Read search impressions beside your answer runs.
Bring Google Search Console impressions into the context of your AI answer runs. Compare dated observations while keeping search performance and sampled AI answers distinct.
Pro is in beta: join the waitlist and we’ll tell you when it opens. No payment is taken.
Shared dates, different measurements.
Illustrative exampleSelect a reporting week to inspect impressions and sampled mentions separately.
Search impressions and answer observations in the same review.
Fictional data. No Google account connection or revenue attribution is performed.
Keep the two measurements distinct
Search Console impressions describe search exposure within that property's reporting. Answer runs describe the samples collected for selected questions. Putting them alongside each other helps a review; it does not establish that one caused the other.
Inspect the question and response evidence in AI answer tracking alongside the separate search-impression measure.
Connect only when the real flow is validated
The read-only Google connection is implemented but has not been connected to Google for real. The integration needs a successful live OAuth flow and a real property before being offered as a working connection.
Use the before-and-after change log to add timing context without claiming that a logged change caused either result.
How it works
- Connect a permitted Search Console property when the beta connection is enabled.
- Choose the relevant reporting period and answer run.
- Inspect the two evidence sets with their own labels.
What to know before you use it
- The real Google OAuth flow has not been tested.
- This feature shows impressions alongside answer runs.
- It does not connect AI exposure to a person, deal or revenue.
Questions
Is the Search Console connection live?
It has not yet completed a real Google connection and needs live validation.
Does this calculate AI revenue?
No. Revenue attribution is not built.
Make the leadership review useful.
Bring search context, sampled answers and delivery decisions into a scoped human review without inventing causal revenue attribution.