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Model and date filters help you control which AI visibility results you are reviewing. Use them to compare platforms, review specific time periods, and understand how your visibility changes over time.

What model filters are for

Model filters let you view results from specific AI platforms. This helps you understand where your brand is strongest or weakest. For example, your brand may appear often in one platform but less often in another. Use model filters when you want to compare visibility across different AI systems.

Locked models

Some models may be locked depending on your plan. Locked models are shown so you can see what is available on higher plans, but they are not included in your active tracking. If a model is locked, Serpy will not use it for your prompt results.

What date filters are for

Date filters let you review results from a specific time period. Use them to understand how your AI visibility changes over time. You can use date filters to:
  • review a recent prompt run
  • compare performance across periods
  • check whether visibility improved after creating content
  • understand changes after source or citation updates
  • review historical answers

Single date vs multiple dates

Selecting one date shows results for that specific run. Selecting multiple dates gives you a combined view of the selected period. This helps you understand both individual runs and broader trends.

Why filters matter

AI answers can change over time. A prompt may mention your brand one day and miss it another day. A source may be cited often during one period and less often later. Filters help you avoid judging visibility from one result alone. When reviewing AI visibility:
  1. Start with all available models.
  2. Review the overall trend.
  3. Filter by model to find strengths and weaknesses.
  4. Use date filters to compare time periods.
  5. Open raw answers when you need more detail.

Tips

Use all available models for the broadest view. Use individual model filters when investigating a specific change. Use date filters when checking whether recent content or source work had an impact. Do not overreact to one date. Look for patterns across time.