AI Visibility & GEO Glossary

Why AI Agent matters: AI agents increasingly make purchasing decisions, making brand visibility critical for conversion.

Example usage: We optimise for AI agent discovery to ensure our clients are recommended in automated workflows.

Related terms: Conversational AI, LLM, AIRO

Why AI Attribution matters: Proper attribution builds trust and drives referral traffic from AI systems.

Example usage: We structure content to maximise AI attribution through clear authorship signals.

Related terms: Model Citation, E-E-A-T, Authority Signals

Why AI Crawler matters: Allowing AI crawlers access ensures your content can be discovered and cited.

Example usage: We configure robots.txt to explicitly allow GPTBot, Claude-Web, and other AI crawlers.

Related terms: Robots.txt, ai.txt, Common Crawl

Why AI Overview matters: AI overviews are becoming the primary way users consume search results. Being cited as a source drives brand authority.

Example usage: Our client appears in AI overviews for 15 high-intent queries in their category.

Related terms: Zero-Click Search, Model Citation, GEO

Why AI Recommendation Optimisation (AIRO) matters: AI tools increasingly guide buyer decisions. AIRO ensures your brand is accurately understood and favoured in AI outputs.

Example usage: We run an AIRO audit to make sure assistants cite the correct product positioning.

Related terms: Generative Engine Optimisation, Authority Signals, Entity

Why AI Safety matters: AI safety measures affect what content AI systems will cite and recommend.

Example usage: We ensure content meets AI safety guidelines to avoid being filtered from responses.

Related terms: Hallucination, Prompt Injection, Grounding

Why AI Visibility matters: Strong AI visibility ensures your brand is represented correctly before a user ever visits your site.

Example usage: We track AI visibility by measuring citations, sentiment, and brand accuracy.

Related terms: AIRO, Model Citation, Authority Signals

Why AI Visibility Score matters: Tracking AI visibility score shows whether optimisation efforts are working.

Example usage: Our client's AI visibility score increased 40% after implementing GEO recommendations.

Related terms: Share of Model, AI Visibility, Model Citation

Why AI-First Content matters: AI-first content prioritises clarity, structure, and entity relationships over keyword density.

Example usage: We create AI-first content that maximises retrieval and citation potential.

Related terms: GEO, Structured Data, Entity

Why ai.txt matters: ai.txt clarifies permissions for AI training or summarisation.

Example usage: We publish ai.txt to define AI usage rules for your brand assets.

Related terms: Robots.txt, AI Crawler, Training Data

Why Answer Engine Optimisation (AEO) matters: AEO improves how AI systems surface your brand in direct answers and recommendations.

Example usage: We structure FAQs and product pages to support AEO across AI assistants.

Related terms: GEO, AIRO, Model Citation

Why Authority Signals matters: Strong authority signals increase trust and recommendation likelihood.

Example usage: We add author bios and awards to strengthen authority signals.

Related terms: E-E-A-T, Model Citation, Schema Markup

Why Brand Consistency matters: Inconsistent information confuses AI systems and reduces recommendation confidence.

Example usage: We synchronise brand data across 50+ platforms to improve AI understanding.

Related terms: Digital Footprint, NAP Consistency, Entity

Why Brand Salience matters: In AI recommendations, brand salience determines whether your brand is mentioned first or at all.

Example usage: We build brand salience by ensuring consistent entity mentions across authoritative sources.

Related terms: Entity, Authority Signals, AIRO

Why Canonical URL matters: Canonical URLs consolidate signals so AI systems see one authoritative source.

Example usage: We set canonical URLs on glossary entries to avoid split relevance.

Related terms: Crawl Budget, Structured Data, Duplicate Content

Why Chatbot matters: Chatbots powered by AI increasingly influence product recommendations and brand perception.

Example usage: We monitor how AI chatbots describe and recommend our client's services.

Related terms: Conversational AI, LLM, AI Agent

Why ChatGPT matters: ChatGPT is one of the most widely used AI assistants, making visibility there crucial.

Example usage: We test and optimise brand representation specifically for ChatGPT responses.

Related terms: GPT, LLM, Conversational AI

Why Citation Rate matters: Higher citation rates indicate stronger authority and trust signals.

Example usage: We track citation rate across ChatGPT, Perplexity, and Gemini to measure performance.

Related terms: Model Citation, AI Visibility Score, Authority Signals

Why Claude matters: Claude is increasingly used in enterprise and technical contexts where accuracy matters.

Example usage: We test brand representation in Claude to ensure accurate technical descriptions.

