# AI Viditeľnosť — Glossary of Checks 10 konceptov AI viditeľnosti v LLM modeloch --- ## K1 — Identity (Knowledge Level) **What is it:** Measures the level of knowledge AI models (ChatGPT, Claude, Gemini, Perplexity) have about your domain and brand. The result is expressed on a scale: none (AI knows nothing), confused (AI mixes up facts), partial (AI knows basic information), good (AI has accurate knowledge), and excellent (AI knows details, history, and brand context). It is tested through a series of questions about the company, products, and services. **Why it matters:** The level of AI knowledge about your brand directly determines the quality of answers users receive. If the level is 'none' or 'confused', AI may provide potential customers with incorrect information or ignore you entirely. According to GEO research (Princeton/IIT Delhi), up to 40% of users now start their searches through AI tools, making it crucial for AI models to have accurate and complete knowledge about your brand. **Real-world example:** When asked 'What is Amazon.com?', ChatGPT responds: 'Amazon is the world's largest e-commerce company, founded in 1994 by Jeff Bezos in Seattle.' This is an 'excellent' level — AI knows the name, focus, founding year, and city. Conversely, a small local business may get a 'none' level if AI has no information about it. ### Sources - [GEO: Generative Engine Optimization](https://arxiv.org/abs/2311.09735) — arXiv (IIT Delhi, Princeton) - [The Complete AI Visibility Guide for SEOs, Marketers, and Site Owners](https://ahrefs.com/blog/ai-visibility/) — Ahrefs - [How Claude uses the internet — Does Anthropic crawl data from the web?](https://privacy.claude.com/en/articles/8896518-does-anthropic-crawl-data-from-the-web-and-how-can-site-owners-block-the-crawler) — Anthropic --- ## K2 — Industry (Brand Mention Rate) **What is it:** Measures how frequently AI models mention your brand in responses to relevant queries. Expressed as a percentage of responses in which the brand appears out of the total number of relevant queries. For example, if out of 50 queries related to your industry AI mentions your brand in 15, the Brand Mention Rate is 30%. **Why it matters:** A brand mention in an AI response is the digital equivalent of a word-of-mouth recommendation. According to Ahrefs, brand mentions from trusted sources are the strongest factor for visibility in AI Overviews. A higher mention frequency builds awareness and trust, as users perceive AI responses as objective and trustworthy recommendations. **Real-world example:** Amazon appears in 70% of AI responses for electronics queries in the US — that's a high Brand Mention Rate. A small local electronics shop may appear in only 5% of responses. The goal is to systematically increase this ratio by optimizing content, building mentions on third-party sites, and strengthening topical authority. ### Sources - [7 ways to grow brand mentions, a key metric for AI Overviews visibility](https://searchengineland.com/7-ways-to-grow-brand-mentions-a-key-metric-for-ai-overviews-visibility-458600) — Search Engine Land - [Ahrefs Brand Radar: See ANY brand's AI visibility](https://ahrefs.com/brand-radar) — Ahrefs --- ## K3 — Competition (Link Presence) **What is it:** Checks whether AI models provide a direct link (URL) to your website within their responses. Some platforms like Perplexity and Google AI Overviews add citations with links by default, while ChatGPT and Claude may not always provide direct URLs. The metric evaluates link presence across all monitored AI platforms. **Why it matters:** A brand mention alone generates awareness, but only a direct link generates traffic and conversions. Without a URL, the user must manually search for your page, reducing the probability of a visit by 60-80%. Perplexity and Google AI Overviews add citations automatically, so it's important to be among the cited sources on these platforms. **Real-world example:** A Perplexity query for 'best pizzerias in New York' — the response contains a list of restaurants and citations with links at the end: '[1] yelp.com, [2] tripadvisor.com'. A website that's cited with a link has positive Link Presence. If your site is mentioned but without a link, the user likely won't click through. ### Sources - [Google Search — AI features and your website](https://developers.google.com/search/docs/appearance/ai-features) — Google - [Perplexity API — Getting Started](https://docs.perplexity.ai/guides/getting-started) — Perplexity - [How to boost your AI search visibility: 5 key factors](https://searchengineland.com/how-to-boost-your-ai-search-visibility-5-key-factors-464398) — Search Engine Land --- ## K4 — Recommendation (Confidence Score) **What is it:** Evaluates the level of certainty and conviction with which AI models talk about your brand. High confidence means AI responds with certainty and without hesitation ('Amazon is the world's largest online retailer'), while low confidence manifests through uncertain phrasing ('It seems that Amazon might be...' or 'I'm not sure, but...'). **Why it matters:** The AI's level of certainty directly affects user trust. If AI responds uncertainly, the