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Search Engine Land » Guides » What the AI Visibility Index tells us about LLMs & search
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Generative AI is reshaping how people search and discover information, making traditional SEO metrics like rankings and click-through rates incomplete.
To address this, Semrush’s AI Visibility Index provides a holistic view of marketing visibility, layering LLM visibility data alongside traditional search. It reports on both global insights and industry-specific breakdowns, helping marketers see the bigger picture of where their brands stand.
The numbers speak for themselves: ChatGPT alone sees over 800 million active users weekly and more than 2.5 billion prompts daily, much of which is invisible to conventional analytics. That’s a lot of potential audience engagement.
With AI-powered search experiences—from Google’s Search Generative Experience to ChatGPT, Claude, and Perplexity—brands now need to think about “LLM visibility,” or how often they surface in AI-generated responses. Understanding and optimizing for this new dimension is quickly becoming a core part of brand tracking and digital strategy.
It’s tempting to think that if you’re crushing it at E-E-A-T, you’ll see killer rankings in the SERPs and you’ll be frequently mentioned in LLMs as well as cited as a source.
Think again. You’ll need a more refined approach.
We already know that AI frequently cites lower-ranking search results. But the way AI handles specific brands is even more nuanced. That same Index found that fewer than 25% of the most mentioned brands were also the most sourced.
Take B2B SaaS leader Zapier. The Index found that it’s the #1 cited search in digital technology and software, but only #44 in brand mentions.
What are LLMs picking up on? The answer lies in the types of assets associated with the brand’s presence. Zapier maintains a large library of integration guides and tutorials, giving it strong authority for AI training since it’s effectively a repository of facts. But when people talk in reviews and discussions, competing brands tend to come up more often.
Publishing a ton of content on your site alone is not enough. LLMs also rely on specific platforms as sources they’ve identified as authorities, providing a knowledge foundation. New information is compared to and understood in terms of this foundation. Your presence on these platforms can also help your visibility.
Why does this matter? A single mention in an AI response might carry more weight than a traditional search result because it’s presented as a curated, authoritative recommendation rather than one of many options. It also might reach a more motivated audience that’s closer to conversion.
Data also shows that the weighting of sources varies by LLM as well as by industry. For example, while ChatGPT tends to rely on Wikipedia and Reddit as major sources, Google AI Mode shows way more variation.
These snapshots of industry trends are powerful starting points for your work and can help illuminate next steps for the next 90 days. But keeping close tabs on your competitors in both the SERPs and LLMs will help you not only directly outpace them, but could also give you insights into trends and headwinds.
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This checklist highlights practical steps you can start on now to make your content and brand more discoverable in AI-powered environments. From technical SEO adjustments to content rewrites and community engagement, these actions help ensure your entity is understood, cited, and trusted by both search engines and generative AI platforms.
AI-driven crawlers such as GPTBot (OpenAI), CCBot (Common Crawl), and Claude-We (Anthropic) function similarly to traditional search engine bots, but their role is to feed information into large language models.
If your robots.txt file accidentally blocks them, whether through overbroad disallow rules or inherited directives, you’re effectively shutting the door on your content being indexed for use in generative AI platforms.
This doesn’t just affect training data; it also limits your chances of being surfaced in real-time responses, summaries, and AI-powered search features.
Regularly auditing your robots.txt file, testing crawler access, and aligning permissions with your visibility goals helps ensure that your brand’s knowledge remains accessible where discovery increasingly happens: inside AI-driven engines and assistants.
Many AI systems, like traditional crawlers, still have difficulty parsing client-side rendered content that depends heavily on JavaScript. If your critical text, product information, or structured data only appears after scripts execute, there’s a strong chance it won’t be fully captured or indexed by AI crawlers. Server-side rendering (SSR) solves this by delivering pre-rendered HTML directly from the server, ensuring that the essential content is visible at the time of crawl.
This approach not only improves accessibility for AI systems like ChatGPT or Perplexity but also enhances performance for users on slower connections. By implementing SSR for high-value pages—such as product detail pages, core service offerings, and FAQ hubs—you create a consistent, crawlable foundation that boosts visibility in both traditional search results and generative AI outputs.
Using elements like <article>, <section>, <nav>, and <header> provides explicit signals about the role and relationship of different blocks of content. For example, wrapping a blog post in <article> tells crawlers this is a standalone piece of information, while <aside> can mark supporting context.
Heading hierarchies (H1 through H6) work the same way: they create a logical outline of your content. An H1 defines the page’s core topic, H2s break it into key themes, and H3s or H4s drill into supporting points. AI crawlers use these patterns to parse and extract information accurately, which directly influences whether your content can be cited in generative responses. Pages that rely only on visual styling (bold fonts, larger text) without semantic tags risk being misinterpreted or overlooked entirely.
