The AI Visibility Glossary

In plain English

How often, and how well, your brand appears in the answers AI assistants give buyers (ChatGPT, Gemini, Claude, Perplexity) when they ask what to buy or who to trust.

Under the hood

A composite measure of a brand's presence in generated answers, tracked across assistants, questions and competitors. Where SEO measures rankings in a list of links, AI visibility measures whether the model names you, describes you correctly and places you well inside one synthesised answer.

Why it matters

Buyers increasingly read one answer instead of scrolling ten links. If your brand isn't in that answer, you are invisible at the moment of decision, however well you rank on Google.

What it means for your brand

Start with a baseline: ask the questions your buyers ask, on each assistant, and note who gets named. Then track it every week, because answers move with every model update.

In plain English

The work of making your brand more likely to be named, quoted or recommended in answers generated by AI assistants.

Under the hood

A set of practices covering content structure (direct, quotable answers), machine-readable data (schema), crawlability for AI bots, third-party corroboration and a consistent brand entity. It overlaps with SEO, but optimises for inclusion in one answer rather than a position in a ranked list.

Why it matters

SEO gets you ranked; GEO gets you named. The two share groundwork, but a page can rank first on Google and still never be mentioned by ChatGPT.

What it means for your brand

Most GEO wins are unglamorous: a clear answer under each question-style heading, the same company facts across your site and profiles, and being described on the review sites and lists that assistants read.

In plain English

Optimising to be the answer, not a link. First used for featured snippets and voice assistants, now for AI assistants too.

Under the hood

The term predates generative AI: it grew out of optimising for Google's direct answers and voice search. Today AEO and GEO describe largely the same work, with AEO stressing the question-and-answer format.

Why it matters

Vendors and agencies use AEO, GEO and AI SEO interchangeably. Knowing they mean nearly the same thing saves you from buying three versions of one service.

What it means for your brand

Write the page the way a buyer asks: a heading phrased as the question, then a two or three sentence answer right under it. That is the format assistants lift most easily.

In plain English

The everyday name for getting your business found and recommended by AI assistants. In practice, the same thing as GEO.

Under the hood

Used in two ways: optimising for AI search (GEO), or using AI tools to do classic SEO faster. Searches for "AI SEO" mix both meanings.

Why it matters

When someone offers you "AI SEO", ask which one they mean. Writing blog posts with ChatGPT is not the same as being recommended by it.

What it means for your brand

The test of AI SEO work is simple: after a few weeks, do the assistants name you more often on the questions your buyers ask? If nobody measures that, it isn't AI SEO.

In plain English

A tool that answers a question directly instead of listing links: ChatGPT, Gemini, Claude, Perplexity, Copilot, and Google's AI Overviews.

Under the hood

A system that combines a language model with retrieval, often live web search, to produce one synthesised answer, sometimes with links to the sources it used.

Why it matters

Each answer engine draws on different sources and models, so your brand can be recommended by one and absent from another.

What it means for your brand

Track the assistants your buyers actually use, not just ChatGPT. A brand named by ChatGPT and missing from Gemini loses every buyer who asks Gemini.

In plain English

The AI-written summary Google shows above its normal search results for many questions.

Under the hood

Generated by Google's Gemini models from pages in Google's index, with links to some of the sources. It appears on a growing share of informational and comparison searches.

Why it matters

It sits above the ten blue links, so it absorbs clicks that used to reach websites. Ranking well no longer guarantees you get seen.

What it means for your brand

If your traffic is falling while your rankings hold, AI Overviews are a likely cause. Being one of the sources cited in the overview is the new version of ranking first.

In plain English

Google's conversational search mode, where you ask follow-up questions and get AI answers instead of a results page.

Under the hood

A separate mode of Google Search powered by Gemini. It runs several searches behind one question and composes an answer with links.

Why it matters

It moves Google further toward the chat format, which raises the stakes on being part of the answer rather than one link among ten.

What it means for your brand

Gemini's answers are a reasonable proxy for how Google's AI features see your brand. If Gemini doesn't name you, AI Overviews and AI Mode are unlikely to either.

In plain English

When an AI answer names your brand, or links to your page, as a source or a recommendation.

Under the hood

In assistants that search the web, citations are the links attached to the answer. More broadly, it is any explicit reference to a brand or source inside the generated text.

