Which AI visibility platforms offer historical data and time-series reporting?
Otterly documents daily prompt monitoring and cumulative counts; Profound documents daily answer datasets and charts. For context, our Volume III study found a mean vendor-set Jaccard overlap of 0.499 across 62 Google AI Overviews repeat pairs on 4 August 2026, making the underlying runs material to a trend.
What we measured
Reported. Otterly's 5 October 2026 monitoring guide, read on 29 October, describes daily collection across engines available in an account. Its prompt table distinguishes cumulative brand mentions and cumulative domain citations from per-response measures, and offers response-by-response and engine-level detail.
Profound's Answer Engine Insights documentation, also read on 29 October, describes prompts sent daily, an aggregated response dataset and charts with their own configuration. It distinguishes visibility from share of voice; those measures use different denominators even when displayed in the same reporting area.
Measured. The Volume III study design compared the sets of named vendors in original and repeated Overview answers. Jaccard overlap divides the shared vendor set by the combined set. The published mean across 62 pairs is a similarity score, not 0.499 of 62 answers or a percentage change in a brand's visibility. That study does not establish either platform's retention period, backfill or historical accuracy.
What this means for you
Separate totals from rates. In Otterly's documented columns, another collected run can increase a cumulative citation total without showing that your citation rate improved. Keep the response count and the engine filter beside the cumulative series before using it as evidence of a change.
For a Profound export, retain the stated metric and its base. The AEI guide defines visibility using responses that contain at least one brand, whereas its share-of-voice explanation uses brand mentions. Switching between those charts is a change of measure, not simply another view of an identical percentage.
The 62 repeat pairs explain why a history request needs the answer records as well as the chart. A changed set of vendor names can arise between repeats. To investigate one movement, keep the two responses with their prompt, engine and collection date; then identify the changed names. That supplies a checkable event instead of attributing every bend in the time series to work completed on your website.
Also asked as
How should I read historical AI visibility results?
which ai visibility platforms offer historical data and time-series reporting?
Related
- Otterly’s monitoring columns
- Profound’s AEI datasets and charts
- Volume III repeat-run evidence
- The Manual on measurement noise
- A fair recheck
Next step
Keep the response from the Free 10-question check (one engine) as one dated sample, rather than a historical series. No email is needed; runs are limited per network and per day, and by the site's daily allowance.
For one sourced factual error, Fix one answer, $490. Retain the answer and the document that supports the correction.
Our answer-study method, v1.1.
Prepared by the Broadcastwell team.
Page owner: Vikram Patel, AI Architect. Corrections: hello@broadcastwell.com