AI visibility tracking.

Four numbers counted from our own scans across ChatGPT, Claude, Gemini, and Perplexity frame why AI visibility tracking matters right now:

• More than four in ten answers name no brand at all. Of 152,606 assistant answers recorded for 49,069 tracked buyer questions, 56.7% named at least one company.
• Discovery-stage answers name a brand 32.7% of the time, against 64.9% at decision — brands are far less visible while buyers are still framing the problem than when they are comparing named vendors.
• Evaluation-stage answers name a brand 65.7% of the time, so the middle of the funnel is crowded: the question is which company gets named, not whether one does.
• 140,492 cited sources came from 41,353 different domains — no single publication decides who gets cited.

Monroya scan data, June 16, 2026 to September 17, 2026. Recounted each time these figures change.

AI visibility tracking is the practice of measuring how a brand or product appears in answers generated by large language model assistants — ChatGPT, Claude, Gemini, Perplexity, and others. It captures three things: whether the brand is mentioned in the answer text, whether the brand's domain is cited in the source list, and how the brand ranks against competitors that get named alongside it.

It is distinct from web analytics (which measures who reaches your site) and from traditional SEO (which measures position on a search engine results page). It sits one layer earlier in the funnel: the answer the buyer reads before they ever consider clicking through. In our own scans, 56.7% of answers named at least one company, and the assistant decided which one without the buyer visiting a single site.

As of 2026, a meaningful share of B2B product discovery happens inside an AI chat rather than a Google query. Buyers ask "what's the best X for Y" and read a four-paragraph answer naming three vendors. The shortlist is set before any site is visited. If your brand isn't in that answer, you don't get evaluated.

The shift matters more than the SEO transition of the early 2000s because there's no second page. An AI answer typically names two to five options. Position eleven on Google was disappointing but recoverable. Absent from the answer is invisible.

Tracking presence is not enough. You also need to watch how your brand is being characterized, where the underlying claims come from, and whether the sources assistants pull from still match reality, because an out-of-date page about you can be quoted as current fact.

Community and social sources are part of that picture rather than the whole of it. Of 140,492 cited sources we recorded, editorial pages accounted for 21.9%, analyst pages 11.8%, review sites 11.6%, social 7.2%, and community forums such as Reddit 1.6%. Watching one of those in isolation gives a misleading read.

Monroya scan data, June 16, 2026 to September 17, 2026.

We do not publish a market-size forecast, because we cannot measure one. What we can measure is who gets named: across 152,606 recorded answers, the brands that appear are the ones with retrievable, sourced pages behind them. Every answer that names a competitor instead of you is a buying conversation you were not part of, and it leaves no trace in your analytics.

Monroya scan data, June 16, 2026 to September 17, 2026.

The ROI case is a comparison between two states of the same pipeline: the demand that happens where you cannot see it, and the demand that lands on you because an assistant named you first.

The cost of dark social invisibility

• The evaluation happens off your property. A buyer asks an assistant to shortlist vendors, reads a synthesized answer, and never visits a site you can instrument. No session, no form fill, no UTM.
• Attribution reports it as direct or branded. The deal that started in an AI answer shows up in your CRM as self-sourced, so the channel that actually created it gets no budget next quarter.
• You lose without a loss record. Being absent from a shortlist produces no lost-deal entry, because you were never in the deal. In Monroya's own scan data, the average discovery-stage inclusion rate is 7.6% — most brands are missing from the conversation entirely at the stage that sets the shortlist.
• Mentions without citations compound the problem. 78.9% of the brands we track have never been cited in a tracked answer: they get named, but no link points back, so the assistant sends the click to whoever documented the answer instead.
• The gap widens quietly. A competitor who fixes this earns compounding source coverage while your baseline stays flat, and nothing in your analytics stack raises a flag.

The pipeline value of being the #1 recommended tool in Perplexity

• You are the default, not an option. The first-named vendor in a synthesized answer functions as the recommendation. The buyer arrives already anchored rather than comparing you cold against four alternatives.
• Traffic arrives late in the journey. Perplexity cites its sources inline, so the click that follows a vendor-selection answer is a buyer who has already read the comparison — closer to a demo request than to a blog reader.
• Sales cycles shorten. Third-party synthesis does the differentiation work your team would otherwise repeat on every first call.
• The position is durable. Being named rests on source coverage across documentation, comparison pages, and reviewer evidence, which does not reset on a bid or a campaign end date the way paid placement does.
• It becomes measurable. Once you track mention rate, citation rate, and position by prompt, the channel produces a number you can defend in a budget review instead of an anecdote.

The practical ROI question is not "what is AI visibility worth in the abstract" but "what share of our decision-stage prompts name a competitor first, and what is one of those deals worth." Multiply your average contract value by the share of vendor-selection prompts you are absent from, and the tracking cost stops being the interesting number.

Inclusion and citation figures are from Monroya's aggregated customer scans across ChatGPT, Claude, Gemini, and Perplexity, 90-day rolling window.