Why it matters

A citation is the AI equivalent of a top ranking: it puts your name, and often a link, in front of a buyer at the moment they decide.

What it means for your brand

Look at which sites the assistants cite when they answer questions in your category. Those are the pages worth being mentioned on: comparisons, directories, review sites and forums.

In plain English

How often the AI actually names your brand or links to your site as a source, not just talks around the topic.

Under the hood

The percentage of relevant answers where the brand is explicitly named or cited, as opposed to answers that cover the category without naming it. High topic coverage with a low citation rate means the model knows the space, but not you.

Why it matters

It separates "the topic is covered" from "we are covered". A model can give a thorough answer about CRM tools and never once say your name.

What it means for your brand

Compare your citation rate with your top competitor's on the same questions. The gap tells you how much ground there is to win, and on which questions.

In plain English

The share of questions where your brand comes up at all: named, recommended or referenced.

Under the hood

The percentage of a defined question set on which the brand appears anywhere in the answer. Broader than citation rate, it counts any appearance, including passing references and comparisons.

Why it matters

It is the top-of-funnel visibility number. Before worrying about how you are described, you need to know whether you are in the answer at all.

What it means for your brand

A mention rate of 30% on buying questions means seven buyers out of ten who ask AI will never hear your name. That is the number to move first.

In plain English

Your slice of the conversation: of all the brands AI names for your buyers' questions, how often it names you rather than a competitor.

Under the hood

The share of brand mentions attributable to one brand within a defined question set and assistant, as a percentage of all mentions of the tracked brands. Tracked over time and per assistant.

Why it matters

It turns "are we there?" into "are we winning?". You can appear in every answer and still be an afterthought behind two competitors.

What it means for your brand

Watch share of voice per question, not just overall. Losing on the highest-intent questions ("best X for Y") matters more than losing on general ones.

In plain English

How AI understands your business as one specific thing: a name, a website, what you do, where, and which profiles belong to you.

Under the hood

Search engines and models build entities from consistent signals: your name, description, logo, address and linked profiles (LinkedIn, Crunchbase, review sites), often expressed in Organization schema with sameAs links.

Why it matters

If your name is generic or your description changes from site to site, the model may confuse you with another company, or fail to connect your website to your reviews.

What it means for your brand

Use exactly the same name and one-line description everywhere: your site, LinkedIn, Google Business Profile, G2 and directories. Consistency is how an assistant becomes confident enough to recommend you.

In plain English

Labels hidden in your web page that tell machines what things are: this is the company, this is the price, this is an FAQ.

Under the hood

Machine-readable markup, usually JSON-LD using the schema.org vocabulary (Organization, Product, FAQPage, Article, LocalBusiness). Crawlers read it to extract facts without guessing from the layout.

Why it matters

It removes ambiguity. A model that reads your price, location and FAQ as data is less likely to state them wrongly.

What it means for your brand

The essentials for most businesses: Organization (with your profiles in sameAs), LocalBusiness if you serve an area, FAQPage on pages that answer common questions, and Product or Offer for what you sell.

In plain English

A plain-text file at the root of your website that summarises what your site is and points AI tools to its most useful pages.

Under the hood

A proposed convention (/llms.txt, written in Markdown) designed for language models rather than search crawlers. Support varies: some AI tools read it, and the major assistants have not committed to using it.

Why it matters

It is cheap to add and can help AI tools understand your site faster, but it is not a ranking switch. Treat it as a helpful extra, not the core of your strategy.

What it means for your brand

Keep it short: one paragraph on what you do and for whom, then links to your key pages. Update it when your positioning changes, or it will describe a business you no longer are.

In plain English

The bots AI companies send to read websites, such as GPTBot and OAI-SearchBot from OpenAI, ClaudeBot from Anthropic, and PerplexityBot.

Under the hood

Each crawler has a user agent you can allow or block in robots.txt. Some collect training data, others fetch pages live when a user asks a question. Many cannot run JavaScript, so text that only appears after scripts load may be invisible to them.

Why it matters

If your robots.txt blocks these bots, or your pages need JavaScript to show their text, assistants may be unable to read you at all. They cannot recommend what they cannot read.

What it means for your brand

Check two things: that robots.txt allows the AI crawlers you want, and that your key pages show their main text in the raw HTML. A free AI readiness check covers both in about a minute.

In plain English

The engine behind ChatGPT, Claude and Gemini. It reads text and predicts what should come next, well enough to write, summarise, compare and recommend.

