How Does Your Brand Show Up in ChatGPT and Gemini? A Guide to Increasing AI Visibility

How does your brand build AI visibility in ChatGPT and Gemini?

AI visibility is the product of three layers: findability, quotability and brand proof. Specifically, findability means AI crawlers can reach and index your pages. Quotability means those pages answer questions in short, sourced passages. Brand proof means independent sites mention your name consistently. A brand that scores well on all three appears in ChatGPT and Gemini answers more often.

I have worked in digital marketing in Istanbul since 2012, and for most of that time the goal was a position on a results page. That goal still matters, of course. However, a growing share of my clients now ask a different question: does ChatGPT or Gemini mention us when someone asks for a recommendation?

This guide answers that question in practice. First, I explain where each assistant gets its information. Then I walk through the three layers one by one, with checklists you can run this week. Finally, I show how to measure the result and how to plan the first 90 days without guesswork.

What is AI visibility, and how does it differ from classic SEO?

AI visibility describes how often an AI assistant names your brand or cites your website when it answers a relevant question. Classic SEO, by contrast, measures positions and clicks. AI visibility measures mentions and citations, and those can happen without a click at all. In other words, the unit of success has changed.

The overlap is still large, because both disciplines reward crawlable, useful, well structured pages. Therefore, I treat AI visibility as an extension of SEO rather than a replacement. The Semrush 2026 AI Visibility Index supports this view: 81 percent of organisations that run SEO and AI visibility as one workflow report more traffic or leads from AI platforms, against 36 percent of those that split them.

Where do ChatGPT and Gemini get their answers from?

The two assistants feed on different pipelines, and that difference shapes everything you do next. ChatGPT search uses its own crawler, OAI-SearchBot, to surface websites in answers. According to the official OpenAI crawler documentation, sites that block this bot do not appear in ChatGPT search results.

Gemini, however, takes another route. The Gemini API documentation on grounding with Google Search states that the model bases answers on live Google Search results and links text segments to source URLs through citations. In practice, this means Gemini visibility depends heavily on your standing in the Google index.

So the strategy splits into two tracks. For ChatGPT, you open the door for OAI-SearchBot and earn mentions on the sites it likes to cite. For Gemini and Google AI Mode, you keep doing rigorous technical SEO and write passages that Google can lift into an answer. Both tracks share the same content foundation, which saves you from doing the work twice.

Why does this matter now: 900 million weekly users and 58 percent fewer clicks?

Two numbers, above all, explain the urgency. First, OpenAI announced on 27 February 2026 that ChatGPT passed 900 million weekly active users, up from 400 million a year earlier, according to Search Engine Land's report. The same statement mentioned more than 50 million individual subscribers and over 9 million paying business users.

Second, Ahrefs updated its click study across 300,000 keywords and compared December 2023 with December 2025. Where an AI Overview appears, the page in first position receives 58 percent fewer clicks on average. In the first measurement from April 2025 the gap was 34.5 percent, so the effect is widening. You can read the updated Ahrefs study for the method.

Here is an example calculation, clearly labelled as an example. A service page gets 1,000 impressions a month in first position. With an assumed 28 percent click rate, that is 280 clicks. Apply the 58 percent drop and roughly 118 clicks remain. The rate is an assumption; the Ahrefs ratio is real. To recover the gap, the page needs to become a cited source inside the answer, not just a link below it.

Does Google require anything special for its AI features?

No, and Google says so in writing. The Search Central document AI features and your website states that there are no additional requirements to appear in AI Overviews or AI Mode, and no special optimisations are necessary. A page needs to be indexed and eligible for a snippet. In short, that is the whole entry ticket.

In addition, Google's guide to optimising for generative AI features, last updated on 10 July 2026, dismisses several popular myths. You do not need an llms.txt file, Markdown copies of pages, content chopped into tiny fragments, or AI specific schema markup. Instead, Google recommends unique content, a crawlable technical setup, readable heading structure, quality images and video, and measurement through the generative AI report in Search Console.

