How to Improve Brand Visibility in AI Search

Most marketing teams we audit are invisible in AI search for the queries that define their category. Not partially visible. Zero mentions. When a buyer asks ChatGPT or Google's AI Overview to recommend a tool, a supplier, or a service in their space, these brands simply do not come up, even when they rank on page one of Google for the same terms.

At Geostar we run these audits every week, and the pattern holds across industries. An Ahrefs analysis of 75,000 brands found that 26% have zero AI Overview mentions at all [1]. The old playbook that earned those page-one rankings does not carry over, because AI engines weigh signals differently than Google's classic algorithm ever did.

This is the practical how-to. We will cover what AI engines actually reward, the highest-leverage tactic almost nobody executes well, how to optimize content for citation, the technical foundation these engines require, why each platform behaves differently, and how to measure whether any of it is working.

The Rules Changed (And Most Brands Haven't Caught Up)

Traditional SEO taught marketers to chase rankings and backlinks. That work still matters, but it is no longer the dominant signal for whether an AI engine will name your brand in an answer. The Ahrefs study of 75,000 brands measured which factors correlate most strongly with AI Overview mentions, and brand web mentions came out on top at a 0.664 correlation, well ahead of backlinks at 0.218 [1].

The gap between winners and everyone else is stark. Brands in the top quartile for web mentions earned roughly 10 times more AI Overview mentions than the quartile below them, according to the same study. That concentration is why so many capable companies see nothing: AI visibility compounds around brands the web already talks about, and a strong backlink profile alone does not close the distance.

The commercial cost is real. AI search visitors convert far better than traditional organic visitors, a pattern we return to in the measurement section below. The traffic arriving through AI answers is smaller in volume today but far more qualified, which means invisibility here costs you your highest-intent buyers first. There is also a defensive dimension: Seer Interactive data shows organic click-through rates drop roughly 70% when an AI Overview appears above the results for the same query [2]. Brands absent from AI answers are losing ground in both channels at once.

How AI Search Engines Actually Decide Which Brands to Mention

Google ranks pages and shows you a list of 10 or more links. An LLM synthesizes an answer and typically cites a handful of domains, often two to seven, that it treats as sources of truth for that specific response. Getting into that short list is a different problem than ranking, and it draws on different inputs.

We group the inputs AI engines rely on into three signal types:

• Earned media. Off-site mentions in news, industry publications, community forums, and third-party reviews. This is the strongest driver, and we cover it in depth below.
• Content quality. Answer-first structure and embedded citations, backed by clear entity information that makes your pages easy to quote.
• Technical access. Schema markup, a robots.txt that permits AI crawlers, and server-rendered content the crawler can actually read.

The relative weight of these signals is where AI search departs from the SEO most teams know. Generative Engine Optimization, or GEO, is the discipline of managing all three deliberately rather than assuming ranking work will carry over.

One more distinction shapes tactics: not all AI engines work the same way under the hood. Self-contained models answer from training data and lean on the authority a brand built up to their knowledge cutoff. Retrieval-based systems fetch live pages at query time and cite what they find. The first rewards durable reputation; the second rewards fresh content that is crawlable and well-structured. Most brands need to satisfy both.

Build Your Off-Site Presence (The Highest-Leverage Tactic)

If you do one thing, do this. Edelman's GEO analysis found that up to 90% of the citations driving brand visibility in LLMs trace back to earned media [3]. That is the single most important number in this article, and it explains why on-site optimization alone plateaus so quickly.

Earned media, in the AI context, is broader than press coverage. It includes news mentions, third-party reviews on sites like G2 or industry directories, discussion in community forums and subreddits, coverage in trade publications, and inclusion in roundup and comparison posts. AI engines read all of it as evidence that your brand is a real, cited entity in its category.

The reason this outranks on-site work is mechanical. A model deciding which brands to name in an answer weighs how often and how credibly the wider web references you, not how polished your own homepage is. You control your own pages completely, which is exactly why they carry less signal: every company's homepage claims it is the best. A mention on a site you do not own is harder to manufacture, so the models trust it more.

Here is how we generate those mentions systematically for clients:

1. Publish data other people want to cite. Original survey results, benchmarks, or analysis from your own work give journalists and bloggers a reason to name you. A citable statistic travels far further than a press release.
2. Get into the roundups and comparison lists. Identify the "best X for Y" and "top tools for Z" articles that rank for your category queries, then pitch to be included. These are exactly the pages AI engines pull from when asked to recommend options.
3. Participate in the conversations already happening. Answer real questions in the forums and communities where your buyers gather. Helpful, non-promotional participation earns the kind of mentions AI engines trust.
4. Run structured review generation. Ask satisfied customers to leave reviews on the third-party platforms your category is judged on, so your brand carries social proof where the models look for it.
5. Pitch earned coverage around genuine news. A product launch, a funding round, or a point of view that connects to a current story gives reporters something to write, and each placement is a durable mention.