By consistently applying semantic HTML and clear heading structures, you make your site more machine-readable, improve content extractability, and increase the odds that both search engines and AI platforms pull your information into summaries, snippets, and conversational answers.
When you test your priority topics in AI platforms such as ChatGPT, Claude, or Perplexity, you’re essentially auditing the sources those systems deem trustworthy and authoritative. Pay close attention to which competitors, publications, or data sets are consistently cited in responses. This reveals the knowledge graph you’re competing against—the ecosystem of entities, sources, and relationships that AI models use to assemble answers.
If your competitors’ blogs, research reports, or even forum contributions appear but your brand does not, that’s a signal of a content or authority gap. It may mean you need to produce more in-depth resources, pursue stronger backlinks and citations, or distribute expertise across third-party sites that AI systems favor. Over time, this type of monitoring shows patterns: which types of sources (academic studies, media outlets, niche blogs, Q&A forums) carry the most weight and where you can strategically position your brand to earn visibility.
In practice, this exercise turns AI outputs into a form of competitive intelligence, helping you benchmark your brand’s presence in generative ecosystems and prioritize the content formats and authority signals that AI models actually reward.
AI optimization (AIO) tools like the AI SEO Toolkit extend beyond traditional SEO analytics by tracking how your brand shows up inside generative platforms. Instead of just measuring keyword rankings or backlinks, these tools reveal your presence across AI assistants and AI-powered search, showing how often your brand is cited compared to competitors, what topics are working for you, and what prompt opportunities are out there. They also surface mentions in AI-generated outputs, letting you see whether platforms like ChatGPT, Claude, or Perplexity are pulling your brand into responses, and in what context.
Equally important is sentiment analysis. AIO tools can detect whether your brand is described positively, negatively, or neutrally within these generative answers. This helps you understand not only how visible you are, but also how you’re being framed in the conversations that influence decision-making.
For example, if competitors are cited more frequently or framed more favorably, it highlights opportunities to strengthen your authority signals through better content, reviews, or media coverage.
You also see Share of Voice alongside sentiment. In traditional marketing and SEO, Share of Voice measures what percentage of the overall conversation or visibility your brand commands compared to competitors. In the world of AI optimization tools, it works the same way, but the “conversation” is happening inside generative platforms like ChatGPT, Claude, Gemini, or Perplexity.
For example, if you ask an AI assistant 100 queries in your category (say, “best project management software” or “how to reduce cloud costs”), and your brand is cited or mentioned in 20 of those answers, your Share of Voice would be 20%. The tools automate this at scale: They track how often your brand appears, how it’s positioned (top citation vs passing mention), and how that compares to key competitors.
This metric is powerful because it tells you how much space your brand occupies in AI-driven discovery ecosystems, where traditional ranking reports can’t reach. A rising Share of Voice means AI platforms increasingly trust and surface your content, while a declining one is an early warning that competitors are capturing attention in generative results. In the AI SEO Toolkit, you can dive deeper into what’s driving positive or negative sentiment and figure out what’s needed to put your brand in the best light, authentically.
In short, these tools give you a new layer of competitive intelligence—one that reflects the realities of AI search. By monitoring visibility, mentions, and sentiment, you can benchmark your standing, identify gaps, and adapt your strategy to stay discoverable and credible in the spaces where users are increasingly finding answers.
LLMs are designed to surface clear, factual, and verifiable information. When content leans too heavily on buzzwords or vague promises—“industry-leading,” “cutting-edge,” “best-in-class”—AI systems have little substance to work with and are less likely to extract or cite it. What resonates instead are specifics: quantifiable data, well-attributed statistics, expert commentary, and case studies that provide evidence.
For example, instead of writing “Our platform improves efficiency,” you might say “Our platform reduces average processing time by 37%, based on a study of 500 enterprise clients.” Similarly, weaving in expert quotes from industry leaders or referencing reputable sources signals to AI that your content is trustworthy and grounded in fact. This not only increases your odds of being cited in AI-generated responses but also builds credibility with human readers who demand more than marketing spin.
Ultimately, the more concrete, evidence-backed language you embed in your content, the more LLMs will view it as useful material to pull into conversational answers, summaries, and overviews—directly boosting your visibility in generative search.
Adding FAQ sections to your most important pages—such as product, service, and topic hubs—bridges the gap between how people ask questions and how AI systems deliver answers. Users often type or speak their queries in a natural, conversational way (“How much does this cost?” “Is this safe for beginners?”), and AI platforms are built to recognize and respond to this Q&A structure. By anticipating these questions and embedding them into your content, you make it easier for generative models to parse, extract, and cite your information.