The same snippet controls also still apply. If a page carries nosnippet, data-nosnippet, a tight max-snippet value or noindex, those rules also limit its use in AI features. Therefore, before any creative work, I check that important pages do not carry leftover restrictions from an old plugin or template.

Which AI bots should you allow in robots.txt?

Bot management is the most misunderstood part of AI visibility, because the names look similar but the jobs differ. For example, OpenAI runs three crawlers. Google also adds Googlebot and a separate Google-Extended token. Each one deserves a deliberate decision rather than a blanket block.

The key insight comes straight from OpenAI's documentation: you can block the training crawler and keep the search crawler open. Many sites copied a rule set in 2023 that blocks everything with GPT in its name. If you did the same, then you probably locked yourself out of ChatGPT search. My robots.txt generator produces a clean file with each bot listed separately, so you can decide line by line.

How do you check whether your site is open to AI crawlers?

You can run this check in about ten minutes, and I recommend doing it before touching a single sentence of content. The point, put simply, is to confirm that the findability layer works. Otherwise, every content improvement lands behind a closed door.

1. Open yourdomain.com/robots.txt and read the lines for OAI-SearchBot, GPTBot, ChatGPT-User and Google-Extended.
2. Search your server log for those user agent strings and note when each bot last visited.
3. Check the Pages report in Search Console and confirm that your key pages show as indexed.
4. Scan the source of those pages for nosnippet, data-nosnippet, max-snippet and noindex.
5. Test the final URL of each page with my redirect checker, because chained redirects waste crawler visits.
6. Confirm DNS and hosting respond quickly with the DNS lookup tool.

If the log shows no OAI-SearchBot visits for months while robots.txt allows it, the problem usually sits elsewhere: a firewall rule, a bot protection service, or a CDN setting. In that case, ask your host to whitelist the published user agents. Then repeat the log check after two weeks.

Which content does AI actually cite?

The Semrush 2026 AI Visibility Index analysed 126 million US AI search prompts between January and April 2026. According to the published summary, ChatGPT shows an average of 15 sources per answer while Gemini shows about 3. That gap, in practice, changes your odds. With 15 slots, a good niche page can get in; with 3 slots, only the strongest sources survive.

The same study found that in Gemini, the overlap between the brand mentioned and the domain cited can drop to 30 percent. Put simply, Gemini may talk about you while citing someone else's page about you. Brand proof therefore counts as much as your own content. In addition, 45 percent of marketing leaders admitted they cannot measure brand visibility in AI answers accurately.

Source preferences also move fast. A separate Semrush study covering 13 weeks and 230,000 prompts showed Reddit citations in ChatGPT falling from roughly 60 percent in early August 2025 to about 10 percent by mid September, while Wikipedia slid from about 55 percent to under 20. Google AI Mode spread citations more evenly, with LinkedIn near 15 percent. The lesson, in short, is simple: never build visibility around one platform's current favourite.

How do you write content that AI can quote?

Quotable content answers a question in a self contained passage that makes sense without the rest of the page. I use a fixed template for every section on a client site, and it works because it mirrors how assistants extract text. Here, then, is the template in five steps.

1. Write the heading as the exact question a customer would ask.
2. Open with a 40 to 60 word answer that begins with a definition or a method, not with a warm up sentence.
3. Add one number with a named source in the next paragraph.
4. Include a list or a table that a model can lift as a unit.
5. Close the section with one sentence that positions your brand in that topic.

Sentence length also matters more than most writers expect. Assistants rarely quote a 45 word sentence, so I keep most sentences under 20 words. My readability checker flags the long ones. Meanwhile, the keyword density tool shows whether a topic term appears often enough to signal relevance without stuffing.

Finally, avoid vague claims like "industry leading" in the answer paragraph. A model cannot verify them, so it skips them. A precise statement with a date and a source, however, is exactly what gets quoted.

How do you teach AI your brand name?

An assistant learns a brand the way a new colleague does: by hearing the same name, the same description and the same facts in many places. Inconsistency, however, is the silent killer here. If your site says one thing, LinkedIn says another and a directory lists an old address, the model hedges or ignores you.

