How do you track changes in AI narratives about your brand over time?

Change tracking requires methodological consistency. The same prompts, run against the same engines, on a fixed cadence, with the full response stored verbatim each time, is what makes change detection possible. AIQ™ is built this way: daily polling, identical prompts across runs, full response storage with diff capability, theme tagging that persists across runs, sentiment scoring on the same scale. From that foundation, the analytical layers become possible: text-level diffs that show exactly what changed in an engine’s response between two dates, theme trajectory analysis that shows which framings are gaining or losing weight, source attribution shifts that show which sources are entering or leaving the engine’s citation set, sentiment trend lines per engine and aggregated. Without the methodological consistency, change detection is impressionistic at best.