Prompt Visibility
Prompt visibility is the practice of measuring whether — and how — a brand appears when a representative set of AI prompts is run against the major AI search engines. It is the AI-era equivalent of rank tracking: instead of asking "where do I rank for [keyword]?", brands ask "do I get mentioned when a buyer asks ChatGPT [buyer question]?" Why it matters: Prompt visibility is the only metric that directly captures AEO and GEO performance, because traditional rank tracking misses every zero-click AI answer. Smart Money Media runs prompt-visibility scans across GPT-5, Claude, Gemini, and Perplexity so brands can see, prompt by prompt, where they win, where competitors win, and which prompts to attack next.
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An AI search engine is an advanced search platform powered by artificial intelligence that fundamentally shifts the search experience from a list of links to conversational, synthesized answers. Unlike traditional search engines, these platforms (such as Google Gemini, Microsoft Copilot, Perplexity, and even integrated AI features like ChatGPT Search) generate comprehensive responses, often citing multiple sources, rather than merely pointing to web pages. Why it matters: This paradigm shift means that for a brand's information to be included or cited, its content must exhibit strong entity signals, demonstrate high authority and factual accuracy, and be structured in a way that AI models can easily process and trust. The goal is to be a primary 'ingredient' in these AI-generated answers, rather than just a link on a results page. For example, a user asking "What are the benefits of [Brand X's] new service?" expects a direct answer citing the brand's official statements or authoritative reviews, not just a list of links to articles about it.
AI SearchAI search is the broad category of search experiences powered by artificial intelligence and large language models, where users receive synthesized, conversational answers instead of (or alongside) traditional lists of links. This includes Google AI Overviews, ChatGPT Search, Perplexity, Microsoft Copilot, and Gemini. Why it matters: AI search has shifted the SEO playing field. Ranking on page one of Google is no longer enough — brands must also be cited by AI models when users ask questions about their industry, products, or expertise. AI search systems prioritize sources with strong entity signals, consistent brand mentions across authoritative sites, structured data, and content that directly answers user intent. Optimizing for AI search means building digital authority through PR, earning media mentions, implementing schema markup, and creating content that AI models can easily understand, trust, and reference in their generated responses.
Answer Engine Optimization (AEO)Answer Engine Optimization (AEO) is the discipline of structuring web content so that AI-powered answer engines — including Google AI Overviews, ChatGPT Search, Perplexity, Microsoft Copilot, and Google Gemini — select that content as the cited source when they generate a direct answer for a user's query. Where traditional SEO optimizes to rank a page on a results list, AEO optimizes to be quoted inside the answer itself. Why it matters: As zero-click search consumes a larger share of all queries, being the cited source inside an AI-generated answer becomes far more valuable than ranking #10 on a traditional results page. AEO best practices include writing one-sentence factual definitions immediately under question-based H2 headings, publishing comprehensive FAQ sections with FAQPage schema, building strong Organization and Author schema, earning third-party citations from authoritative outlets, and maintaining a public llms.txt file. Brands that adopt AEO early are positioned to dominate AI citations as the AI search market matures.
Share of Model (AI Share of Voice)Share of Model — also called AI Share of Voice — is the percentage of relevant AI prompts in a category for which a given brand is mentioned, normalized against competitors. If 100 buyer-intent prompts are run against ChatGPT and Brand A is named in 38 of them while Brand B is named in 12, Brand A has 38% Share of Model versus 12% for Brand B. Why it matters: Share of Model is the AI-era analog of Share of Voice in traditional media. It is the cleanest single metric for tracking whether AEO and GEO investment is moving market perception — and it is the metric that most accurately predicts inbound demand from AI-driven buyer research.
ORMORM stands for Online Reputation Management — the operational discipline of monitoring, shaping, and defending what appears about a brand, executive, or project across Google search results, AI answer engines (ChatGPT, Perplexity, Gemini, Google AI Overview, Claude), social platforms (X, Reddit, Discord, Telegram, Warpcast), review sites (Trustpilot, G2, Glassdoor, Google Business Profile), and earned media coverage. Why it matters: ORM is distinct from PR. PR is offensive — earn coverage, build narrative, compound authority. ORM is defensive — monitor mentions, counter coordinated FUD campaigns, correct factual errors, suppress inaccurate or outdated negative URLs by ranking authoritative content above them, and rebuild reputation after a triggering event (exploit, depeg, regulatory inquiry, founder controversy, FUD attack). The four working elements of credible ORM are monitor, respond, suppress lawfully, and rebuild — run in parallel, not sequentially. Crypto ORM specifically operates inside the FTC Endorsement Guides, Section 17(b) anti-touting rules, Section 5 registration constraints, and platform terms of service. ORM tactics that involve Astroturfing, fake reviews, undisclosed paid commentary, coordinated bot pushback, court-order forgery, or 'guaranteed first-page suppression in 30 days' are not reputation management — they are FTC and SEC enforcement risk dressed up as a service. Credible ORM treats AI Overview citations, Wikipedia presence, and structured-data entity signals as first-class reputation surfaces alongside the classic Google SERP.
Structured DataStructured data is machine-readable code — most commonly implemented as JSON-LD using the Schema.org vocabulary — that explicitly labels the entities, relationships, and facts on a webpage so search engines and AI engines can interpret them precisely instead of inferring them from text. Common types include Organization, Person, Article, FAQPage, HowTo, Product, Review, Event, and DefinedTerm. Why it matters for AEO and GEO: Structured data is the single most-leveraged technical SEO investment for AI search. AI engines use it to disambiguate entities, surface FAQ answers in AI Overviews, ground HowTo steps, and confirm authorship and credibility. A page with the right structured data is dramatically more likely to be cited verbatim by ChatGPT, Perplexity, Google AI Overviews, and Bing Copilot than the same content without it. Structured data is not optional infrastructure for any brand serious about being cited in AI answers.