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Documentation Index

Fetch the complete documentation index at: https://docs.shareofmodel.ai/llms.txt

Use this file to discover all available pages before exploring further.

Share Of Model measures AI-driven brand performance across three pillars. Each pillar answers a different question, and each maps to a distinct module in the product. Together they form a complete picture of how a brand exists inside an AI-driven search ecosystem.

Visibility

Where do I appear?

Perception

How am I described?

Influence

What shapes the answer about me?

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Visibility — Where do I appear?

Visibility is the presence layer. It measures whether and how prominently your brand or domain shows up in answers, across both traditional search engines and generative engines. It is captured by:
  • Presence Rate — how often you appear in eligible results.
  • Visibility Score — how prominently you appear, combining presence and ranking.
  • Average Position — the rank you reach when you do appear.
Read these signals to understand where you exist in the AI landscape, by engine, thematic, prompt, and time period. This is the tactical layer — the closest analogue to traditional SEO performance.

Search Visibility module

Visibility metrics live in the Search Visibility module.

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Perception — How am I described?

Perception is the meaning layer. It measures what models think your brand stands for — the recurring concepts, attributes, and category cues they consistently associate with you. It is captured by:
  • Awareness — how often a brand is mentioned in a category, and how prominently.
  • Pros and cons clusters — the recurring strengths and weaknesses LLMs surface.
  • Attribute scores — -5 to +5 per attribute, vs. competitors.
  • Sentiment — how brand mentions are framed (-1 to +1).
Read these signals to understand how you are framed by AI models. This is the strategic layer — it provides the conceptual context that makes tactical visibility moves meaningful.

Brand module

Perception metrics live in the Brand module.

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Influence — What shapes the answer about me?

Influence is the causal layer. It measures which sources feed an LLM’s answer — the domains, URLs, and communities the model relies on when forming an opinion or surfacing a citation. It is captured by:
  • Source role — whether a domain acts as a Source, a Link, or both.
  • Citation rate — how often a source is used to build the answer.
  • Influence Score — frequency × position weighting, on which sources LLMs lean.
  • Fan-out queries — the internal queries the model uses, and which sources surface inside them.
Read these signals to understand what to influence to move visibility and perception. If the goal is to be cited by ChatGPT for “best running shoes for trail”, the influence layer tells you which sites already get cited — and where you need to be present.

Sources & Links

Per-domain influence inside Search.

Attribute Influence Sources

Per-attribute influence inside Brand.

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How the three pillars fit together

The pillars build on each other — each layer explains the one before it.
If you only have…You can answerYou miss
VisibilityWhere am I present and where am I not.Why answers favour someone else, and what they say about me.
Visibility + PerceptionWhat models think of me and where I appear.Which sources to influence to change the picture.
Visibility + Perception + InfluenceThe full causal chain — from sources, to perception, to visibility.—
Read the three pillars in this order:
  1. Perception to understand the conceptual ground.
  2. Visibility to measure where you stand on that ground.
  3. Influence to identify the levers that move both.

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What’s next

Analyses & Collects

The unit that produces all three pillars.

Recommendations & Briefs

How insights become marketing action.