Glossary

The GEO & AI Search Glossary

98 terms every marketer needs to understand how brands win visibility in ChatGPT, Perplexity, Claude, Gemini and Google AI — from Generative Engine Optimization to llms.txt.

GEO Fundamentals

AI Citation

An AI citation is a source link that an AI engine attaches to its generated answer, attributing a claim to a specific web page. Citations appear as numbered references or inline links in engines like Perplexity, ChatGPT Search, and Google AI Overviews. Earning citations drives referral traffic and signals that engines trust the cited domain.

AI Recommendation

An AI recommendation is an explicit endorsement of a brand, product, or service in an AI-generated answer, where the engine advises the user to choose or consider it rather than merely mentioning it. Recommendations carry direct purchase influence, since users increasingly treat AI assistants as trusted advisors during buying decisions.

AI Search

AI search refers to search experiences where a large language model generates a direct, synthesized answer instead of returning a list of links. Examples include ChatGPT Search, Perplexity, Google AI Overviews and AI Mode, Microsoft Copilot, and Gemini. AI search typically combines model knowledge with live web retrieval and cites a small set of sources.

AI Search Ranking Factors

AI search ranking factors are the signals that determine which brands and sources appear in AI-generated answers. Key factors include third-party corroboration across trusted sources, content extractability and factual density, crawler accessibility, freshness, brand-category association strength in training data, and authority signals such as reviews, original research, and consistent expert coverage.

AI Visibility

AI visibility is the degree to which a brand appears in answers generated by AI engines such as ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. It is typically measured by tracking a fixed set of relevant prompts and recording how often the brand is mentioned, cited, or recommended across each engine over time.

Answer Engine Optimization (AEO)

Answer Engine Optimization (AEO) is the practice of structuring content so search systems can extract and present it as a direct answer. Originally focused on featured snippets and voice assistants, AEO now covers AI answer engines like ChatGPT and Perplexity, emphasizing concise, question-led formatting that machines can quote verbatim.

Brand Mention (in AI Answers)

A brand mention in AI answers is any instance where an AI engine names a brand in its generated response, whether as a recommendation, comparison entry, or passing reference. Mentions are the core unit of AI visibility measurement: tracking how often, where, and in what sentiment a brand is mentioned reveals its standing in AI search.

Citability

Citability is the degree to which a web page's content can be easily retrieved, extracted, and cited by AI engines. Highly citable pages contain self-contained answer passages, explicit facts and statistics, clear structure, and current information, making them preferred sources when engines ground their generated answers in web content.

Citation Rate

Citation rate is the percentage of AI-generated answers that cite a specific domain as a source, measured across a tracked prompt set. It captures how often AI engines like Perplexity, ChatGPT Search, and Google AI Overviews retrieve and attribute a website's content, making it the key metric for a site's retrievability and trustworthiness.

Content Gap Analysis

Content gap analysis in GEO is the process of identifying prompts where AI engines mention or cite competitors but not your brand, then tracing each gap to its cause, missing content, weak third-party coverage, or poor citability, and producing a prioritized list of content to create or improve.

Conversational Query

A conversational query is a search expressed in natural language, often as a full question or multi-sentence request, rather than as keywords. Typical of AI assistants and voice search, conversational queries carry richer context, constraints, and intent, and frequently occur in multi-turn dialogues where each question builds on previous answers.

Generative Engine Optimization (GEO)

Generative Engine Optimization (GEO) is the practice of increasing how often a brand is mentioned, cited, and recommended in answers generated by AI engines such as ChatGPT, Perplexity, Gemini, and Google AI Overviews. It combines content structuring, authority building, and continuous measurement so large language models select your brand when answering relevant questions.

GEO Audit

A GEO audit is a structured assessment of how visible and citable a brand is in AI search. It measures current mentions and citations across AI engines, checks technical access for AI crawlers, scores content citability, maps competitor presence, and produces a prioritized action plan for improving visibility in generated answers.