Under the hood

A transformer neural network with billions of parameters, trained on very large text collections to predict the next token. Chat, summaries and recommendations all emerge from that one objective plus further training.

Why it matters

Every AI recommendation about your brand is produced by one of these models, combining what it learned in training with what it finds at the moment of the question.

What it means for your brand

Different assistants run different models, trained on different data. That is why your visibility has to be measured per assistant, not once for "AI".

In plain English

AI that produces something new (text, images, code, audio) rather than sorting or scoring what already exists.

Under the hood

Models that generate outputs by sampling from learned probability distributions: language models for text, diffusion models for images, speech models for audio.

Why it matters

When a buyer asks an assistant for a recommendation, the answer is generated fresh each time. There is no fixed results page to rank on, only a probability of being named.

What it means for your brand

Because each answer is generated, the same question can name you once and leave you out the next time. Visibility is a rate, measured over many answers.

In plain English

Everything a model learned from before it was released.

Under the hood

The text collection used to train the model, up to a cutoff date. Facts that appear often and consistently in it are recalled more reliably.

Why it matters

What the web said about your brand before the cutoff, in volume and consistency, shapes what the model "believes" when it answers without searching.

What it means for your brand

Being described the same way across many independent sources (articles, reviews, directories, forums) is how a brand ends up in a model's memory. One page on your own site rarely is.

In plain English

The date after which a model knows nothing from its training.

Under the hood

The end date of the training data. Anything later, such as a new product, a price change or a rebrand, is unknown to the model unless it looks it up with web search when answering.

Why it matters

An assistant answering from memory may describe your old pricing, your old name, or your competitors' position from a year ago.

What it means for your brand

Keep your key facts current on pages AI crawlers can read, so assistants that search the web find the up-to-date version. And check regularly what each assistant says about you.

In plain English

The moment the model actually produces an answer. Training is school; inference is the job.

Under the hood

A pass through the model to generate output tokens. For assistants that search, it includes fetching and reading pages before writing the answer.

Why it matters

Every buyer question triggers a new inference, which is why answers can differ between users, days and model versions.

What it means for your brand

Treat each answer as a sample, not a verdict. A single check tells you what one answer said; a weekly series tells you where you really stand.

In plain English

When the model states something false with complete confidence. It isn't lying: it has no concept of truth, it produces plausible text.

Under the hood

A consequence of next-token prediction: the model optimises for likely continuations, not verified ones. Rates fall sharply when the correct facts are present in the sources it reads.

Why it matters

An assistant can state wrong facts about your business, such as an old price, a service you don't offer or a competitor's feature attributed to you, to a buyer who believes it.

What it means for your brand

Publish clear, current facts on pages AI can read (pricing, services, locations, FAQ), keep them consistent on your profiles, and check regularly what each assistant says about you.

In plain English

Before answering, the AI looks things up and answers from what it found. An open-book exam instead of a memory test.

Under the hood

A two-stage pattern: retrieval finds the passages most relevant to the question, from the web or a document store, then the model writes its answer from those passages.

Why it matters

When an assistant searches before answering, your pages can be among the sources it reads. That is how a brand missing from training data can still be recommended.

What it means for your brand

Make your pages easy to retrieve and quote: one topic per page, the question in the heading, the answer right below it, and facts stated plainly rather than buried in marketing copy.

In plain English

Basing an answer on specific sources, and pointing to them, instead of on the model's memory alone.

Under the hood

Combining retrieval with instructions to answer from the sources found, usually with citations. Google calls web-backed answers in Gemini "grounding with Google Search".

Why it matters

Grounded answers are more accurate and usually cite their sources, which gives brands a direct way in: be one of the sources.

What it means for your brand

When you test an assistant on your category, look at the sources it grounds its answer on. Getting mentioned on those pages is often faster than ranking your own.

In plain English

When ChatGPT, Gemini, Claude or Perplexity searches the internet before answering, instead of relying on memory.

Under the hood

The assistant runs one or more searches, reads the top results and writes an answer from them, usually with links. Perplexity searches by default; ChatGPT, Claude and Gemini search when a question needs current information.

Why it matters

Search-backed answers reflect today's web, so recent content, reviews and comparisons can change what an assistant recommends within weeks, not years.

