The Model Visibility section of your Visibility Audit (formerly known as a report) shows how often your brand is mentioned in AI-generated responses across large language models (LLMs), such as Google Gemini, OpenAI's models, Claude, and others. Each model draws from different sources and interprets questions in its own way, so your visibility can vary significantly from one model to another.
Why do AI models show different results?
AI models are not uniform. Several factors affect how they respond to the same question:
- Training data: each model is trained on different datasets. Some may prioritize brand websites or structured sources like documentation, while others pull more from public forums or general web content.
- Source preferences: some models rely more heavily on trusted publications, reviews, or FAQ-style content. Others may over-index on conversational sources like Reddit or Quora.
- Query interpretation: every model interprets questions slightly differently. Since Gumshoe asks each question as a unique persona, the model's interpretation is shaped by who it thinks is asking.
- Content freshness: some models refresh their training data more frequently. You might see higher visibility in newer models simply because they have seen more recent updates to your content.
- Search optimization: if your brand has content that is structured for AI (for example schema markup, clean metadata, citations), it is more likely to be surfaced in responses across models.
How do I use this section?
If your brand performs well in one model but is absent in others, that is a strong signal to diversify your content or optimize for AI search compatibility.
Model Visibility helps you:
- Understand which AI platforms are recognizing your brand and which are not
- Pinpoint gaps in model-specific visibility
- Adjust your strategy by tailoring content toward the sources each model trusts
- Use these insights to dive deeper into your Conversations