GEO Strategy

A GEO strategy is a structured plan for growing a brand's visibility in AI-generated answers. It defines target prompts and engines, establishes measurement baselines, prioritizes content and authority initiatives to win specific answer placements, and sets a continuous loop of monitoring, diagnosis, and optimization across platforms like ChatGPT, Perplexity, and Gemini.

Mention Position

Mention position is the order in which a brand appears among all brands named in an AI-generated answer. First position typically signals the engine's primary recommendation and captures the most user attention. Tracking average position across prompts shows not just whether a brand appears in AI answers, but how strongly it is endorsed.

Mention Rate

Mention rate is the percentage of AI-generated answers that name a specific brand, measured across a tracked set of prompts. If a brand is mentioned in 30 of 100 answers collected for its tracked prompts, its mention rate is 30 percent. It is the foundational metric for quantifying brand presence in AI search.

Prompt Tracking

Prompt tracking is the practice of repeatedly querying AI engines with a fixed set of prompts that mirror real customer questions, then recording which brands are mentioned, cited, and recommended in each answer. It is the AI-search equivalent of rank tracking, providing the raw data behind visibility scores and competitive analysis.

Share of Voice (AI Search)

Share of voice in AI search is the percentage of brand mentions a company captures out of all brand mentions in AI-generated answers for a defined set of prompts. If AI engines produce 200 brand mentions across your tracked prompts and 50 name your brand, your AI share of voice is 25 percent.

Visibility Score

A visibility score is a composite index, usually 0 to 100, that summarizes how prominently a brand appears in AI-generated answers. It typically combines mention frequency, mention position, and citation presence across a tracked prompt set and multiple AI engines, giving teams one trendable number for overall AI search performance.

Zero-Click Search

A zero-click search is a query that ends without the user clicking through to any website, because the answer was delivered directly on the results page or in an AI-generated response. With AI Overviews and chat assistants answering questions in place, the majority of searches now resolve without a website visit.

AI Platforms

ChatGPT Search

ChatGPT Search is OpenAI's web-connected search experience inside ChatGPT. It combines a Bing-derived index with OpenAI's own crawling to retrieve live web pages, then generates a conversational answer with inline source links. For brands, appearing in its cited sources drives visibility and referral traffic from hundreds of millions of ChatGPT users.

Claude (Anthropic)

Claude is Anthropic's family of AI assistants, widely used for professional work, coding, and research. Claude answers from trained knowledge by default and can run web searches that produce cited sources. Its enterprise and developer-heavy audience makes Claude mentions especially valuable for B2B and technical brands.

DeepSeek

DeepSeek is a Chinese AI lab known for open-weight models, including the R1 reasoning series, offered at very low cost. Its chat app gained massive global adoption in 2025, and its open models power many third-party tools. Brand representation in DeepSeek's training data shapes answers across that whole ecosystem.

Google AI Mode

Google AI Mode is a fully conversational search experience within Google Search, powered by Gemini. Instead of a summary above blue links, it delivers a complete AI answer with follow-up questions and cited sources. It uses query fan-out, running many background searches per question, which changes how content gets discovered and cited.

Google AI Overviews

Google AI Overviews are AI-generated summaries that appear above organic results on many Google searches. Powered by Gemini models, they synthesize information from multiple web pages and display source links alongside the answer. They reduce clicks to traditional listings, making inclusion in the overview itself a key visibility goal for marketers.

Google Gemini

Google Gemini is Google's flagship AI model family and consumer assistant. The Gemini app answers questions conversationally with optional Google Search grounding, and the same models power AI Overviews and AI Mode in Search. Visibility in Gemini therefore influences the largest combined AI answer footprint on the web.

Grok (xAI)

Grok is the AI assistant from xAI, integrated directly into X (formerly Twitter) and available as a standalone app. Its defining trait is real-time access to X posts alongside web search, making it unusually responsive to live conversation and news. Brand sentiment on X can flow directly into Grok's answers.

