Dealer AI visibility glossary

AEO (answer engine optimization). The practice of getting your store into the answer an AI assistant gives, rather than a link on a results page. See AEO for car dealers.

Agent readiness. Whether an AI agent can read and act on a store: structured data, crawler access, llms.txt, machine-readable inventory, and an MCP endpoint. Scored 0 to 100 on our methodology.

AI discovery file. A machine-readable file at a well-known path describing the business for AI agents.

AI Mode. Google's conversational search surface. Composes multi-store answers from Google's index and the Business Profile.

AI Overview. The AI summary above traditional Google results.

Anchor city. One of several cities inside a metro used as the location for a prompt, so a market's score reflects where its shoppers actually are.

AutoDealer schema. JSON-LD that declares a website a car dealership, with address, hours, brands, and links to profiles.

Citation share. The share of an engine's cited sources that came from each domain. Shows whether AI learned about a store from its own site or from DealerRater, Carfax, Yelp, and others.

ClaudeBot, GPTBot, Google-Extended, PerplexityBot. The crawlers that feed Anthropic, OpenAI, Google, and Perplexity models. Blocking them in robots.txt removes a store from what those engines can read.

Find, Buy, Service prompts. The three buckets of non-branded questions used to measure visibility: which dealer, which car in stock, where to service.

GEO (generative engine optimization). Writing and structuring content so a generative model will cite it. See GEO for car dealerships.

Geo-pinned vs geo-neutral. Running a prompt from a residential connection inside the market, versus with no location signal. The gap between the two is a store's out-of-market visibility.

JSON-LD. The format for structured data on a web page. What "schema" is written in.

llms.txt. A plain-text file at /llms.txt describing a site for language models and linking its important pages. Template here.

MCP (Model Context Protocol). An open standard that lets AI agents call tools, such as searching a store's inventory, and get structured data back. See MCP for car dealers.

Named first. The share of AI answers in which a store is listed before any competitor.

Relative vs absolute score. Relative sets the market leader to 100. Absolute measures against a fixed standard. See why a 100 is not AI ready.

Repetitions. How many times each prompt was run. AI answers vary, so a score without repetitions is a single sample. See sample size.

Share of voice. A store's mentions divided by all dealer mentions in the same set of answers.

Spread. The range of a metric across repetitions. Two stores inside the spread are tied.

Visibility. The share of AI answers that name a store, for a given prompt set and engine.