Data interpretation constraints
While KIME provides a standardized framework for measuring AI visibility, it is important to understand the inherent limitations of the data. AI models are non-deterministic, meaning they can produce slightly different answers even when given the same prompt. These metrics provide a high-level strategic overview rather than a fixed, absolute count of every mention occurring across the global AI ecosystem.Accuracy and model variance
Visibility scores reflect the performance of your brand within the specific prompt library and models you have selected. Because different LLMs—such as ChatGPT, Claude, and Gemini—are trained on different datasets and updated on different schedules, your metrics will naturally vary between platforms.- Model Updates: A sudden shift in your performance score may be caused by a model update or a change in the LLM’s internal ranking logic rather than a change in your own content.
- Retrieval context: For models that use real-time web search (RAG), your visibility is dependent on the specific sources the AI chooses to browse at that exact moment.
- Geographic limitations: KIME simulates tracking at the country level. However, local ISP variations or hyper-local news sources in specific cities may occasionally result in slight differences from the simulated data.
The “black box” of AI
AI models do not provide “ranking factors” in the same way traditional search engines do. Consequently, while KIME can identify where you are mentioned and the sentiment of that mention, it cannot definitively state the exact reason why a model chose to mention a competitor over your brand. The metrics should be used to identify patterns and correlations rather than as proof of specific algorithmic triggers.Sentiment nuances
Sentiment analysis is performed by AI classifiers that evaluate tone and context. While highly accurate, these classifiers may occasionally struggle with:- Sarcasm or irony: Complex brand-related humor in user reviews or forum posts.
- Industry jargon: Highly technical terms that may be perceived as neutral by a classifier but are viewed as positive by an expert audience.
- Ambiguous language: Sentences that discuss a brand in both a positive and negative light simultaneously.