Design a responsible answer sampling method
Agree on buyer questions, markets, assistants and the limits of repeatability before drawing conclusions.
Do not invent a universal AI ranking score.
Visibility in language-model answers is not equivalent to a Google ranking position. A useful measurement programme distinguishes where a brand appears, what was actually cited and whether any visit or qualified enquiry followed.
Visibility in language-model answers is not equivalent to a Google ranking position. A useful measurement programme distinguishes where a brand appears, what was actually cited and whether any visit or qualified enquiry followed.

Agree on buyer questions, markets, assistants and the limits of repeatability before drawing conclusions.
Do not invent a universal AI ranking score.Check whether pages are accessible, accurate and easy to interpret. Look for confusing naming, stale offers or conflicting identity information.
Prioritise high-quality source data before cosmetic prompt tricks.Review referral behaviour and the website experience for visitors who arrive from AI-assisted discovery.
Track outcomes without collecting unnecessary personal information.Work is tailored to the existing site and agreed scope. These are possible areas for a project, not a promise that every item is included in a free review.
We avoid guarantees about Google rankings, AI citations or lead volumes. Clear expectations are part of the service.
No. External assistants and personalisation create visibility gaps. Reporting should state the sampling method and limitations.
Share your website, target market and the issue you want to solve. This staging preview illustrates the user journey; it does not save or deliver submissions.
For a real enquiry, use the existing live RankDigital contact page ↗ until a secure production backend is connected.
STAGING DEMO: no form data is transmitted or stored.