user will seek further verification and may choose a competitor about whom AI speaks with greater confidence. The Confidence Score correlates with the quantity and quality of sources about your brand that the AI model processed during training. **Real-world example:** When asked 'Is Shopify a reliable e-commerce platform?', ChatGPT responds: 'Shopify is one of the largest and most reliable e-commerce platforms with over 4 million active stores.' This is high confidence — no hesitation, specific numbers. Conversely, the response 'Shopify is apparently some kind of e-commerce platform' indicates low confidence. ### Sources - [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774) — OpenAI - [The Claude Model Card](https://docs.anthropic.com/en/docs/about-claude/models) — Anthropic - [LLMO: 10 Ways to Work Your Brand Into AI Answers](https://ahrefs.com/blog/llm-optimization/) — Ahrefs --- ## K5 — Technology (Reputation Sentiment) **What is it:** Analyzes the sentiment (tone) of AI responses about your brand — whether positive, neutral, or negative. AI models synthesize information from numerous sources, and their responses reflect the overall sentiment that exists about your brand on the internet. The metric evaluates phrasing, descriptors, and the context in which AI mentions you. **Why it matters:** Negative sentiment in AI responses can damage brand reputation among thousands of users daily. Unlike a single negative review that a person can put into perspective, an AI model presents a synthesis of all available information as objective fact. That's why it's important to monitor and actively influence sentiment through quality PR and content marketing. **Real-world example:** When asked 'experiences with company XY', AI responds: 'Company XY has predominantly positive reviews; customers praise fast delivery and quality customer support.' This is positive sentiment. If AI responded: 'Company XY has many negative reviews; customers complain about long delivery times,' the sentiment would be negative and the brand should respond. ### Sources - [AI Brand Performance: See How AI Talks About Your Brand](https://www.semrush.com/ai-seo/brand-performance/) — Semrush - [How to measure and maximize visibility in AI search](https://searchengineland.com/how-to-measure-and-maximize-visibility-in-ai-search-462953) — Search Engine Land - [Gemini Apps Privacy Hub](https://support.google.com/gemini/answer/13594961) — Google AI --- ## K6 — Local awareness (Multi-model Consistency) **What is it:** Measures the consistency of information about your brand across different AI models (ChatGPT, Claude, Gemini, Perplexity). Each model has a different training dataset, architecture, and information sources, which can lead to contradictory responses. The metric compares key facts (name, focus, products, contact) across models and expresses the level of agreement. **Why it matters:** If one AI model says the right things about you and another states incorrect information, it creates confusion and distrust among users. Consistency across models strengthens brand credibility. Inconsistency often indicates a lack of structured data on the website or contradictory information across different sources that different models interpret differently. **Real-world example:** ChatGPT states that your company is based in New York, Claude claims it's in Chicago, and Gemini has no information about the headquarters at all. Multi-model Consistency is low in this case. The solution is to update Organization schema on the website, unify data on Google My Business, Wikipedia, and business registries so all AI models have access to the same facts. ### Sources - [LLM optimization in 2026: Tracking, visibility, and what's next for AI discovery](https://searchengineland.com/llm-optimization-tracking-visibility-ai-discovery-463860) — Search Engine Land - [Publishers and developers FAQ](https://help.openai.com/en/articles/12627856-publishers-and-developers-faq) — OpenAI - [15 of the Best AI Search Monitoring Tools](https://ahrefs.com/blog/aeo-tools-optimize-for-llms/) — Ahrefs --- ## K7 — Complete profile (Query Coverage) **What is it:** Measures coverage across different types of user queries — informational ('what is...'), navigational ('company XY website'), transactional ('buy product XY'), and comparative ('XY vs. competitor'). It expresses how many query types your brand appears in within AI responses. A higher value means AI knows you across different contexts and stages of the buying process. **Why it matters:** Users ask AI different types of questions at different stages of decision-making. If AI mentions you only for informational queries but not transactional ones, you're losing customers at the crucial purchasing stage. Complete query coverage ensures brand presence across the entire customer journey — from awareness to purchase. **Real-world example:** An accounting firm tracks 4 query types: informational ('what is a tax return'), navigational ('accounting firm New York'), transactional ('hire an accountant online'), comparative ('best accounting firms in New York'). AI mentions them for informational and navigational queries but not