Measurement methodology
Measure AI Visibility With Consistent Testing Conditions
AI results are dynamic. Prime Axiom measures changes across a consistent set of prompts, platforms, locations, and testing conditions to identify directional visibility improvements.
Core measurement categories
Mention rate
How often the company appears across a controlled prompt set.
Citation rate
How often an answer cites an owned or relevant third-party source.
Accuracy
Whether names, services, locations, and differentiators are described correctly.
Share of voice
Visibility relative to defined competitors under consistent tests.
Sentiment and consistency
How stable and favorable the company description is across answers.
Source coverage
Which owned, review, directory, media, and authority sources influence answers.
AI-referred traffic
Qualified sessions attributed to identifiable AI referral sources.
Leads and conversions
Qualified inquiries and outcomes tied to AI-assisted discovery where measurable.
How comparisons remain useful
A baseline records the prompt wording, platform, model or product surface when available, location, account state, date, cited sources, competitors, and answer text. Follow-up tests reuse those conditions where practical.
Results are interpreted as directional patterns rather than a permanent rank. Reporting identifies limitations, material testing changes, and attribution gaps instead of presenting isolated answers as guaranteed outcomes.