content Google crawls from your site may be used to train Gemini models and to ground answers in the Gemini app [2]. It does not change how your pages appear or rank in Google Search, so AI Overviews and AI Mode still follow Googlebot and your snippet rules [1][2]. Blocking it is a valid choice. It should be a deliberate one, made with the Gemini app in mind, not a line copied from a template.

How is it different from SEO?

SEO and Gemini optimization share a foundation, but they aim at different results. SEO aims for a high position in a ranked list of links. Gemini optimization aims for a mention and a recommendation inside a written answer, which depends on what the whole web says about you as much as on your own pages.

We see strong SEO as the floor. Without it, Gemini cannot find you. With only SEO, you are findable but not necessarily named.

The measurement shift

A keyword has one rank at a time. A Gemini answer changes with the wording of the question, the surface and the run. So the useful number is not a single position. It is how often you are named across the questions your buyers actually ask, repeated enough times to see the pattern.

How do you write for Gemini?

Write pages that answer one buyer question clearly at the top of each section, in words Gemini can lift and quote on their own. Use question headings that match how buyers ask, back claims with primary sources, and keep structured data true to what the page shows. Clear, specific and checkable beats long and general.

Lead every section with the answer

Put a short, self-contained answer right under each heading: two to four sentences that still make sense if Gemini quotes them on their own. Then give the detail, the examples and the evidence. A section that builds up to its point is harder to cite than one that states it first.

Use the buyer's question as the heading

Buyers type full questions into Gemini, often with their context attached: team size, stack, industry, budget. Headings such as "How long does setup take for a team on HubSpot?" match those questions far better than a label such as "Implementation". Map the real questions first, then write to them.

Cite primary sources

When two pages say the same thing, the one that shows its evidence is easier to trust. Link to the original study, the official documentation or your own first-party data, not to a summary of a summary. State numbers with their source and date.

Keep structured data honest

Structured data helps Google understand what a page is about, but Google is clear that AI features need no special markup [1]. Use the types that fit the page (Organization, Article, Product, FAQPage where the FAQ is visible) and make sure every value matches the text a reader sees. Markup that says more than the page does helps no one.

Do you need an llms.txt file?

Not for Google. Google says there are no special files or AI-specific markup needed to appear in its AI features [1]. Other AI models may read such a file, and it does no harm, but it does not replace an indexable page with a clear answer on it.

Which signals does Gemini weigh?

Gemini leans on the same kinds of evidence a careful buyer would: pages that show real expertise, claims backed by sources, third-party reviews and discussions, and a brand description that matches across the web. Weakness in one area can keep you out of an answer even when your own site is strong.

When Gemini recommends a brand, it puts its own credibility behind that answer. So it favours brands the web already vouches for. Five groups of signals matter most in our audits.

1. Expertise you can see. Named authors, first-hand detail, clear methods and dated updates. Pages that read as if someone did the work, not as a rewrite of other pages.
2. Evidence behind claims. Primary sources, original data and specific, checkable statements.
3. Third-party voices. Reviews, comparison articles, community threads and videos where buyers discuss you. Gemini reads these for the same questions your buyers ask.
4. One consistent story. The same category, audience and description on your site, your profiles, directories and review pages. Mixed descriptions produce vague or outdated answers.
5. Freshness. Current pricing pages, current positioning and dated content. Old reviews keep shaping answers until newer sources replace them.

None of this is a trick. The brands Gemini names most often are the ones that are easiest to describe accurately. Our work is to make that true for you on every surface Gemini reads.

How do you measure Gemini visibility?

Measure Gemini by running your real buyer prompts many times on each surface and counting how often you are named, where you sit in the answer and which sources are cited. Track the Gemini app, AI Overviews and AI Mode separately, compare yourself with the brands named instead, and tie each change to the fix that caused it.

Why "rank #1" does not exist here

The same prompt can return a different answer on the next run. One check tells you almost nothing. A repeated set of prompts, written in your buyers' words and run on a schedule, shows the pattern: which themes you win, which you lose and to whom.

What to track

• Named rate. The share of runs in which Gemini names you for a prompt theme.
• Position. Whether you are the first recommendation or a footnote.
• Sources cited. Which pages Gemini cites when it names you, and when it names someone else.
• Objections. What Gemini says against you when buyers are close to a decision.
• Every surface and every model. Gemini app, AI Overviews and AI Mode, next to every other major AI model your buyers use.

We track all of this in Max Visibility, our own AI visibility platform, which is included for full-service GEO clients. The audit in week one sets the baseline. Most clients see initial results and leading indicators within 60 to 90 days, depending on category and how fast fixes ship. We show each change model by model rather than promising a date.

Gemini is one part of a wider program. See how we approach every AI model in Generative Engine Optimization, and how we fix the crawl and indexing foundation in the technical SEO audit.

References

1. Google Search Central, "AI features and your website".
2. Google Search Central, "Google's common crawlers" (Google-Extended).
3. Google Search Central, "Robots meta tag specifications" (nosnippet).