for transactional and comparative ones — query coverage is 50%. Commercial content and reviews need strengthening. ### Sources - [AI Visibility Toolkit: Boost Brand Visibility in AI Search](https://www.semrush.com/kb/1493-ai-visibility-toolkit) — Semrush - [How to optimize for AI search: 12 proven LLM visibility tactics](https://searchengineland.com/optimize-ai-search-llm-visibility-tactics-468106) — Search Engine Land - [10 Ways to Use Ahrefs' Brand Radar to Grow AI Visibility](https://ahrefs.com/blog/brand-radar-use-cases/) — Ahrefs --- ## D1 — Main topic (Factual Accuracy) **What is it:** Evaluates the correctness of facts that AI models state about your brand. It checks the accuracy of basic data: company name, founding year, headquarters, products, prices, contact information, number of employees, and other verifiable facts. Expressed as a percentage of correct facts out of the total number of verifiable AI claims about your brand. **Why it matters:** AI hallucinations are a real problem — models can confidently state incorrect facts about your company. A wrong price, nonexistent product, or incorrect contact can deter a potential customer or damage your reputation. Regular monitoring of factual accuracy enables you to identify and correct wrong information by updating structured data and publicly available sources. **Real-world example:** AI claims about your company: 'Company XY was founded in 2005, is headquartered in Chicago, and offers 3 main products.' In reality, it was founded in 2008, is based in New York, and offers 5 products. Factual Accuracy is therefore 0 out of 3 key facts (0%). The solution is to update information on Wikipedia, in business registries, on LinkedIn, and deploy correct Organization schema. ### Sources - [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774) — OpenAI - [The Claude Model Card](https://docs.anthropic.com/en/docs/about-claude/models) — Anthropic - [Free AI Brand Visibility Tool: Check Your AI Search Presence](https://www.semrush.com/free-tools/ai-search-visibility-checker/) — Semrush --- ## D2 — Category (Competitive Position) **What is it:** Determines your brand's position compared to competitors in AI responses. It measures relative share of mentions (share of voice), ranking in lists and recommendations, and overall sentiment compared to competing brands. If AI lists your competitor first and you third when asked 'best X in [region]', your competitive position is lower. **Why it matters:** Absolute mention numbers have limited value without competitive context. If you have 50 mentions but your main competitor has 200, you're significantly behind. Benchmarking against competitors allows you to identify specific areas where they outperform you and focus optimization precisely where it has the greatest impact on acquiring customers. **Real-world example:** Three travel agencies: when asked 'best travel agency in the US', AI responds: '1. Expedia, 2. Booking.com, 3. Kayak.' Kayak has competitive position 3 out of 3. After a campaign focused on building mentions, reviews, and content authority, it moves to 2nd place after 6 months. The long-term goal is to occupy position 1. ### Sources - [The AI Visibility Index: Here's who's winning AI search](https://searchengineland.com/the-ai-visibility-index-heres-whos-winning-ai-search-463319) — Search Engine Land - [Ahrefs Brand Radar: See ANY brand's AI visibility](https://ahrefs.com/brand-radar) — Ahrefs - [Semrush AI Visibility — Win Every Search](https://www.semrush.com/ai-seo/overview/) — Semrush --- ## D3 — Keyword combination (Update Freshness) **What is it:** Measures how current the information AI models state about your brand is. It checks whether AI knows your latest products, services, changes in offerings, new locations, or current pricing policy. Outdated information indicates that the AI model lacks access to fresh data or that your current information isn't sufficiently visible on the web. **Why it matters:** AI models have training data with a certain delay (knowledge cutoff). If you recently changed your offering, opened a new location, or launched a new product, AI may not know about it. Regular content updates on the website, in structured data, and on authoritative sources (Wikipedia, LinkedIn, Google My Business) help AI models maintain current information. **Real-world example:** A restaurant expanded its menu with vegetarian dishes in 2025 and opened a new location downtown. However, AI still references only the old menu and the single original location. Update Freshness is low. Solution: update the website, add LocalBusiness schema for the new location, publish a press release, and update the Google My Business profile. Within 2-4 months, AI models will register the changes. ### Sources - [Google Search — AI features and your website](https://developers.google.com/search/docs/appearance/ai-features) — Google - [A 90-day SEO playbook for AI-driven search visibility](https://searchengineland.com/a-90-day-seo-playbook-for-ai-driven-search-visibility-466751) — Search Engine Land - [LLM Visibility: What It Is and How to Optimize for It](https://ahrefs.com/blog/llm-visibility/) — Ahrefs ---