Microsoft Copilot

Microsoft Copilot is Microsoft's AI assistant, embedded in Windows, Edge, Bing, and Microsoft 365. For web questions it retrieves from the Bing index and generates answers with cited source links. Its deep distribution inside workplace software makes it a major AI answer surface for B2B brand visibility.

Mistral Le Chat

Le Chat is the AI assistant from Mistral AI, the leading European AI lab. It offers fast conversational answers with web search and cited sources, and has strong adoption in France and across European enterprises favoring EU-based AI. For brands targeting European markets, Le Chat is an increasingly relevant answer surface.

Perplexity AI

Perplexity AI is an answer engine that performs live web retrieval for every query and generates responses with numbered citations linking to each source. Because every claim is attributed, it is the most transparent major AI search platform, and a high-signal target for marketers measuring which content earns AI citations.

How LLMs Work

AI Agent

An AI agent is a system where a language model autonomously plans and executes multi-step tasks using tools, searching the web, browsing pages, calling APIs, writing files, iterating until a goal is met. Agents increasingly perform research and purchasing tasks for users, making them a new audience that evaluates brands without human eyes on your website.

AI Hallucination

An AI hallucination is a confident but false statement generated by a language model, invented facts, wrong attributions, or nonexistent details, produced because models predict plausible text rather than verify truth. For brands, hallucinations matter doubly: AI can state false things about your company, and false claims erode user trust in uncited answers.

Context Window

The context window is the maximum amount of text, measured in tokens, a language model can consider at once: the system prompt, conversation history, retrieved documents, and its own output. It limits how many sources an AI engine can read per answer, making the competition to be among those few sources intense.

Embeddings

Embeddings are numerical vector representations of text that capture meaning, so semantically similar passages sit close together in mathematical space. AI search systems use embeddings to match questions with relevant content by meaning rather than keywords. They determine whether your page is even considered when an AI retrieves sources for an answer.

Fine-Tuning

Fine-tuning is additional training applied to a pre-trained language model on a smaller, specialized dataset to adapt its behavior, style, or domain knowledge. Companies fine-tune models for support bots and vertical assistants. For marketers, it explains why AI products built on the same base model can describe brands differently.

Grounding (AI)

Grounding is the practice of anchoring a language model's answer in verifiable external sources, search results, documents, or databases, retrieved at answer time, rather than relying on trained memory alone. Grounded answers cite sources and stay current, which is why grounding determines whether a brand's live web content can appear in AI responses.

Inference (LLM)

Inference is the runtime process where a trained language model generates output, predicting tokens one by one in response to a prompt. Every AI answer users see is an inference run. Its cost and latency constraints explain why engines retrieve few sources, summarize aggressively, and cache answers, all of which shape brand visibility.

Knowledge Cutoff

A knowledge cutoff is the date after which a language model has no trained knowledge, the point its training data ends. Without web search, a model cannot know about products, rebrands, or news after its cutoff. This explains why AI chatbots describe outdated versions of brands and why retrieval features matter.

Large Language Model (LLM)

A large language model is an AI system trained on massive text datasets to predict and generate language. LLMs like GPT, Claude, and Gemini power AI chatbots and answer engines. Because they answer questions by synthesizing learned patterns, what they say about a brand reflects how that brand appears across their training data.

Query Fan-Out

Query fan-out is a technique where an AI search system decomposes one user question into multiple parallel sub-queries, retrieves results for each, and synthesizes everything into a single answer. Used prominently by Google AI Mode, it means pages can earn citations by answering narrow sub-questions, not just the visible query.

Reasoning Model

A reasoning model is a language model trained to work through problems step by step before answering, spending extra inference-time computation on planning, verification, and multi-step research. Models like OpenAI's o-series, DeepSeek R1, and reasoning modes in Claude and Gemini power deep research features that read and cite many sources.

Reranking

Reranking is a second-pass scoring step in retrieval pipelines where a specialized model re-orders initially retrieved documents by true relevance to the query before the best few are passed to the language model. It is the final filter deciding which sources an AI answer actually uses and cites.

