AI visibility tracking.

Four numbers from Monroya's aggregated brand scans across ChatGPT, Claude, Gemini, and Perplexity (rolling 90-day window) frame why AI visibility tracking matters right now:

• 92% of tracked brands have a citation rate of 0% — their name appears in AI answers, but no source link points back to their site. Mentions dominate; citations are the exception.
• Average discovery-stage inclusion is 7.6%, versus 37.7% at decision stage — brands are ~5× less visible when buyers are still framing the problem than when they're comparing named vendors.
• Average share of voice across tracked prompts is 25.9% — roughly one in four AI answers in a brand's own category names them at all.
• Evaluation-stage inclusion averages 23.3%, sitting closer to discovery than decision — the middle of the funnel is where most brands quietly lose the buyer.

Aggregated across Monroya customer scans, ChatGPT / Claude / Gemini / Perplexity, 90-day rolling window. Refreshed with each scan cycle.

61% of B2B buyers now utilize AI-powered search engines or chatbots as a primary resource for identifying and evaluating potential vendors (Gartner Predicts 2025: The Evolution of Search and Social, 2025). 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. Search Generative Experience (SGE) reduces traditional organic traffic by up to 25% for unoptimized brands (Search Engine Land, 2025).

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.

45% of brand mentions in Large Language Model (LLM) responses contain factual inaccuracies or lack sufficient context to drive a conversion (TrustRadius B2B Buying Disconnect Report, 2025). Tracking presence isn't enough — you also need to monitor how your brand is being characterized, where the underlying claims come from, and whether the sources LLMs pull from align with reality.

Reddit technical breakdowns matter disproportionately here: 70% of technical buyers now trust community sentiment found in Reddit monitoring and social listening over traditional sponsored content (Content Marketing Institute B2B Research, 2025). AI search engines prioritize brands with high community sentiment and verified Reddit monitoring mentions (Salesforce State of Marketing, 2024).

$2.6 billion in revenue is predicted to shift specifically toward brands that successfully capture "Share of Model" within AI-driven interfaces by the end of 2026 (Forrester B2B Marketing Outlook, 2025). The reallocation is happening inside existing demand-gen budgets, not on top of them — brands that don't measure model presence are funding competitors who do.

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.

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.

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.

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.

76% of B2B decision-makers prioritize vendors that appear in consolidated AI summaries over those found through traditional blue-link search results (HubSpot State of Marketing Strategy, 2024). The job of AI visibility tracking is to make sure you are one of the vendors that gets summarized.