LLM Visibility

What LLM visibility is

When a buyer asks ChatGPT, Gemini, Claude or Perplexity which tool to use, the answer names a few brands and cites a few pages. Being one of them is LLM visibility. It is not the same as ranking: a page can sit at position one on Google and never be cited in an AI answer, and a brand with weaker rankings can be the one the model recommends.

LLM Visibility in OnPagePilot measures that directly, by asking the questions your buyers ask and recording who the answers name and what they cite.

Seven AI surfaces, one view

OnPagePilot asks the questions your buyers ask and records whether you, or your competitors, are named and cited in the answers from ChatGPT, Gemini, Claude, Perplexity, Grok, Google AI Mode and Google AI Overviews. For each tracked query you can expand the answer every platform gave, with the URLs it cited, so you see the exact wording that did or did not include you.

ChatGPTGeminiClaudePerplexityGrokGoogle AI ModeAI Overviews

History, not a snapshot

Every scan is kept per keyword, and share of voice is snapshotted daily, so you see the trend and can tie a change in AI visibility to what you published. The tracked-queries list shows per-platform status, visibility, the change since the last scan, sentiment, citations and the last check date; a single scan tells you where you stand today, the history tells you whether you are moving.

Competitors side by side

Competitor share of voice is overlaid on yours, with a competitive leaderboard, and the citation lists show which pages the models actually pull from, including the ones that are not yours. The source analysis lists the domains the AI answers cite, which competitors they cite next to you, and the prompts behind each citation. That is the reading list for what to write next.

What moves the number

AI answers cite pages that cover a subject completely and are easy to extract from: clear headings, direct answers, consistent terminology. That is the same thing Topical Authority builds and the SEO Editor scores. When a competitor is cited for a query and you are not, open the page the model cited, run the keyword through Topical Authority, and write the page that covers the subject better. Then watch the next daily snapshot.

A practical workflow

1. Track the questions, not the keywords. Add the queries a buyer would actually type into an AI assistant about your category.

2. Read the citations before the score. The share-of-voice number tells you where you are; the cited pages tell you why.

3. Fix one query at a time. Pick the query where a competitor is cited and you are not, build the page, publish it, and let the daily snapshots show the change.

4. Watch sentiment as well as presence. Being named is not enough if the mention is negative; the sentiment column records how you were described.

The difference is what sits next to it

Other tools track AI mentions. Here the tracking sits beside the competitor analysis, the Technical Audit and the content engine that changes the answer: one plan, not four bills. The measurement and the fix are in the same product.

Where to go next

The LLM Visibility feature page shows the tracked queries, the expanded answers, the share-of-voice chart and the source analysis with product captures. The LLM Visibility FAQ answers the questions we get most often about how the scans work. The Topical Authority guide explains how to build the content that gets cited.

If you are in the BETA and a scan reports an answer that does not match what you see on the platform, tell us on the contact page.