What it means for your brand

The pages assistants find through search are usually the ones that already rank and are cited elsewhere. Classic SEO still feeds AI visibility; it just isn't enough on its own.

In plain English

Search that understands what you meant, not just the words you typed.

Under the hood

Retrieval ranked by similarity of meaning (using embeddings) rather than shared keywords, often combined with keyword search because exact names still matter.

Why it matters

Assistants match buyer questions to pages by meaning. A page that answers "how much does it cost" can be found for "is it affordable" without sharing a word.

What it means for your brand

Write in your buyers' words, not your internal jargon. If customers say "AI visibility" and your site says "generative presence analytics", you are harder to match.

In plain English

Turning text into a list of numbers that captures its meaning, so a computer can measure how similar two texts are.

Under the hood

A dense vector, typically hundreds to thousands of dimensions, produced by an embedding model. Texts with related meanings land close together, even without shared words.

Why it matters

Embeddings are how retrieval systems decide which page answers a question, before the model writes anything.

What it means for your brand

Clear pages with one topic match clear questions. Pages that mix five topics match all of them weakly.

In plain English

How much the model can hold in mind at once: the question, the pages it read and its own answer.

Under the hood

The maximum number of tokens in one request, input and output combined. Modern models handle from about 128,000 to over a million tokens, but they pay less attention to the middle of very long inputs.

Why it matters

An assistant reading several pages to answer one question only uses part of each. The parts it uses are usually the clearest, most direct passages.

What it means for your brand

Put the important facts first, in plain sentences. A key fact buried in the eighth paragraph is less likely to make it into the answer.

In plain English

The unit AI reads and writes in. Roughly three quarters of a word in English.

Under the hood

Sub-word units produced by a tokeniser. Models are limited, and billed, by the number of tokens they read and write.

Why it matters

Assistants read your pages in tokens, under a limit. Long, padded pages spend that budget on filler.

What it means for your brand

Concise pages are easier to use in full. Say what you do, for whom and at what price early, then go into detail.

In plain English

The question or instruction typed to an AI.

Under the hood

The full input sent to the model: system instructions, conversation history, any pages it retrieved, and the user's message.

Why it matters

For your visibility, the prompts that matter are your buyers' questions: "best X for Y", "X vs Z", "is X worth it". Each wording can produce a different answer.

What it means for your brand

Track a fixed set of real buyer questions, in their words, including your city or niche if you are local. That set is your visibility scoreboard.

In plain English

The standing instructions an assistant always follows, whatever the user types.

Under the hood

A privileged message that sets the assistant's role, tone, rules and tools. Models weight it above user messages, though it is not a security boundary.

Why it matters

Each assistant's hidden instructions shape how it recommends: some favour well-reviewed options, some avoid naming brands without sources, some prefer fewer, safer picks.

What it means for your brand

You cannot change ChatGPT's system prompt, but you can see its effect: if an assistant only names brands with strong reviews, your reviews are part of your visibility.

In plain English

A dial from predictable to varied. Low means nearly the same answer every time; high means more variety.

Under the hood

A setting applied to the model's probabilities before it picks each word. Consumer assistants use a setting above zero, so their answers vary.

Why it matters

The same question asked twice can get two different answers, sometimes naming you and sometimes not.

What it means for your brand

That is why one check isn't enough: visibility is measured as a rate over many answers, week after week, not from a single screenshot.

In plain English

Showing the AI two or three examples of what you want before asking for the real thing.

Under the hood

In-context learning: examples in the prompt shape the output without changing the model.

Why it matters

It explains why wording matters: questions that look like typical comparisons ("best X for Y") trigger list-style answers that name several brands.

What it means for your brand

When you choose which questions to track, include the list-style ones ("best", "top", "alternatives to"). They are where brands get named most.

In plain English

The model working through its reasoning step by step before answering.

Under the hood

Generating intermediate reasoning before the final answer. Newer "reasoning" models do it internally by default, often while running several searches.

Why it matters

Reasoning assistants compare options against criteria (price, reviews, features, location) before recommending. Brands with clear, checkable facts on those criteria hold up better.

What it means for your brand

Make your differentiators explicit and verifiable on your site: who it is for, the price, the location, what is included. Vague claims don't survive a step-by-step comparison.

In plain English

Retraining a model on extra examples so it permanently adopts a style or a task.

Under the hood

Continued training on a curated dataset, adjusting the model's weights. It teaches behaviour more than facts.

