Skip to main content

​
Data collection methodology

KIME generates metrics by simulating thousands of real-world interactions between users and AI models. Every 24 hours at 00:00 GMT, our system runs your entire prompt library through the specific LLMs and geographic regions you have selected. The resulting text is then parsed by our analysis engine to identify brand mentions, determine their placement in the response, and classify the sentiment of the language used. This standardized approach ensures that your data is consistent and comparable across different time periods.

​
The AI Performance Score

The AI Performance Score is a composite index that provides a single measure of your brand’s overall health. It is calculated as a weighted average of three core dimensions:
  1. Visibility Volume: How often you are mentioned.
  2. Placement Quality: Where you rank in the response.
  3. Sentiment Polarity: The tone of the mention.
By combining these factors, the Performance Score prevents a single metric from skewing your perceived brand health. For example, high visibility will not result in a high Performance Score if the sentiment is consistently negative.

​
Handling edge cases

Our calculation engine is designed to handle several common data scenarios to ensure accuracy:
  • No Response: If an AI model fails to provide an answer or states it does not have information, that specific query is excluded from the visibility calculation to prevent penalizing your score for model errors.
  • Multiple Mentions: If your brand is mentioned multiple times in a single response, KIME counts this as one “visibility hit” but uses the highest (best) placement for your ranking score.
  • Indirect Mentions: Our system is trained to recognize brand variations and common misspellings to ensure all relevant mentions are captured accurately.