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation is an AI architecture where a language model retrieves relevant documents, typically via web or database search, before generating its answer, grounding the response in fetched content. RAG powers AI search engines like Perplexity and ChatGPT Search, and it is the mechanism through which web pages earn citations in AI answers.

Semantic Search

Semantic search retrieves information by meaning rather than keyword matching, using embeddings to find content conceptually related to a query even when wording differs entirely. It underpins how AI engines select sources for answers, making intent coverage and passage clarity more important than exact-match keywords for brand visibility.

System Prompt

A system prompt is the hidden instruction set a platform gives its language model before any user input, defining behavior, tone, safety rules, and how to use tools like web search and citations. It silently shapes every AI answer, including whether and how brands get recommended, compared, and sourced.

Temperature (LLM)

Temperature is a setting that controls how random a language model's output is during generation: low values produce consistent, predictable answers, higher values produce varied, creative ones. It is a key reason the same prompt about a product category can name different brands on different runs of the same AI.

Token (LLM)

A token is the basic unit of text a language model processes, typically a word fragment of about four characters or three-quarters of a word in English. Models read, generate, and price everything in tokens. Token limits shape how much of a web page an AI can ingest when composing an answer.

Training Data

Training data is the text corpus, web pages, books, code, forums, and licensed content, used to teach a language model during training. It determines what the model knows and believes, including how it describes brands. A brand's presence in training data shapes AI answers for years, since models retrain infrequently.

AI Crawlers & Technical

AI Crawler

An AI crawler is an automated bot operated by an AI company that fetches web pages to collect training data for language models or to retrieve fresh content for AI search answers. Examples include GPTBot, ClaudeBot and PerplexityBot. Each identifies itself with a user agent string and can be allowed or blocked via robots.txt.

AI Referral Traffic

AI referral traffic consists of human visitors who arrive at a website by clicking a link inside an AI platform such as ChatGPT, Perplexity or Gemini. It is distinct from AI bot crawls, which are automated. Standard analytics frequently misattributes these visits as direct traffic, hiding the true impact of AI visibility.

AI Traffic Attribution

AI traffic attribution is the practice of correctly identifying website visits and conversions that originate from AI platforms like ChatGPT, Perplexity and Gemini. Because AI clients often strip referrer data, these visits frequently appear as direct traffic, so attribution requires referrer rules, server-side detection and landing-page analysis.

Bot Traffic

Bot traffic is automated, non-human activity on a website, ranging from search and AI crawlers to scrapers and malicious bots. In AI search measurement, the relevant slice is crawls by AI bots like GPTBot or PerplexityBot, which signal that AI platforms are reading your content but are invisible to client-side analytics.

CCBot (Common Crawl)

CCBot is the crawler of Common Crawl, a nonprofit that publishes massive free archives of web content. Its corpus is one of the most widely used sources for training large language models. Blocking CCBot via robots.txt keeps your content out of future Common Crawl snapshots used by many AI labs.

ChatGPT-User

ChatGPT-User is the user agent OpenAI sends when ChatGPT fetches a web page live during a conversation, typically when a user asks it to browse, summarize or verify something. It acts on direct user requests rather than crawling systematically, making it a real-time fetcher distinct from GPTBot and OAI-SearchBot.

ClaudeBot

ClaudeBot is Anthropic's web crawler that collects publicly available content used to train and improve the Claude family of AI models. It identifies itself via the ClaudeBot user agent and honors robots.txt directives, so site owners can allow or restrict it. Allowing it helps Claude models learn about your brand and content.

Crawl Budget

Crawl budget is the number of pages a crawler will fetch from a site within a given period, shaped by the site's server capacity and the crawler's assessment of its value. Originally a Google Search concept, it now extends to AI crawlers, which typically fetch fewer pages and prioritize fresh, authoritative content.