Related terms: LLM, Conversational AI, AI Safety

Why Common Crawl matters: Content in Common Crawl influences AI training data and model knowledge.

Example usage: We ensure key pages are accessible to Common Crawl for inclusion in AI training sets.

Related terms: Training Data, AI Crawler, Crawl Budget

Why Content Atomisation matters: Atomised content is more likely to be accurately quoted and cited by AI systems.

Example usage: We atomise key facts and claims to improve RAG retrieval accuracy.

Related terms: RAG, Content Cluster, Structured Data

Why Content Cluster matters: Content clusters signal topical depth and authority to AI systems.

Example usage: We create content clusters around key service categories to dominate AI recommendations.

Related terms: Topical Authority, Pillar Content, Internal Linking

Why Context Window matters: Content that fits within the context window is more likely to be fully processed and accurately cited.

Example usage: We structure pages to deliver key information within typical context window limits.

Related terms: Token, LLM, RAG

Why Conversational AI matters: Conversational AI is a primary interface for brand discovery and recommendations.

Example usage: We test brand representation across major conversational AI platforms.

Related terms: Chatbot, Voice Search, AI Agent

Why Crawl Budget matters: Optimising crawl budget ensures AI systems spend more time on key pages.

Example usage: We reduce duplicate pages to preserve crawl budget for priority content.

Related terms: Robots.txt, Sitemap, AI Crawler

Why Digital Footprint matters: AI models aggregate your digital footprint to form their understanding of your brand.

Example usage: We audit the digital footprint to ensure consistent brand signals across all platforms.

Related terms: Brand Consistency, Entity, Knowledge Graph

Why Duplicate Content matters: Duplicate content confuses AI systems about which source is authoritative.

Example usage: We consolidate duplicate content to strengthen canonical authority signals.

Related terms: Canonical URL, Crawl Budget, Authority Signals

Why E-E-A-T matters: AI systems and search engines evaluate E-E-A-T for quality and reliability.

Example usage: We align expert author profiles to boost E-E-A-T confidence.

Related terms: Authority Signals, Model Citation, Topical Authority

Why Embeddings matters: Clean embeddings improve retrieval accuracy in AI systems and RAG pipelines.

Example usage: We shape content structure so embeddings capture the right context.

Related terms: Vector Index, RAG, Semantic Search

Why Entity matters: Clear entities help AI models understand brand relationships and attributes.

Example usage: We reinforce the brand entity with consistent naming across the web.

Related terms: Knowledge Graph, Named Entity Recognition, Schema Markup

Why Featured Snippet matters: Featured snippets capture attention and often feed into AI-generated responses.

Example usage: We structure FAQ content to capture featured snippets for key queries.

Related terms: Zero-Click Search, Structured Data, Schema Markup

Why Fine-tuning matters: Fine-tuned models may have different biases and preferences that affect brand visibility.

Example usage: We monitor how fine-tuned enterprise AI systems represent our clients.

Related terms: Training Data, LLM, Model Weights

Why Freshness Signals matters: AI assistants prefer up-to-date sources, especially in fast-moving topics.

Example usage: We update sitemap metadata to improve freshness signals.

Related terms: Sitemap, Last-Modified Header, RAG

Why Generative Engine Optimisation (GEO) matters: GEO helps your brand appear in AI responses across ChatGPT, Gemini, and Perplexity.

Example usage: Our GEO roadmap aligns content to AI intent and entity relationships.

Related terms: RAG, Structured Data, Model Citation

Why Google Gemini matters: Gemini influences AI Overviews in Google Search, affecting billions of queries.

Example usage: We optimise for Gemini to improve visibility in Google's AI-powered search features.

Related terms: AI Overview, Multimodal AI, LLM

Why GPT (Generative Pre-trained Transformer) matters: GPT models are among the most widely used AI systems for search and recommendations.

Example usage: We optimise content structure for GPT-based assistants and search tools.

Related terms: LLM, Transformer, ChatGPT

Why Grounding matters: Grounded AI responses are more accurate and trustworthy, benefiting brands with strong source content.

Example usage: We provide clear, verifiable claims that help AI systems ground their responses.

Related terms: RAG, Hallucination, Model Citation

Why Hallucination matters: Hallucinations can spread misinformation about your brand. Strong authority signals reduce this risk.

Example usage: We monitor AI outputs to catch and correct hallucinations about our client's products.

Related terms: Authority Signals, E-E-A-T, Grounding

Why Industry Vertical matters: AI systems may have different knowledge depths across industry verticals.