First, start with the basics on your own domain. Write an About page that states who you are, where you operate, what you do and since when, in plain sentences. Use the same brand name spelling everywhere, including capitalisation. Give every article a named author with a short bio, because assistants weigh author identity when they decide what to trust.

Then extend the same facts outward. Specifically, your Google Business Profile, LinkedIn company page, industry directories and social profiles should repeat the identical description. Organisation markup helps keep this tidy; my schema generator creates it in a minute. If the brand story itself is muddled, that is a positioning problem first, and I usually solve it through brand identity work before any AI visibility campaign.

Why can third party mentions be stronger than your own site?

Assistants trust independent sources more than a brand talking about itself, and the citation data reflects that. In the 13 week Semrush study, community platforms and reference sites dominated ChatGPT citations, while LinkedIn held around 15 percent of Google AI Mode citations. None of those are your website. Consequently, the brand proof layer often decides whether you get named.

The practical sources I work with fall into a few groups. Each one needs a slightly different approach, so I plan them as separate mini projects rather than one vague "PR" line item.

• Expert quotes and guest contributions in trade publications that your customers already read.
• Complete, consistent profiles in industry directories and comparison lists.
• Customer reviews on platforms relevant to your category, with responses from your team.
• LinkedIn posts from named people at your company, not only the company page.
• Creator collaborations that mention the brand naturally, which I structure through influencer marketing.

Above all, aim for spread rather than volume. Twenty mentions across ten different platform types survive a citation shift far better than two hundred mentions on one forum that a model drops next month.

Which technical SEO tasks matter most for AI visibility?

Technical SEO for AI visibility is mostly the technical SEO you should already have, applied with more discipline. Assistants crawl less patiently than Googlebot and cite pages that render cleanly on the first try. So the priorities below focus on speed, clarity and coverage.

• Fast server response and no render blocking scripts in front of the main text.
• Clean semantic HTML with one H1, ordered H2 and H3 headings and real paragraph tags.
• A current XML sitemap that lists only canonical, indexable URLs; my XML sitemap generator builds one quickly.
• Correct hreflang between language versions, so each assistant cites the right language page.
• No accidental noindex or snippet limits on high value pages.
• Internal links from strong pages to the passages you want cited.

Crawl budget deserves a specific mention. If a site exposes thousands of filter URLs, an AI crawler may spend its visit there and never reach your best guide. Therefore, I block parameter noise and consolidate thin pages before promoting anything. This is standard work within my SEO consulting engagements, and if the site itself is outdated, a rebuild through web design often costs less than patching.

Does schema markup increase AI visibility?

Schema markup does not directly increase AI visibility, and Google's own guidance says AI specific markup is unnecessary. However, that is not the same as saying schema is useless. Structured data clarifies entities: who wrote a page, which organisation stands behind it, what a product costs, which questions a page answers.

In my experience, that clarity pays off indirectly. Consistent Organisation and Person markup reduces the chance that an assistant confuses you with a similarly named company. Article markup with author and date gives a model the provenance signals it uses when choosing between two similar passages. FAQ markup keeps your answers in a machine readable form that mirrors the question and answer structure assistants prefer.

So my rule is modest: implement the standard types correctly, validate them, and stop there. Do not invent custom markup for AI, and do not expect schema to rescue a page with weak content. The schema generator covers the standard types I use for clients.

How do multilingual brands show up in ChatGPT and Gemini?

Multilingual visibility works one language at a time, because a Turkish prompt, an English prompt and a German prompt trigger different source sets. A brand that dominates Turkish answers can be invisible in German ones. For that reason, I run the manual prompt test separately for each market and never assume results carry over.

Three things move the needle. First, real translated pages with their own quotable passages, not a machine translated copy of the home market site. Second, correct hreflang annotations so Google, and by extension Gemini, associates the right page with the right language; my hreflang generator produces the tags. Third, country specific brand proof: German directories, German trade press, German LinkedIn posts.

