Gemini SEO: brand visibility inside Google

Gemini summarises, and only a compact answer can be summarised. If yours is scattered across half a text, the model takes it from elsewhere, even if your page ranks above every competitor.

One sentence from the model can come from three different sites. If yours is not among them, the customer will not even see your company name, let alone the offer.

For services to local customers, Gemini looks at the Google business profile and the reviews first. A neglected profile covers even a well-written page.

Gemini SEO is work to make Google's model name your brand and give your page as a source in the answers it shows in three places: in AI Overviews above the search results, in its app and in the office suite. The difference from other language models is practical: Gemini draws on the Google index, so a fix made for the search engine counts here from the first reindexing, not from the next model version.

What we hear when we propose work on Gemini

“We have had SEO for years, why a separate service”

With Gemini you are closer to the finish line than with any other model, and we say so on the first call. What is left is the form of the content, structured data and your Google business profile, all of which fit in the same SEO subscription you already have.

“Nobody in Poland uses Gemini”

The app has a smaller reach in Poland than ChatGPT, and that is true. But Gemini also speaks in the search engine itself, above the results, and everyone ends up there. The number of app downloads says nothing about how many people will see its answer about your industry.

“We will wait until it settles down”

We are not waiting for a product, only for reindexing. The changes we propose are read today by the ordinary search engine, so even if Google dropped the Gemini name in a year, the tidied content and business profile would stay where they were.

This page is only about Gemini, and the umbrella page describes the whole family of engines. We do not sell a place in the answer, and we do not promise that Gemini will cite you in particular. The model builds every sentence anew, and the choice of sources is made on Google's side, with no field where anyone could apply for a spot in that answer. So we agree on something else: your brand's share across an agreed set of questions and the work that raises that share.

One answer is not a measurement, because the next query looks different. That is why we ask the same set of questions every week, report the reading every month and discuss the conclusions and the change in share once a quarter: in a shorter window you mostly see noise.

• Your customers search on Google and read the answer above the results.
• You prefer one team for SEO and model visibility rather than two.
• You want a dated share figure, not a screenshot of one question.
• You accept that the first shift shows after six to twelve weeks.

• You want to cover all models at once: that is AI SEO.
• You are interested in ChatGPT: it has its own page and its own measurement.
• You want a one-off state measurement first: that is the AI Overview audit.

LLM SEO is a broader term than this service. It covers visibility in every large language model that answers with a sentence instead of a list of links, so also in ChatGPT, Perplexity and in search engines built on generative AI. Gemini breaks away from this group through one feature that translates directly into pricing: it sits in the Google ecosystem and uses the same index as the search results. Traditional SEO does not stand next to the work on the model here, it is its foundation.

That does not mean one technique works the same everywhere. The same material gives a different effect in Gemini, in ChatGPT and in Perplexity, because each of them draws on different sources and decides differently what to cite. That is why the strategy starts with the question of which visibility you are after. If it is all channels at once, the umbrella service is the right one, not optimisation for a single engine: content and structured data are then created once and count in all of them.

In Gemini itself we measure four things separately, because a brand gets into them for different reasons: answers in the app, AI Overviews summaries above the search results, the classic ranking on Google and which domains the model gives as a source instead of yours. The last of these figures is the least obvious and the most useful, because it tells you where your customer will read the answer they could have read on your site. Fighting for position and editing for citation are two different tasks.

With a generated answer, getting a position is no longer the only problem. The system still has to decide which information it trusts enough to put into a sentence signed with its own name. That is why, alongside the text on the page, we work on what the model finds outside it: named authors with a track record, your own data, case studies with numbers, reviews, industry publications and consistent company information on external sites. The more independent confirmations, the harder it is for the model to get your brand wrong.

Data nobody else has ranks highest. A study, a benchmark or a case study with specific numbers gives you a chance to become the source instead of another page repeating general knowledge. That is the difference between content that gets cited and content that gets skipped: the first adds a fact, the second repeats what the model already knows from a thousand other places.

The layout of the text matters too, though this is not about formatting for show. The model copes better with information that is unambiguous, placed at the start of a section and not hidden between promotional sentences. In practice this means one question per section, a short answer in the first sentence, the detail below and a source next to every number. People read such text faster, so the writing loses nothing.

This is closer to Generative Engine Optimization than to adding a few questions to an FAQ. Keywords stay in play, we just extend them with questions, comparisons and requests for recommendations, which is how people really talk to a model. The heading then answers the customer's question, not a keyword from a research tool.

Gemini SEO: frequently asked questions

Gemini SEO: what is worth knowing