Google-Extended

Google-Extended is a robots.txt control token that lets site owners opt out of having their content used to train Google's Gemini models. It is not a separate crawler; Googlebot still crawls normally. Blocking Google-Extended does not remove a site from Google Search or AI Overviews, which follow standard search indexing.

GPTBot

GPTBot is OpenAI's web crawler that collects publicly available content to train and improve its language models, including the GPT series. It identifies itself with the GPTBot user agent and respects robots.txt, so site owners can block it. Blocking GPTBot affects model training only, not ChatGPT search citations.

JavaScript Rendering

JavaScript rendering refers to content being generated in the browser by JavaScript after the initial HTML loads, as in single-page applications. Most AI crawlers do not execute JavaScript, so client-side-rendered content is invisible to them. Server-side rendering or pre-rendering is essential for AI search visibility.

JSON-LD

JSON-LD (JavaScript Object Notation for Linked Data) is the recommended format for embedding structured data in web pages. It places Schema.org markup in a script tag as a clean JSON block, separate from visible HTML, making it the easiest format to implement, validate and maintain, and the one Google recommends.

llms.txt

llms.txt is a proposed convention where a site publishes a markdown file at its root giving large language models a curated map of its most important content. The goal is helping AI systems find canonical pages efficiently. Adoption is growing, but it is not an official standard and major AI engines have not committed to honoring it.

OAI-SearchBot

OAI-SearchBot is OpenAI's search crawler that discovers and indexes web pages to power ChatGPT search results and citations. Unlike GPTBot, it is not used for model training. Blocking OAI-SearchBot in robots.txt removes your pages from ChatGPT's search index, eliminating your ability to be cited in its answers.

PerplexityBot

PerplexityBot is the web crawler operated by Perplexity AI that indexes pages for its answer engine's search index. Pages it crawls become eligible to appear as cited sources in Perplexity answers. Blocking it in robots.txt removes your content from Perplexity's index and eliminates citation opportunities on that platform.

robots.txt

robots.txt is a plain-text file at a website's root that tells crawlers which parts of the site they may access, using User-agent and Disallow/Allow directives. Originally built for search engines, it is now the primary mechanism for controlling AI crawlers like GPTBot and PerplexityBot. Compliance is voluntary but honored by major operators.

Structured Data

Structured data is machine-readable markup, usually Schema.org vocabulary embedded in web pages, that explicitly describes what content means: an organization, product, FAQ, article or review. It helps search engines and AI systems disambiguate entities and extract facts reliably, supporting rich results and cleaner interpretation by answer engines.

User Agent

A user agent is the identification string a browser or bot sends with every web request, declaring what software is making it. AI crawlers identify themselves with tokens like GPTBot or PerplexityBot, which is how sites detect AI bot activity in logs and target them with specific robots.txt rules.

Content & Authority

Brand Authority

Brand authority is the degree to which a brand is recognized, trusted, and treated as a reference within its category — by audiences, search engines, and AI systems. It accumulates from expertise demonstrated over time, third-party validation, and consistent presence, and it strongly influences whether AI engines mention a brand in recommendations.

Comparison Page

A comparison page evaluates two or more products or services against each other — X vs Y pages, alternatives pages, and feature matrices. These pages target high-intent evaluation queries, and AI engines retrieve them heavily when users ask assistants to compare options, making them critical assets for influencing AI-driven buying decisions.

Content Cluster

A content cluster is a group of interlinked pages covering one topic: a pillar page surveying the subject plus focused pages addressing each subtopic, question, and use case. The structure builds topical authority, captures queries at every depth, and supplies AI engines with a matching passage for nearly any question in the topic.

Content Freshness

Content freshness is how recently a page was published or meaningfully updated, and how current its information is. Search engines boost fresh content for time-sensitive queries, and AI engines with live retrieval favor recent sources — while models without browsing are limited by their training cutoff, making maintained content visible across both modes.