There is a subtlety here that separates GEO from traditional SEO: unlinked mentions count. In classic SEO, a mention without a hyperlink passed little value. In AI search, the correlation is driven by the mention itself, whether or not it carries a link. A brand named 50 times across credible sites with no links can outperform one with a cleaner backlink profile but a quieter presence. That changes how you value coverage. Our implementation playbook sequences this work so the highest-leverage placements come first.

Optimize Your Content for AI Citation

Off-site presence gets you into consideration. On-site structure determines whether the model can lift a clean, quotable answer from your page. The universal pattern across every high-performing page we study is answer-first architecture: lead with a direct answer to the question, then expand into detail. Models reward pages that hand them the answer without hunting for it.

Research from Princeton on GEO techniques found that citing authoritative sources and adding statistics meaningfully improve citation rates in LLM responses, with improvements of up to 40% across queries [4]. The practical translation is that the same moves that make content easy for a person to skim also make it easy for a model to quote.

Two of those rows deserve emphasis. First, hub-and-spoke clustering: a single strong page rarely establishes you as an entity a model trusts on a topic, whereas a cluster of interlinked pages covering the subject in depth does. Second, information gain: AI engines have no reason to cite the tenth article that says the same thing. Original client data, a proprietary framework, or a genuine case study gives the model something it can only get from you.

Question-based subheadings help because AI prompts are conversational. Users of AI-powered search tend to phrase queries in full sentences of roughly 15 to 23 words rather than typing short keyword phrases, so headers written the way people actually ask questions align your content with the prompts it needs to match. Our deeper guide on optimizing content for AI search engines walks through the structural details.

One practical test we run on client content: submit the main question the page targets into ChatGPT and see whether your page would be the obvious source to quote. If the answer requires language your page buries in the third section, restructure so the answer lands in the opening paragraph. The model should find it in the first scroll.

Fix the Technical Foundation AI Engines Require

None of the above matters if the crawler cannot reach your pages. We find broken foundations constantly, and the fixes are usually fast.

• Explicitly allow AI crawlers in robots.txt. The major AI bots need to be permitted by name. That means GPTBot and OAI-SearchBot for OpenAI, PerplexityBot for Perplexity, and both ClaudeBot and anthropic-ai for Anthropic. This is not the default, and plenty of sites silently block the very engines they want to appear in. When we ran our own site audit, we found that our robots.txt did not explicitly allow AI crawlers. It is among the most common gaps we see.
• Ship schema markup. Organization, Article, FAQ, and HowTo schema give engines structured facts about your pages. Schema markup has become standard among pages that earn consistent AI citations, which makes it table stakes rather than a differentiator. Our complete guide to schema markup covers the implementation.
• Render content server-side. Most AI crawlers do not execute JavaScript. If your content only appears after a client-side render, the crawler sees an empty page. Server-side rendering or static generation puts the words where the bot can read them.
• Keep content fresh. Retrieval-based engines favor recently updated pages. A publish date from three years ago signals stale information, so revisiting and updating cornerstone content earns you standing with the systems that fetch live results.

These four fixes take a day or two to implement but are permanent infrastructure for everything else. We run them first on every engagement because there is no point in earning great coverage if the bots that would use it cannot read your site.

Cover Multiple Platforms Because Each AI Engine Behaves Differently

Treating "AI search" as one destination is the mistake we correct most often. ChatGPT and Google AI Overviews disagree on brand recommendations for a majority of queries, which makes single-platform optimization an incomplete strategy. What one platform surfaces, another may never mention.

The platforms also cite differently by design. ChatGPT leans heavily on its training corpus and the domain authority a brand accumulated before the model's cutoff, supplemented by live web citations. Google AI Overviews rewards structured data and pulls from the pages already ranking in Google's index. Perplexity is aggressively citation-driven, surfacing sources prominently in nearly every answer, which makes fresh, well-sourced content especially valuable there.

Where to start depends on your audience. For B2B, ChatGPT is the pragmatic first priority: 47% of B2B buyers prefer it, and 50% now begin their software buying journey in an AI chatbot [5]. If your buyers live in that environment, earning ChatGPT visibility returns the most per unit of effort. For platform-specific tactics, our guide on getting your brand mentioned by Perplexity breaks down that environment in detail.