A tracking tool maintains a set of prompts that represent the questions your buyers actually ask. It runs those prompts on a schedule against multiple AI providers, parses each response, and records who got mentioned, who got cited, and in what order. Done credibly, this means many runs per prompt per day — single-shot sampling is too noisy to be useful.

The output is a time series: your share of voice in this prompt set, broken down by provider, by persona, and by competitor. Good tools layer opportunity detection on top — surfacing the specific gaps where closing them would move the score the most.

Generative Engine Optimization (GEO) is the practice of getting your brand named and cited inside AI-generated answers on retrieval-augmented surfaces — ChatGPT Search, Perplexity, Google AI Overviews, Copilot, and Gemini with browsing. Unlike SEO, which targets a ranked list of links, GEO targets the single answer that names two to five vendors and shapes the buyer's shortlist.

The questions below are the definitional ones buyers and teams ask when they first meet the category. Each answer is marked up as FAQPage structured data so assistants can extract it directly.

What is GEO?

GEO (Generative Engine Optimization) is the practice of getting your brand named and cited inside AI-generated answers on retrieval-augmented surfaces — ChatGPT Search, Perplexity, Google AI Overviews, Copilot, and Gemini with browsing — where the model pulls live sources at query time.

What is AI visibility?

AI visibility is how often and how prominently a brand appears inside AI-generated answers: whether it is mentioned in the answer text, whether its domain is cited as a source, and how it ranks against the competitors named alongside it. GEO is the work; AI visibility is the measurement layer that tells you whether the work landed.

How is GEO different from traditional SEO?

SEO optimizes for a ranked list of ten blue links. GEO optimizes for the single generated answer that names two to five vendors. The levers are different: GEO prizes citable statistics, comparison pages, structured data, review-site presence, and community threads over traditional ranking signals alone.

Why does GEO matter for B2B brands?

A meaningful share of B2B product discovery now happens inside AI chats. Buyers ask 'what's the best X for Y' and read a short answer naming a few vendors. If your brand isn't cited, you don't make the shortlist — regardless of your organic search position.

What moves the needle in GEO?

Four things: citable comparison content that models can quote, presence on review sites and directories, active Reddit and community threads, and structured data that helps retrieval systems understand your entity. Each gives the model a source it can pull at query time.

How do you measure GEO success?

Track mention rate, citation rate, and share of voice across the prompts your buyers actually ask, split by provider and buyer stage. A GEO program is working when your brand appears in more answers and is cited as a source more often over time.

Which tools track GEO and AI visibility?

Tools in this category include Monroya, Profound, Otterly, AthenaHQ, Peec AI, and Scrunch AI. Monroya runs each buyer prompt multiple times per model across ChatGPT, Claude, Gemini, and Perplexity, separates mentions from citations, splits results by buyer stage (discovery, evaluation, decision, retention), and traces every score back to the raw model response.

How long does GEO take to show results?

On retrieval-augmented surfaces, new comparison pages, review-site listings, and community threads can change answers in days to weeks, because the model fetches sources at query time. Changes that depend on the base model's own knowledge take months, in step with model refresh cycles.

The two acronyms get used interchangeably and shouldn't be. Generative Engine Optimization (GEO) is the practice of getting your brand named and cited inside AI answers on retrieval-augmented surfaces — ChatGPT Search, Perplexity, Google AI Overviews, Copilot, Gemini with browsing — where the model pulls live sources at query time. Large Language Model Optimization (LLMO) is the practice of getting your brand represented in the model's underlying weights and default behavior, so it is named even when no live retrieval happens.

The distinction matters because the two disciplines use different levers, on different timelines, measured against different surfaces:

In practice, GEO is where near-term score movement lives — a well-placed comparison page or a well-answered Reddit thread can land in a retrieval-augmented answer within a scan cycle. LLMO is the compounding layer underneath: entity consistency and third-party authority that decides how the base model describes you the next time it is refreshed.

AI visibility tracking sits above both. It measures whether either kind of work is landing — on retrieval-augmented surfaces for GEO, and on browsing-off surfaces for LLMO — and attributes lift to the specific asset or change that caused it. Treating GEO and LLMO as one bucket ("AI SEO") makes it impossible to tell which lever is working, which is why the measurement layer keeps them separate.

Three questions separate signal from noise. First, how many runs per prompt per day — anything below five is likely to misreport movement as a trend. Second, do they distinguish mentions from citations — collapsing them hides which kind of work will fix the gap. Third, can you audit any number back to the raw model response — if not, the score is taken on faith.

Secondary questions: how many providers, do they support persona framing, do they generate the asset that closes the gap, and is the pricing usage-based or seat-based for the team size you actually need.

A credible AI visibility report tells you, for a given prompt set and persona, your share of voice today, what changed this week, which competitor moved against you, and the top three actions that would close the biggest gaps. Anything less is a dashboard, not a tool.

The job of AI visibility tracking is to make sure you are one of the vendors the assistant summarizes. In our scans, the companies that get named repeatedly are the ones whose facts are easy to retrieve and easy to check, not the ones with the most pages.