Share of Model (AI Share of Voice)

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.

ORM 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ORM is defensive — monitor mentions, counter coordinated FUD campaigns, correct factual errors, suppress inaccurate or outdated negative URLs by ranking authoritative content above them, and rebuild reputation after a triggering event (exploit, depeg, regulatory inquiry, founder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.

ChatGPT is the conversational AI assistant developed by OpenAI, launched in November 2022, that interprets natural-language questions and generates synthesized written answers using large language models (currently the GPT-4 and GPT-5 family). With the addition of ChatGPT Search, it now actively browses the live web and cites external sources directly inside its responses, making it one of the most influential answer engines alongside Google AI Overviews and Perplexity. Why it matters: For brands, ChatGPT is no longer just a chatbot — it is an active referral source and reputation surface. When prospects ask ChatGPT about a service, an industry, or a specific company by name, the brands that get cited inside the answer win the trust transfer and the click-through. Earning ChatGPT citations requires the same foundations as Answer Engine Optimization: third-party validation from authoritative outlets, complete schema markup, comprehensive FAQ content, and a public llms.txt file that tells AI crawlers what your site is authoritative on. Brands invisible to ChatGPT in 2026 are increasingly invisible to their own prospects.

llms.txt is a proposed plain-text file placed at the root of a website (e.g. /llms.txt) that summarizes the site's purpose, lists its most important canonical URLs, and provides AI crawlers with a compact, structured map of what the site is authoritative on. It is the AI-engine analog to robots.txt and sitemap.xml, designed specifically to help large language models index, ground, and cite the right pages. Why it matters: As ChatGPT, Perplexity, Claude, Google AI Overviews, and Bing Copilot increasingly drive discovery, llms.txt is becoming a meaningful AEO and GEO infrastructure layer. A well-crafted llms.txt tells AI engines exactly which pillar guides, services, and authoritative resources to cite when answering questions in the brand's domain — reducing the risk of being misrepresented or omitted. Sites without llms.txt are not penalized, but sites with a clean, accurate llms.txt give themselves a structural advantage in AI citation outcomes. Smart Money Media's own llms.txt is publicly available at /llms.txt, and any site can generate a spec-compliant file in 30 seconds with our free llms.txt generator at /tools/llms-txt-generator.

Structured 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.

Multimodal citations are AI-engine answers that incorporate not just text but also images, charts, video clips, and audio passages — each with its own source attribution. Google AI Mode and Gemini already surface inline images and video thumbnails alongside text answers, and ChatGPT Search regularly cites image sources. Why it matters: A brand whose visual assets (charts, infographics, product photos, founder portraits) carry proper alt text, structured-data attribution, and clean, fast-loading hosting becomes citable across more answer surfaces — capturing visibility that text-only optimization misses entirely. Multimodal-citation readiness is fast becoming a core AEO and GEO requirement.