Once you've identified target queries, the automated system tests them periodically—daily, weekly, or on whatever schedule makes sense for your monitoring needs. Each test queries the AI model with your specified prompt, captures the response, parses which sources were cited, and records whether your content appeared. Over time, this builds a database showing your visibility trends, how often competitors appear for the same queries, and which topics you're gaining or losing ground on.
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Two characters might have identical Unicode skeletons but render differently in specific fonts, or have different skeletons but render identically in a particular typeface. Detecting this requires rendering glyphs and comparing pixel output. No purely Unicode-data-based approach handles it, and UTS #39 does not attempt to.
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