Measure What Matters

Given limited resources, sequence the work rather than attempting everything at once. We start clients with the technical foundation because it is fast and unblocks everything else, then move to content restructuring for citation, then invest the bulk of ongoing effort in earned media, which is the slowest to compound but the highest ceiling. Measurement runs underneath all of it from day one, because without a baseline you cannot tell whether any of the work is landing.

The sequencing matters because the three pillars have different time horizons. Technical fixes resolve in days or weeks once implemented. Content restructured for citation typically registers in retrieval-based systems within a few crawl cycles. Earned media compounds over months: each new mention raises the floor of your visibility, but the effect is gradual. Setting that expectation with stakeholders before you start avoids the common trap of abandoning a program that is working but has not yet peaked.

You cannot manage AI visibility you are not tracking, and the metrics are different from the rankings dashboards most teams live in. We track five things for every engagement:

1. AI share of voice. How often your brand appears versus competitors across a fixed set of tracked category queries. This is the headline number, and it is the one that moves as earned media accumulates.
2. Citation URL tracking. Which specific pages are being cited, and by which platforms. This tells you where to double down and which content is actually earning quotes.
3. AI-referred traffic. Segment your analytics for sessions arriving from AI sources like ChatGPT and Perplexity so you can tie visibility to real visits and conversions.
4. Entity correctness. Run test queries and check whether the AI describes your brand and its positioning accurately. Models sometimes get facts wrong, and correcting the source material is part of the work.
5. Time to citation. How quickly new content or a fresh earned mention starts showing up in AI answers. This tells you how responsive each platform is to your efforts.

Because AI search visitors convert 4.4 times better than traditional organic [5], the return on this measurement is favorable even at low traffic volumes. A small number of highly qualified visits justifies the tracking. Our measurement framework lays out how to instrument each of these.

If you want to see where you stand today, we run a free GEO and growth audit that checks your AI share of voice, technical foundation, and earned media footprint in one pass. You can book a free audit and get a concrete read on your starting position.

Frequently Asked Questions

**How long does it take for changes to show up in AI search results?**Retrieval-based platforms like Perplexity can reflect fresh content or new mentions within days once they are crawled. Training-based systems like ChatGPT move slower, since durable reputation and broad web mentions accrue over weeks and months. Plan for a horizon of several weeks to see meaningful movement, with earned media being the slowest but most durable lever.

**Does traditional SEO still matter if AI search is growing?**Yes. Google AI Overviews pulls from pages that already rank, so classic SEO still feeds a major AI surface. The shift is one of emphasis, not replacement: keep the SEO foundation and add earned media, structured data, and citation-ready content on top of it.

**Which AI platform should I prioritize first?**For most B2B brands, ChatGPT is the pragmatic starting point because it captures the largest share of buyer attention. If your audience skews toward research-heavy buyers who value visible sources, Perplexity deserves early focus. Let your audience's behavior, not the platform's size, decide.

**Can small brands compete with large ones in AI search?**Yes, and more easily than in traditional SEO. Because unlinked mentions and topical depth matter as much as raw domain authority, a focused brand that earns credible coverage and publishes genuinely useful content can outperform a larger, quieter competitor in a specific category.

**What is the difference between GEO and SEO?**SEO optimizes to rank pages in a list of search results. GEO, or Generative Engine Optimization, optimizes to get your brand cited and recommended inside AI-generated answers. They share a technical foundation, but GEO puts far more weight on earned media, entity clarity, and content that models can quote directly.

References

[1] Louise Linehan, Xibeijia Guan. "An Analysis of AI Overview Brand Visibility Factors (75K Brands Studied)." Ahrefs Blog, 2025-05-26. https://ahrefs.com/blog/ai-overview-brand-correlation/

[2] Nick Haigler. "How AI Overviews Are Impacting CTR: 5 Initial Takeaways." Seer Interactive, 2024-09-24. https://www.seerinteractive.com/insights/how-ai-overviews-are-impacting-ctr-5-initial-takeaways

[3] Nick Taylor. "How Brands Can Stay Visible in an AI-Driven Search World." Edelman, 2025-05-09. https://www.edelman.com/insights/how-brands-stay-visible-ai-search

[4] Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande. "GEO: Generative Engine Optimization." Princeton University / arXiv, 2023. https://arxiv.org/abs/2311.09735

[5] Asher Rumack. "AI Search Visibility Stats That Might Surprise B2B SaaS Marketers." Column Five Media, 2026-01-27. https://www.columnfivemedia.com/ai-search-visibility-stats-that-might-surprise-you-in-2026/