What AI search tracking covers
AI search is where retrieval meets generation: Google's AI results and the assistants that search the live web before answering. Tracking it means recording how those surfaces answer your queries, who they name, and what they cite, on a schedule.
It is a different job from classic rank tracking. A rank describes your position on a list; an AI search answer may not contain a list at all, so the tracked object is the answer itself.
Four capabilities that matter
- Scheduled repeated sampling: answers vary between draws, so a tracker that checks once per query is recording noise with a timestamp.
- Per-surface results: presence in Google AI results says nothing about Perplexity, and blending them produces a number no decision can use.
- Citation capture: the domains an answer cites are the actionable part; a tracker that drops them keeps the score and discards the levers.
- Stable history: when tracked queries are added or removed, the trend should carry a marker, or every basis edit will masquerade as a visibility change.
Periodic checks or continuous tracking
A periodic manual check is legitimate while stakes are low: before content work starts, or when the category rarely appears in AI answers at all. The switch to continuous tracking earns itself once you ship changes and need to know whether they landed.
Weekly cadence on a stable query set is enough for most programs. What matters is that the cadence is kept and the basis holds still, not that the interval is short.
Where AeoWatch fits
AeoWatch tracks AI search alongside assistant surfaces: AI search monitoring covers the search-grounded engines, and AI Overview tracking covers Google's AI results specifically. Both run repeated samples on your query set, record citations per answer, and mark prompt-basis changes in the history.
The same evidence rule applies here as everywhere in the product: any presence number opens down to the samples that produced it.