Digital PR

Digital PR is the practice of earning coverage, mentions, and links from online publications, journalists, and influential sites through newsworthy content, data stories, and expert commentary. Beyond backlinks, it shapes how brands appear in third-party sources — the material AI models learn from and retrieval engines cite when describing a market.

E-E-A-T

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It is the framework Google uses to assess content quality, especially for topics affecting health, finances, or safety. Strong E-E-A-T signals include author credentials, first-hand experience, accurate sourcing, and a consistent reputation across the web that both search engines and AI systems can verify.

Entity SEO

Entity SEO is the practice of optimizing content around entities — clearly defined people, brands, products, and concepts — rather than keyword strings. It helps search engines and AI systems unambiguously identify what and who your content is about, using structured data, consistent naming, and connections to knowledge bases like Wikipedia and Wikidata.

FAQ Schema

FAQ schema is structured data markup (FAQPage in Schema.org vocabulary) that labels question-and-answer pairs on a page in machine-readable JSON-LD. It tells search engines and AI crawlers exactly which questions a page answers and what the answers are, mapping content directly onto the conversational queries users ask AI assistants.

Featured Snippet

A featured snippet is the highlighted answer box at the top of Google search results, extracted from a ranking page to answer a query directly. Snippets take paragraph, list, or table form. The extraction skills that win snippets — concise, self-contained answers under descriptive headings — are the same ones that earn citations in AI-generated answers.

Information Gain

Information gain is the measure of how much new, unique value a piece of content adds beyond what already exists on a topic. Content that merely restates consensus offers low gain; content with original data, novel analysis, or first-hand detail offers high gain — and gives both search engines and AI systems a reason to surface and cite it.

Internal Linking

Internal linking is the practice of linking pages within the same website to each other, using descriptive anchor text. It distributes authority across pages, establishes topical relationships, guides users through related content, and helps both search crawlers and AI crawlers discover, contextualize, and correctly interpret a site's structure.

Knowledge Graph

A knowledge graph is a structured database of entities — people, organizations, products, places — and the relationships between them. Google's Knowledge Graph powers knowledge panels and entity understanding in search, while similar structures inform how AI systems resolve ambiguity, verify facts, and decide which brands belong to which categories.

Listicle

A listicle is an article structured as a numbered or ranked list — best tools, top strategies, leading companies. The format dominates commercial-intent search results, and AI engines cite listicles disproportionately when answering best-X and recommendation queries, making them one of the highest-leverage formats for influencing AI answers.

Original Research

Original research is content built on data you generated yourself — surveys, benchmarks, experiments, analyses of proprietary datasets — rather than compiled from existing sources. It earns links and media coverage in traditional SEO, and it is among the strongest citation magnets in AI search because language models preferentially cite primary sources for factual claims.

Passage Ranking

Passage ranking is the evaluation of individual sections of a page, rather than the whole page, to determine relevance to a query. Google introduced passage-based ranking in 2021, and AI search engines extend the principle: they retrieve, score, and cite self-contained passages, making section-level structure as important as overall page quality.

Pillar Page

A pillar page is a comprehensive page covering a broad topic end to end, serving as the hub of a content cluster. It links out to focused subpages covering each subtopic in depth, and they link back. The structure concentrates authority, organizes coverage for crawlers, and creates many citable entry points for AI retrieval.

Programmatic SEO

Programmatic SEO is the creation of large numbers of pages from structured data and templates — location pages, integration pages, comparison matrices — each targeting a specific long-tail query pattern. Done well, it captures demand at scale; done carelessly, it produces thin content that search engines and AI retrieval systems ignore.

Schema Markup

Schema markup is code added to web pages using Schema.org vocabulary — usually as JSON-LD — that explicitly describes page content to machines: what an article is about, who wrote it, what a product costs, how an organization is defined. It powers rich results in search and helps AI systems parse, verify, and attribute content accurately.