In addition, check the language of the sources that assistants already cite for your category in each market. If German answers lean on a handful of local portals, those portals are your priority for mentions. This is exactly the kind of work I describe in the 90 day plan below, repeated per language.

How do you measure your visibility in ChatGPT and Gemini?

Measurement combines one official report with a few manual routines. Google added generative AI performance reports to Search Console on 3 June 2026, rolled out worldwide by 31 August 2026. The report breaks impressions down by page, country, device and date, and AI Overviews and AI Mode clicks also sit inside the Web search type of the standard Performance report.

For ChatGPT there is no equivalent dashboard, so I keep a manual prompt panel. The routine is simple and repeatable.

1. Write 20 prompts that reflect real buying questions in your category.
2. Run each prompt on ChatGPT and Gemini once a month, in a fresh session.
3. Record three things per prompt: was the brand mentioned, was a URL cited, which competitors appeared.
4. Store the results in a simple table and compare month over month.

Two more signals complete the picture. Watch for direct traffic without UTM parameters that arrives on deep guide pages, since assistants often strip tracking. Tag your own campaign links with the UTM builder so that untagged traffic stands out. Also, grep your server log for ChatGPT-User visits, because each one usually means a person asked the assistant about a page of yours.

Should you switch off Google's new AI control?

For most brands, no. The control that Google introduced alongside the generative AI reports lets site owners decide whether their site appears in generative AI search features. Sites that opt out receive no traffic or impressions from those features. Google also states that the choice does not act as a signal in classic Search ranking.

The decision comes down to your business model. A publisher that lives on page views and sees an answer summary cannibalising clicks might test the switch. A service business, a manufacturer or a consultancy, however, gains from being named in the answer even when the click never comes. In those cases the assistant behaves like a referral, and referrals are worth keeping.

If you do consider opting out, measure first. Run the generative AI report for at least eight weeks, note the impressions and any leads that mention AI tools, and compare that with the click loss you fear. In short, treat it as a reversible experiment rather than a philosophical stance.

What are the most common AI visibility mistakes?

The mistakes I see repeat across industries, and most of them come from copying advice without checking the source. Here are the ones that cost the most, along with the fix I apply in each case.

• Relying on an llms.txt file as a strategy. Google says it is unnecessary; write better passages instead.
• Blocking OAI-SearchBot while trying to block GPTBot. Separate the two rules and re-test.
• Optimising for a single platform's current favourite source. Citation shares moved by 50 points in six weeks last year.
• Publishing invented statistics to sound authoritative. Models cross check numbers and drop pages that contradict known sources.
• Letting brand descriptions drift between site, profiles and directories. Align them before promoting anything.
• Measuring nothing beyond a screenshot. Set up the 20 prompt panel and the Search Console report from week one.

One more mistake deserves a paragraph of its own: treating this as a one time project. Assistants refresh their retrieval constantly. A page that gets cited in March can vanish in May because a competitor published a clearer answer. Consequently, the monthly review is not optional; it is the work.

How do you increase AI visibility in 90 days?

A 90 day plan keeps the three layers in the right order: open the doors, make the best pages quotable, earn proof, then measure. The timeline below is the sequence I use with clients, and it assumes one person can spend a few hours a week on it.

1. First, weeks 1 to 2: run the crawler access check, fix robots.txt, remove snippet restrictions, repair redirects and submit a clean sitemap.
2. Next, weeks 3 to 6: rewrite your ten most valuable pages with the quotable section template, one question heading and one direct answer at a time.
3. Then, weeks 7 to 10: build brand proof through LinkedIn posts by named people, complete directory profiles, customer reviews and two or three expert contributions.
4. Finally, weeks 11 to 13: run the prompt panel, read the generative AI report, compare with the baseline and decide what the next quarter focuses on.

Set expectations honestly. Structural fixes show up within weeks, while new mentions take one to three months to influence answers. Those are field based starting ranges, not guarantees. If you would rather have someone run the whole cycle with you, my packages describe how I structure it, and you can get in touch to talk about your market first.