Multimodal Citations

Gemini is Google's family of multimodal large language models that powers Google AI Mode, AI Overviews, the Gemini consumer app, and Google Workspace AI features. Gemini models can reason over text, images, audio, video, and code simultaneously, and they integrate tightly with Google Search, Google Knowledge Graph, and YouTube. Why it matters: Because Gemini is the engine behind every Google AI surface, optimizing for Gemini citation is effectively optimizing for the majority of branded AI search traffic in the United States. Brands earn Gemini visibility through Knowledge Graph entity strength, schema markup, high-authority backlinks, and content that answers questions in structured, citation-ready prose.

Google AI Mode is Google's dedicated conversational search experience that replaces the traditional ten-blue-links interface with a multi-turn, AI-generated answer surface powered by Gemini. Unlike AI Overviews — which appear above standard SERPs — AI Mode is a separate destination where users ask follow-up questions, refine queries, and receive synthesized responses with inline citations. Why it matters: AI Mode represents Google's commitment to AI-first search and is rapidly becoming the surface where high-intent commercial and research queries are answered. To be cited in AI Mode, brands need strong entity signals (Wikidata, Knowledge Graph), structured data, authoritative third-party mentions, and content written to answer questions in clear, extractable passages. Optimizing for AI Mode is core AEO and GEO practice.

Source attribution is the inline citation behavior of AI search engines — the linked sources that appear next to or beneath an AI-generated answer. Different engines attribute differently: Perplexity cites aggressively with numbered footnotes, ChatGPT Search shows source cards, Google AI Overviews surfaces a small carousel of sources, and Claude cites sparingly but reliably. Why it matters: Source attribution is the new SERP. A brand that gets attributed in an AI answer captures both clicks (for users who follow the citation) and trust signals (the brand becomes associated with that answer for every user who reads it). AEO is fundamentally about earning more, better-positioned source attributions.

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.

Model Context Protocol (MCP) is an open standard, introduced by Anthropic in 2025 and rapidly adopted across the industry, that defines how AI applications (Claude, ChatGPT, IDEs, agents) connect to external tools, data sources, and APIs. MCP is to AI agents what USB-C is to hardware — a universal connector. Why it matters: As AI agents move from chat surfaces to autonomous task execution, the brands that publish MCP servers (exposing their data, tools, or content via the protocol) become first-class citizens in the agentic ecosystem. For B2B brands, an MCP server is the modern analog of a public API: it makes the brand directly usable inside the AI workflows where buying decisions are increasingly happening.

Agentic search is the next stage of AI search: instead of returning a single synthesized answer, an AI agent autonomously plans and executes a multi-step research task — fetching pages, comparing sources, running calculations, and producing a structured deliverable. ChatGPT Agent, Perplexity's Deep Research, and Google's Project Mariner are all early agentic-search products. Why it matters: Agentic search radically expands the volume of pages an AI engine consults for a single user question — from a handful of sources for a chat answer to dozens or hundreds for an agent task. That means more total citation opportunities for well-structured, citation-worthy content, and a sharper penalty for sites that bots can't easily fetch.