Semantic HTML

Semantic HTML is the use of HTML elements that describe the meaning of content — headings, articles, lists, tables, nav — rather than generic containers like div and span. It helps browsers, assistive technologies, search engines, and AI crawlers parse page structure accurately, making content easier to extract, index, and cite.

Topical Authority

Topical authority is the perceived depth and breadth of a website's expertise on a specific subject, built by covering a topic comprehensively across many interlinked pages. Sites with strong topical authority rank more consistently for related queries and are more likely to be retrieved and cited by AI search engines answering questions in that domain.

Measurement & Analytics

AI Brand Perception

AI brand perception is the composite picture AI models hold and present of a brand: what it does, who it serves, its strengths, weaknesses and standing versus alternatives. Formed from training data and retrieved sources, this perception shapes millions of AI-mediated recommendations and can lag or contradict a brand's current reality.

AI Conversion Tracking

AI conversion tracking measures which signups, leads and purchases originate from AI search engines like ChatGPT, Perplexity and Gemini. It connects AI referral visits to downstream conversion events, proving the revenue impact of AI visibility, a connection most analytics setups miss because AI traffic frequently arrives unattributed or labeled as direct.

AI Search Funnel

The AI search funnel describes how buyers move from question to purchase when AI engines mediate research: discovery, comparison and objection-handling happen inside the chat, and users visit websites late, already shortlisted and ready to act. The result is fewer clicks than classic search but markedly higher intent per visit.

Answer Volatility

Answer volatility is the tendency of AI engines to give different answers to the same prompt across runs, days and models, caused by sampling temperature, model updates and changing retrieval results. It makes single spot-checks unreliable for measuring AI visibility and is the core reason repeated daily sampling is required.

Competitive Benchmarking

Competitive benchmarking in AI search compares your brand's visibility, mention rate, citation share and sentiment against named competitors across the same set of prompts and AI engines. It turns isolated metrics into context, showing whether you lead, trail or are absent in the categories where buyers ask AI for recommendations.

Core Web Vitals

Core Web Vitals are Google's user-experience metrics for page performance: Largest Contentful Paint (loading), Interaction to Next Paint (responsiveness) and Cumulative Layout Shift (visual stability). They influence Google rankings and, indirectly, AI search visibility, since slow or unstable pages frustrate the crawlers and users behind AI citations alike.

GEO Reporting

GEO reporting is the regular measurement and communication of brand performance in AI search engines, covering visibility scores, mention and citation rates, sentiment, competitor benchmarks and AI-driven traffic. Effective reports translate prompt-level data into trends and actions stakeholders understand, proving whether generative engine optimization work is moving business metrics.

Marketing Attribution

Marketing attribution is the practice of assigning credit for conversions to the marketing touchpoints that influenced them, using models like first-touch, last-touch, linear or data-driven. In the AI search era, attribution must account for influence that happens inside AI chats, where research occurs without generating any trackable click.

Search Intent

Search intent is the underlying goal behind a query: to learn (informational), to find a site (navigational), to compare options (commercial investigation) or to act (transactional). Matching content to intent has always governed search performance, and in AI search it determines which prompts deserve tracking and which content gets recommended.

Sentiment Analysis (AI Mentions)

Sentiment analysis of AI mentions measures whether ChatGPT, Perplexity, Claude or Gemini describe a brand positively, neutrally or negatively when it appears in their answers. Unlike social listening, it evaluates the framing an AI model itself generates, revealing how each engine characterizes your strengths, weaknesses and reputation to buyers.

Share of Model

Share of model is the percentage of AI model answers in a category that feature your brand, measured across a defined set of prompts and engines. The AI-era analogue of share of voice, it quantifies how much of the recommendation space ChatGPT, Perplexity, Gemini and Claude allocate to you versus competitors.

UTM Parameters

UTM parameters are tags appended to URLs (utm_source, utm_medium, utm_campaign, utm_term, utm_content) that tell analytics tools where a visit came from. They override referrer-based detection, making them the most reliable attribution method for links you control, though they cannot tag clicks from AI answers you do not author.

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