Why this exists
People increasingly get answers from an assistant instead of clicking a link. When that happens, a page that ranks well but is never quoted has lost the visitor entirely, and the usual analytics will not tell you why. LLMention exists to make that failure mode visible before it costs you traffic.
The category has a credibility problem in the other direction too. Tools describe mysterious “AI visibility scores” without saying how they are computed, and some make claims about files and markup that the evidence does not support. This project takes the opposite approach.
What makes it different
- The method is published in full. All twelve dimensions, every check, its exact rule and its point value are on the methodology page, generated from the same data the scanner executes, so the documentation cannot drift from the behaviour.
- No score floors. A page that satisfies nothing scores near zero rather than being lifted to a respectable-looking minimum.
- Low-value signals are weighted low.
llms.txtis one of the three least-weighted dimensions here, because Google has said it does not use it in Search and crawler support is inconsistent. Several tools in this category imply otherwise. - Limits are stated up front. The scanner does not query ChatGPT about your brand. It measures whether your pages are in a state that makes being cited possible, which is a narrower and more honest claim.
Who runs it
LLMention is maintained by the LLMention team as an independent project. It is not affiliated with OpenAI, Anthropic, Google or Perplexity, and it holds no data relationship with them. The scanner is free and requires no account; the paid option is a manual audit, described on the pricing page.
Written and maintained by the LLMention team. The scoring method is published in full on the methodology page, and the source is public at github.com/Alex13192/geo-scanner, so a disagreement with a result can be specific: say which check and which URL.
Contact
Questions, corrections and audit requests all go to the same place: alex.xu@ccie13192.com. If the scanner reports something about your site that you believe is wrong, that is the most useful message you can send, and the method is public specifically so a disagreement can be specific.
Primary sources
Where this project makes a judgement about what generative engines favour, it follows published research rather than folklore:
- Generative Engine Optimization: How to Dominate AI Search — KDD 2024, Princeton and Georgia Tech.
- What Generative Search Engines Like
- The llms.txt convention