Model Coverage

See how your brand performs across AI models.

Klairia compares visibility across selected LLMs so teams can see whether one model recommends them while another leaves them out.

Problem

One AI answer is not the market.

ChatGPT, Claude, Gemini and other assistants can produce different recommendation sets. A single manual query cannot show whether visibility is consistent across the model landscape.

Outcome

What Klairia gives you

  • Avoid overreacting to one model's answer.
  • Find model-specific gaps in brand recognition.
  • Track whether visibility improves across the models your buyers use.
  • Support reporting with repeatable model coverage.

How it works

A repeatable workflow for multi-model llm scanning.

1

Choose models within your workspace plan limits.

2

Klairia runs the same topic and prompt structure across those models.

3

Results are normalized into model-level scores and scan-level summaries.

4

Model differences are shown in detailed results and trend views.

Questions

Common questions about multi-model llm scanning.

Can teams choose which models to scan?

Yes. Workspace and site selections control which models are used, within the plan limits configured for the account.

Why scan more than one model?

Different AI assistants can mention different products. Multi-model scanning helps teams avoid making decisions from a single answer.

Related features

Build the full AI visibility workflow.

Start tracking

See how AI assistants describe your brand.

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