LLM Visibility
LLM visibility is how often and how favorably large language models mention your brand in their answers. Learn how to measure it and what moves it.
TL;DR
LLM visibility is how often and how favorably large language models mention your brand when they answer questions. It covers what ChatGPT, Claude, Gemini, and Perplexity say about you from model knowledge, and what they say after retrieving live sources.
Why LLM visibility matters
Assistants now sit between buyers and websites for a growing share of research, and the model decides which brands enter the conversation before anyone reaches a search result.
- Shortlist control: An answer names a few options. Absence from that list removes you from consideration without any signal that it happened.
- Description accuracy: The model characterizes your pricing, ideal customer, and strengths in its own words, and those words reach buyers who never visit your site.
- Off-site dependence: Models learn about you mostly from third-party pages, so visibility rests on sources you influence rather than own.
- Traffic substitution: As models answer questions outright, being named inside the answer replaces some of the value a click used to deliver.
How to measure LLM visibility
Build a market-sized prompt set
Keep adding buyer questions until new competitors stop showing up in results. Ten prompts is a demo, not a measurement.
Run across every model separately
Coverage differs sharply between engines, so a blended number hides the one model where you are invisible.
Repeat on a schedule
Answers are non-deterministic. The same prompt returns different brands on different runs, which makes single checks unusable for tracking.
Record mentions and citations apart
Being named and being cited as a source are different outcomes with different fixes.
Log the sources behind each answer
The cited domains are your target list for off-site work.
Report with confidence ranges
A move from 30% to 34% inside overlapping bands is variance, not progress.
LLM visibility vs. search visibility
LLM visibility: Whether a model names you inside a generated answer, assembled from many sources at once, with no fixed positions and different results between runs.
Search visibility: Where your pages rank in an ordered list for tracked keywords, stable enough to check once and trust until the next update.
Tracking this by hand means re-running dozens of prompts across several models every week and logging brands and sources manually. Findrix does it across seven engines, reports mention and citation rates per engine with the uncertainty shown, and ranks the sources feeding answers in your market, including the ones that never mention you. Every gap comes with the fix already written: technical, content and off-site. The audit is free, takes about a minute, and requires no signup.
Metrics for LLM visibility
- Mention rate: The share of tracked prompts where a model names your brand, calculated per model.
- Share of voice: Your mentions against all brand mentions across the prompt set, which turns a raw rate into a competitive read.
- Citation rate: How often your pages are credited as sources rather than your brand being described secondhand.
- Sentiment: Whether the descriptions attached to your name are favorable, neutral, or negative.
- Position in answer: Whether you appear first or fourth, since order carries weight in a short list.
The problem with LLM visibility scores
Every tool in this category reports a score, and almost none of them tell you the denominator. In classic rank tracking you defined the keyword list, so you knew exactly what the percentage was a percentage of.
Most LLM visibility tools generate the prompt set themselves and keep it hidden, which means two tools can report wildly different scores for the same brand in the same week and both be internally consistent. Add non-determinism on top, and a score with no prompt count and no confidence range is closer to a mood reading than a metric.
Before trusting any number, ask what prompts produced it, how many runs it averages, and how wide the uncertainty is. A vendor unwilling to answer is measuring their own prompt set, not your market.
Frequently asked questions
What is a good LLM visibility score?
There is no cross-industry benchmark, and anyone quoting one is comparing incompatible prompt sets and methods. Judge yourself against two things only: your own trend on a fixed prompt set, and your gap against named competitors measured the same way in the same week.
How is LLM visibility different from SEO?
SEO determines which pages rank in an ordered list of links. LLM visibility determines whether a model names your brand while composing an answer from many sources at once. A page can hold position one in Google and never appear in an AI answer for the same question.
Can I improve LLM visibility without changing my website?
Partly, and often that is where most of the gain is. Models draw heavily on third-party pages, so earning mentions and correcting facts on sources they already trust moves visibility even when your own site stays untouched. On-site work still matters for crawlability, structured data, and factual clarity.