What are AI Visibility metrics?

AI Visibility metrics are the quantitative measures that describe how often, how favorably, and with what sourcing a brand appears inside answers generated by large language models. The core eight are Mention Rate, Citation Rate, Prompt Coverage, Share of Model, Avg Rank When Mentioned, Sentiment, Source Quality, and Recency.

How do AI Visibility metrics differ from SEO metrics?

SEO metrics measure ranked links on a search results page. AI Visibility metrics measure inclusion, framing, and citation inside a generated answer. There is no click-through rate — the answer itself is the endpoint.

How do you measure Gemini AI Visibility?

Gemini is measured by running your target prompts against both Gemini chat and Google's AI Overview, then scoring Mention Rate, Citation Rate to owned domains, Share of Model against category competitors, and Avg Rank when mentioned. Gemini surfaces citations in a card format, which makes citation share directly observable.

How often should metrics be measured?

Weekly is the minimum for trend detection. Daily is standard on the AI Scale System and above. LLM answers vary run-to-run, so we sample each prompt N times per model to establish a stable rate.

Cluster · AI Visibility Metrics

The 8 metrics that measure AI Visibility

Presence. Rank. Sentiment. Citation. Across every model. Per prompt. Every day.

Pillar Metrics Dashboard Audit Report ROI Ecommerce Share of Model Cluster AI SEO

Mention Rate

The % of tracked prompts in which your brand is mentioned by the model.

Citation Rate

The % of mentions that include a source citation pointing at a URL you own.

Prompt Coverage

How many of your target commercial prompts your brand appears in, out of the tracked set.

Share of Model

Your share of total brand mentions inside a given LLM for a category — the AI-native equivalent of share of voice.

Avg Rank When Mentioned

When you are mentioned, what position you appear in relative to competitors (1 is best).

Sentiment

Aggregate sentiment (−1 to +1) of the language the model uses when describing your brand.

Source Quality

The average domain authority and trust rating of the sources the model cites when talking about you.

Recency

How up-to-date the model's information about your brand is, measured against your latest verified facts.

Per LLM

How metrics behave, model by model

Every LLM ranks and cites differently. The metric definition is stable — the signal weighting is not.

Model

Gemini

Gemini blends Google Search + its own model. High-weight signals: knowledge-graph entity, structured data (Product, Organization, FAQ), and top-3 organic ranking on target queries. Track: appearance rate across AI Overview + Gemini chat, cited domains, and source diversification.

Model

ChatGPT

ChatGPT (browsing on) leans on Bing + fine-tuned retrieval + long-tail training data. Signals: brand entity strength, third-party review coverage, structured comparison tables. Track: mention rate on 'best/compare/vs' prompts, citation share to owned domains, memory bias in personalized threads.

Model

Claude

Claude weights authoritative long-form content and trusted third-party sources heavily. Signals: analyst mentions, wiki-style entries, editorial reviews. Track: sentiment (Claude is the most verbose describer), source quality, and refusal rate on commercial prompts.

Model

Perplexity

Perplexity is a live-retrieval answer engine — citations are surfaced next to every answer. Signals: real-time content freshness, schema.org, canonical URLs. Track: citation rate (this is Perplexity's most transparent metric), rank of your citation card, and follow-up prompt behavior.

Model

Copilot

Microsoft Copilot uses Bing + GPT + enterprise-grounding. Signals: Bing rank, LinkedIn entity, Microsoft Learn / trusted enterprise sources. Track: appearance in work/enterprise prompts, tenant-grounded answers when enabled, and cited domains.

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AI Visibility Metrics · FAQ

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