Why it matters

Brands can't fine-tune ChatGPT or Gemini to recommend them. Anyone promising to "train ChatGPT on your brand" is describing something else, or nothing.

What it means for your brand

The levers you actually control are the sources the models read: your site, your profiles, your reviews and third-party mentions.

In plain English

An AI that doesn't just answer, but takes steps to complete a task: searching, comparing, filling in forms, even buying.

Under the hood

A loop where the model receives a goal and a set of tools, chooses a tool, reads the result and repeats until the task is done.

Why it matters

Buyers are starting to hand research and shortlisting to agents. An agent that shortlists vendors only considers the ones it can find and understand.

What it means for your brand

Make the facts an agent needs easy to read on your site: what you sell, prices, availability, how to buy or book. Hidden pricing and vague pages get skipped.

In plain English

Giving an AI a set of actions it can take (search the web, open a page, check a calendar) and letting it choose which one to use.

Under the hood

The model receives function definitions and emits structured calls; the application runs them and feeds the results back.

Why it matters

Web search is a tool. Whether an assistant uses it, and which pages it opens, decides whether your current content is part of the answer.

What it means for your brand

When you test your visibility, note whether the assistant searched the web. Answers from memory reflect your past reputation; answers from search reflect your current content.

In plain English

A standard way to connect AI assistants to tools and data, so one connector works with any assistant that supports it.

Under the hood

An open protocol, introduced by Anthropic and widely adopted, that standardises how models discover and use external tools and data through MCP servers.

Why it matters

It lets businesses plug their own data into assistants, and lets tools such as visibility trackers be queried directly from an AI chat.

What it means for your brand

If your team works in Claude or ChatGPT, an MCP connection lets them ask "how did our AI visibility change this week?" without opening another dashboard.

In plain English

Paid placements inside AI assistants, such as ads in ChatGPT, shown alongside or within the answer.

Under the hood

Ad formats that appear in conversational interfaces, targeted by the topic of the conversation rather than by a keyword. Formats and rules are still evolving from one platform to the next.

Why it matters

Paid placement can put you in front of buyers while your organic visibility grows, but it doesn't change what the assistant itself says about you.

What it means for your brand

Measure both: where you appear organically in answers, and what you pay to appear next to them. Paying to show up on questions you already win organically is wasted budget.

In plain English

Hidden instructions placed in content an AI reads, such as a web page or a review, to make it say or do something.

Under the hood

The model can't reliably tell trusted instructions from untrusted content it reads. Indirect injection, through pages fetched by search, is the harder case.

Why it matters

Some sites hide text to manipulate AI recommendations. Assistants increasingly detect and discount it, and it can damage your credibility if exposed.

What it means for your brand

Don't try it. Earn mentions with clear content and genuine third-party coverage: that is what holds up as assistants get better at spotting manipulation.

In plain English

The rules and checks that stop an AI saying or doing something harmful or wrong.

Under the hood

Layered controls: instructions, filters on inputs and outputs, restricted tools and policies. In assistants, they include rules about recommending products and citing sources.

Why it matters

Guardrails make assistants cautious: many avoid recommending a brand they can't support with sources, especially in health, finance and legal categories.

What it means for your brand

In sensitive categories, credible sources matter even more: official listings, professional directories, reviews and press coverage give the assistant a reason to name you.

In plain English

A repeatable test of an AI system: the same set of questions, scored the same way, run again and again.

Under the hood

A set of cases plus scoring rules (exact match, a rubric, or a model acting as judge), used to detect improvements and regressions.

Why it matters

Tracking your AI visibility is an eval of the assistants on your brand: a fixed question set, asked on a schedule, scored on whether and how you are named.

What it means for your brand

Keep your question set stable so the weeks are comparable. Add questions when your business changes, and pause old ones rather than deleting them, so their history stays.

In plain English

Deterministic means the same input always gives the same output. Probabilistic means it usually does. Classic search is closer to the first; AI answers are the second.

Under the hood

Language model outputs are sampled from a probability distribution. Even at low temperature, exact repeatability is not guaranteed across versions or infrastructure.

Why it matters

There is no permanent "position 1" in AI answers. Your brand has a probability of being named, which shifts with each model update and each new piece of content.

What it means for your brand

Judge progress on trends, not single answers: a mention rate rising over several weeks is real progress; one good screenshot is not.