AI citation monitoring

Example: turn 35 buyer questions into evidence

Sample Brand tracks 35 high-intent comparison, category, trust, and product questions. The first monitoring cycle shows exactly which platforms mention the brand, which answers link to its domain, and where competitors appear instead. The team now has a repeatable baseline rather than a few hand-picked screenshots.

Step by step

1. Choose the questions buyers actually ask

Add your own prompts or select discovered suggestions. Give each question a topic, language, and market, and keep direct-brand checks separate from category questions so they do not inflate headline visibility metrics.

2. Set the schedule and platform coverage

Choose the cadence and AI platforms available on your plan. Runs execute automatically, while retries and fallbacks handle temporary platform failures without silently changing the denominator.

3. Inspect the evidence behind each result

Open any prompt row to read the stored answer, see whether Sample Brand was mentioned, and inspect every cited URL. A citation means the answer linked the configured domain; an unlinked brand mention remains a separate signal.

4. Review the trend and choose the next action

Compare citation rate, mention rate, sentiment, and share of voice by prompt and platform. A change is useful only when the underlying answer explains why it moved.

What makes the data trustworthy

Rates, not anecdotes: every number has a denominator of scheduled runs (prompt monitoring). Mentions are matched against your configured brand and aliases — not naive string search — and citations match your actual domain. When a data point is backfilled or estimated, it is labeled as such rather than passed off as measured.