Example usage: We assess AI visibility within specific industry verticals to benchmark against competitors.

Related terms: Topical Authority, Share of Model, AI Visibility

Why Inference matters: Understanding inference helps optimise content for how AI systems actually process queries.

Example usage: We test inference outputs across multiple AI models to ensure consistent brand representation.

Related terms: LLM, Prompt Engineering, Context Window

Why Internal Linking matters: Strategic internal linking helps AI systems understand content relationships and hierarchy.

Example usage: We build internal link structures that reinforce topical clusters for AI crawlers.

Related terms: Content Cluster, Crawl Budget, Topical Authority

Why Knowledge Graph matters: AI systems rely on relationship signals to connect your brand to trusted sources.

Example usage: We connect product entities to authoritative references in the graph.

Related terms: Entity, Structured Data, Authority Signals

Why Large Language Model (LLM) matters: LLMs drive AI assistants that shape brand discovery and perception.

Example usage: We test how each LLM describes your product category.

Related terms: GPT, Transformer, Training Data

Why Last-Modified Header matters: Accurate timestamps reinforce freshness signals for AI retrieval.

Example usage: We ensure Last-Modified headers reflect real content updates.

Related terms: Freshness Signals, Sitemap, Crawl Budget

Why Local SEO matters: AI assistants increasingly answer local queries, making local optimisation essential.

Example usage: We optimise Google Business Profile and local signals for AI-powered local search.

Related terms: NAP Consistency, Schema Markup, Voice Search

Why Microsoft Copilot matters: Copilot reaches enterprise users and influences B2B discovery and recommendations.

Example usage: We ensure brand visibility in Microsoft Copilot for enterprise market reach.

Related terms: Conversational AI, AI Agent, LLM

Why Model Citation matters: Citations drive trust and traffic from AI-generated answers.

Example usage: We optimise for model citation by making references easy to parse.

Related terms: RAG, Structured Data, Authority Signals

Why Multimodal AI matters: Multimodal AI expands brand visibility opportunities beyond text to visual and audio content.

Example usage: We optimise product images with alt text and metadata for multimodal AI discovery.

Related terms: LLM, Visual Search, Voice Search

Why Named Entity Recognition (NER) matters: Clear NER signals help AI systems accurately identify and describe your brand.

Example usage: We ensure consistent entity naming to improve NER accuracy across AI systems.

Related terms: Entity, Knowledge Graph, Natural Language Processing

Why NAP Consistency matters: Consistent NAP data helps AI systems verify business legitimacy and location.

Example usage: We audit NAP consistency across directories to strengthen local AI visibility.

Related terms: Brand Consistency, Digital Footprint, Local SEO

Why Natural Language Processing (NLP) matters: NLP powers how AI systems interpret your content and represent your brand.

Example usage: We structure content for optimal NLP parsing and entity extraction.

Related terms: LLM, Named Entity Recognition, Semantic Search

Why Perplexity AI matters: Perplexity explicitly cites sources, making it a high-value platform for brand visibility.

Example usage: Our client appears as a cited source in 80% of relevant Perplexity queries.

Related terms: RAG, Model Citation, AI Overview

Why Pillar Content matters: Pillar content attracts AI citations and serves as a canonical source for a topic.

Example usage: Our pillar page on AI visibility ranks and gets cited across multiple AI platforms.

Related terms: Content Cluster, Topical Authority, Canonical URL

Why Prompt Engineering matters: Understanding how users prompt AI helps optimise content for likely queries.

Example usage: We analyse common prompts in your category to align content with user intent.

Related terms: LLM, Search Intent, Model Citation

Why Prompt Injection matters: Understanding prompt injection helps protect brand content from manipulation.

Example usage: We audit content for vulnerability to prompt injection attacks.

Related terms: AI Safety, LLM, System Prompt

Why Query Understanding matters: Better query understanding means AI can match your content to relevant user needs.

Example usage: We structure content to align with how AI systems parse and understand queries.

Related terms: Search Intent, Semantic Search, Natural Language Processing

Why Recommendation Frequency matters: Higher recommendation frequency directly correlates with AI-driven leads and conversions.

Example usage: We increased recommendation frequency by 60% through targeted authority building.

Related terms: Share of Model, AI Visibility Score, AIRO

Why Retrieval-Augmented Generation (RAG) matters: If your content is not retrievable, it is unlikely to be cited or recommended.

Example usage: We structure documentation so RAG systems can pull accurate snippets.