FAQs also help you capture long-tail queries that may not justify standalone pages but are still valuable for traffic and visibility. When optimized with clear, concise answers—and, where relevant, enriched with structured data markup—they can feed directly into AI Overviews, featured snippets, and conversational responses. This not only boosts your chances of being quoted but also positions your site as a reliable resource when users are in research or decision-making mode.
Over time, well-crafted FAQs strengthen your site’s role as a conversational authority, ensuring that both search engines and AI-driven platforms recognize your content as aligned with the way people actually ask questions.
Active participation in niche online communities like Reddit, Quora, and GitHub not only builds trust with human audiences but also increases your footprint in the sources that AI systems frequently draw from. These platforms are rich with Q&A-style content, practical solutions, and peer-to-peer insights, which are exactly the kind of material large language models index and surface in generative answers.
The key is to engage authentically. Instead of dropping links or promotional soundbites, focus on answering questions thoroughly, sharing unique perspectives, or contributing code snippets and documentation where relevant. Over time, your contributions can gain upvotes, visibility, and citations, signaling authority both to users and to AI models trained on that content.
By embedding your expertise into these high-signal communities, you effectively plant seeds of authority across the web. These contributions not only drive referral traffic and brand recognition but also increase the likelihood that your insights will be quoted, paraphrased, or referenced in AI-generated responses, extending your reach well beyond your owned channels.
Reviews act as social proof not only for people but also for AI systems. Generative models scan sentiment signals from platforms like Google, G2, Trustpilot, Yelp, or industry-specific directories, and they often incorporate those perspectives into summaries and recommendations. If most of the visible reviews about your brand are negative or if you lack reviews altogether, AI may interpret your entity as less credible, reducing the likelihood of being favorably cited in conversational search.
The solution is to proactively encourage authentic reviews from satisfied customers and manage feedback consistently. Prompting clients at the right moment in their journey, simplifying the review process, and addressing complaints constructively all help shift sentiment in your favor. Positive patterns of feedback create a reputation signal that both users and AI can trust, while thoughtful responses to negative reviews demonstrate accountability.
Over time, this builds a reputation moat: a body of credible, third-party validation that AI systems draw from when generating answers. In practice, that means your brand isn’t just trusted by human audiences; it becomes algorithmically trusted, increasing your chances of being surfaced, recommended, and cited across AI-driven platforms.
Media coverage functions as a high-value trust signal in both traditional SEO and AI-driven discovery. Mentions in reputable outlets—whether through press articles, interviews, podcast appearances, or industry features—are disproportionately influential because AI models weigh these sources more heavily than self-published content. When your brand is cited by journalists or discussed in respected media, that reference becomes part of the authoritative data pool models use to generate responses.
Strategically, this means pursuing earned media partnerships should go beyond PR vanity—it’s a visibility play in the era of generative AI. Pitching thought leadership pieces, offering expert commentary, or collaborating on podcast discussions not only introduces your brand to new audiences but also creates durable citations that AI systems are more likely to trust and replicate.
By cultivating these relationships, you build a citation footprint that extends across different formats (articles, transcripts, audio summaries). Each mention increases the probability that your brand surfaces in AI overviews, conversational answers, and knowledge graph associations, reinforcing your authority in the spaces that matter most.
Entity-based clustering takes the traditional SEO concept of topic clusters and adapts it for the AI-first search environment. Instead of treating each page as a standalone asset, you organize content around well-defined entities—people, products, industries, or concepts—that map directly to how AI systems structure knowledge. A strong cluster typically includes a pillar page that defines the entity, supported by subpages, FAQs, and related resources that cover its attributes, use cases, and connections to other entities.
This structure makes it easier for AI systems to understand relationships and context. For example, a cybersecurity brand that builds a cluster around “Zero Trust Security” would include subtopics like authentication methods, case studies, regulatory standards, and common FAQs. By interlinking these assets, you give both search engines and LLMs a clear semantic map, reinforcing your expertise in that domain.
The payoff is twofold: Your brand is more likely to be recognized as an authoritative source within that knowledge space, and your content becomes more extractable, meaning AI platforms can easily pull accurate, well-structured snippets into summaries, answers, and generative search results. In short, entity-based clusters teach AI systems exactly who you are and why you’re credible.
The shift toward AI-mediated information discovery represents the most significant change in search behavior since the advent of Google. Developing competency in LLM visibility tracking and optimization will ensure your brand remains discoverable and authoritative in the new landscape of AI-powered information consumption.
As these tools and methodologies continue to mature, the brands that invest early in understanding and optimizing for LLM visibility will have significant competitive advantages in the AI-first world that’s rapidly approaching. Dive deeper into the AI Visibility Index or go straight into exploring your LLM visibility.
Andrea Pretorian
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