Model coverage
The range of AI models in which a brand or finding appears.
What is Model coverage?
Model coverage shows whether visibility is isolated to one system or consistent across the market. Each model can use different retrieval methods, indexes, and answer styles, so cross-model agreement is more durable evidence than a single response.
In practiceWhat does this look like in a real AI answer?
A brand appears in ChatGPT and Perplexity but remains absent from Gemini and Claude.
Why it matters for AI visibility
AI answers can shape awareness and buying decisions before a person visits a website. Understanding model coverage helps teams connect what a model says with the prompts, sources, and technical signals that influenced the result. The useful next step is to observe the evidence, compare it over time, and act on the specific gap.
How to measure or use it
- Define the scope. Choose the relevant market, prompts, models, locations, and time window.
- Keep the evidence. Save the complete answer, its citations, and the page or claim being evaluated.
- Compare patterns. Look for repeated movement across prompts and models instead of reacting to one response.
- Take a specific action. Improve the owned page, correct a claim, earn third-party inclusion, or create better evidence.