LLM visibility is how often, and how favorably, large language models mention your brand when people ask them questions your business should answer. It is the metric underneath every AI search conversation, and most brands have never measured theirs.
When someone asks ChatGPT, Gemini, Claude, or Perplexity for advice in your category, your LLM visibility is the probability you get named, weighted by how early and how favorably. It is probabilistic because the same question can produce different answers between sessions, which is why single spot-checks mislead and sampled rates are the real measure.
Buyers now describe situations to language models and act on the names that come back. Our research across 1,200 buyer moments found recommendations forming early in those conversations, before any traditional search happens. A brand with strong rankings and weak LLM visibility is winning a channel buyers are leaving.
Models synthesize consensus. The inputs they weigh: how consistently the web describes you, review volume and sentiment, presence in the comparison and best-of content they cite, structured data they can parse, and content that answers questions directly enough to quote. None of it is purchasable, all of it is buildable.
Fix a set of twenty real buyer questions. Run each through the four assistants repeatedly and score mentions, framing, and competitor share. Track it weekly, because model updates rewrite answers without warning. This is the measurement layer Aethon runs continuously, with an LLM visibility tracker built on full conversations rather than keyword prompts.
Work the drivers in order of leverage: sharpen positioning so models can categorize you, publish direct quotable answers to the questions buyers ask, build presence in the citations assistants lean on, and correct stale third-party sources feeding them wrong facts. Then verify the answers moved, because effort that does not change an answer is a cost, not a strategy.
How often and how favorably large language models like ChatGPT, Gemini, Claude, and Perplexity name your brand when users ask relevant questions.
Run your buyers’ real questions through the four assistants in fresh sessions and record the answers, or use a tool that samples them continuously and reports rates.
One that covers all four major assistants, samples repeatedly, tracks whole conversations, and maps the citation sources behind each answer. Aethon adds the execution layer on top.
Effectively yes. LLM visibility emphasizes the models themselves; AI visibility is the broader umbrella. Both measure presence in AI answers.
No assistant sells placement in organic answers. Visibility is earned through the consensus models read, which is why early honest work compounds.