A vote for
what’s next.

Compare changes in your visibility with a category benchmark over the same period.

A shared movement could help explain a change in your numbers. It would be a signal to investigate, not evidence that a model update caused it.

Use verified crawler activity and the pages visited to suggest topics worth exploring.

Suggestions would be labelled as inferences. A bot request cannot reveal an exact private question or prove that a person asked it.

Compare snapshots of important cited pages to flag removed mentions, new competitors and deleted pages.

See the tracked prompts associated with each source, so you can review what changed. That connection alone would not prove why an answer changed.

Include a draft page in a controlled test and inspect the answer produced for selected prompts.

The result would be a test response, not a forecast of live citations or rankings on an AI platform.

Test product discovery, comparison and shopping steps to identify missing information or blocked journeys.

Checks would be read-only or run in a sandbox and stop before purchase.

Bring observed ads and organic brand mentions into one view where reliable evidence supports the comparison.

Coverage would be shown alongside results. This would not assume complete ad data for every prompt or platform.

Look across tracked or discovered questions for answers that name no brand.

Use those gaps to plan content opportunities. Any demand estimate would include its source and be clearly labelled.

Keep a suitable set of pages unchanged while making a defined change to another set, then compare their visibility.

Results would show sample sizes and limits. A measured difference would not guarantee that the change caused it.

Check brand visibility and factual accuracy in supported voice assistants.

The available checks would depend on provider and device access, with supported coverage made clear.

Find brands with declining visibility and review the evidence behind each opportunity.

Turn that evidence into a draft prospect pitch for an agency to review. Nothing would be sent automatically.

Track public facts about company leaders and founders in AI answers.

Flag gaps, outdated information or confusion using public company information, without collecting private personal data.

When an incorrect claim can be traced to a cited page, collect the supporting evidence and draft a correction request.

Not every model error has an identifiable source. Requests would always remain drafts until reviewed.

See when most observed citations come from a few publishers, which tracked prompts use those sources and where broader coverage could help.

The view would show the collected citations and time window behind each result. It would not predict a visibility loss from losing a source.

Run the same question repeatedly and with small wording changes under controlled settings.

Compare how often your brand appears, the sample size and variation. These would be controlled tests, not private user conversations or a guarantee of future answers.