Related terms: Vector Index, Freshness Signals, Knowledge Graph

Why Robots.txt matters: Proper crawling ensures AI systems can find and trust your content.

Example usage: We allow AI crawlers to access documentation while blocking staging pages.

Related terms: Crawl Budget, ai.txt, AI Crawler

Why Schema Markup matters: Schema helps AI and search engines interpret content accurately.

Example usage: We use schema markup to specify authorship and product details.

Related terms: Structured Data, Entity, Model Citation

Why Search Intent matters: AI systems match content to search intent, making intent optimisation crucial for visibility.

Example usage: We map content to informational, navigational, and transactional intent patterns.

Related terms: Semantic Search, Query Understanding, GEO

Why Semantic Search matters: AI-powered search uses semantic understanding to match queries with relevant content.

Example usage: We optimise for semantic search by building topical depth and clear entity relationships.

Related terms: Embeddings, Vector Index, Natural Language Processing

Why Sentiment Analysis matters: AI systems consider sentiment when making recommendations, favouring positively perceived brands.

Example usage: We monitor sentiment in AI responses about our clients to identify reputation issues.

Related terms: Natural Language Processing, Brand Salience, AI Visibility

Why Sentiment Score matters: Negative sentiment in AI responses can damage brand perception at scale.

Example usage: We monitor sentiment scores to catch and address reputation issues in AI outputs.

Related terms: Sentiment Analysis, AI Visibility, Brand Salience

Why Share of Model matters: Share of Model reveals whether your AI visibility is gaining ground or slipping behind rivals.

Example usage: We benchmark share of model to measure competitive movement across AI assistants.

Related terms: AI Visibility, Model Citation, Authority Signals

Why Sitemap matters: Sitemaps help AI crawlers find updated pages quickly.

Example usage: We submit an updated sitemap to surface new glossary entries.

Related terms: Freshness Signals, Robots.txt, Crawl Budget

Why Structured Data (JSON-LD) matters: Structured data makes entities and attributes unambiguous to AI systems.

Example usage: We add JSON-LD markup for products, authors, and organisation data.

Related terms: Schema Markup, Entity, Knowledge Graph

Why System Prompt matters: System prompts influence which sources and brands AI systems prioritise in responses.

Example usage: We understand system prompt patterns to optimise content for AI assistant behaviour.

Related terms: Prompt Engineering, LLM, AI Agent

Why Temperature (AI) matters: Higher temperature settings increase variability in how your brand might be described.

Example usage: We test brand mentions across different temperature settings to ensure consistency.

Related terms: LLM, Inference, Hallucination

Why Thin Content matters: AI systems deprioritise thin content, reducing visibility and citation potential.

Example usage: We enhance thin pages with comprehensive, authoritative information.

Related terms: Topical Authority, E-E-A-T, Content Cluster

Why Token matters: Token efficiency affects how much of your content AI systems can process and remember.

Example usage: We optimise content density to convey maximum value within token limits.

Related terms: Context Window, LLM, Embeddings

Why Topical Authority matters: AI systems prioritise topically authoritative sources when generating recommendations.

Example usage: We build topical authority through comprehensive content clusters.

Related terms: E-E-A-T, Authority Signals, Content Cluster

Why Training Data matters: Content in training data shapes how AI models perceive and describe your brand long-term.

Example usage: We ensure authoritative content is available for inclusion in future training data.

Related terms: LLM, Knowledge Graph, Common Crawl

Why Transformer matters: Understanding transformer architecture helps explain how AI models process and prioritise content.

Example usage: Transformer-based models use attention mechanisms to weigh the importance of different content sections.

Related terms: LLM, GPT, Embeddings

Why Vector Index matters: Vector indexes control what RAG systems can retrieve and cite.

Example usage: We engineer pages so they embed cleanly into vector indexes.

Related terms: RAG, Embeddings, Semantic Search

Why Visual Search matters: Visual search expands discovery opportunities for products and brands with strong visual identity.

Example usage: We optimise product images with descriptive filenames and alt text for visual search.

Related terms: Multimodal AI, Schema Markup, Image SEO

Why Voice Search matters: Voice search queries are conversational and often result in single-answer responses.

Example usage: We optimise for voice search by targeting question-based queries with concise answers.

Related terms: Conversational AI, Featured Snippet, Zero-Click Search

Why Zero-Click Search matters: With AI overviews and featured snippets, over 60% of searches now end without a click. Being cited in these answers is critical for visibility.

Example usage: We optimise content structure to win zero-click positions in AI overviews.

Related terms: AI Overview, Featured Snippet, GEO