The job an AI visibility tool has to do
Three functions define the category. Sample: run real buyer questions through the four major assistants repeatedly, because single checks mislead. Score: turn the answers into mention rates, share of voice, and sentiment you can trend. Explain: surface the citations and consensus signals producing each answer, because those are the levers you can pull.
The test most tools fail
Ask any AI visibility tool one question: this answer names my competitor, why? Tools that respond with a chart failed. Tools that respond with the three review pages and two listicles carrying that recommendation passed, because now you have a work list instead of a mood. Diagnosis is the difference between a tool and a dashboard.
Conversations beat prompts
Buyers do not talk in keywords. They say the lease ends in March or the firm just lost its biggest client, and the recommendation forms across that whole conversation. A tool sampling isolated prompts measures a behavior nobody has. Aethon samples full buyer moments through Contextual AI Presence Mapping©, which is why its numbers predict pipeline rather than decorate reports.
Tool, or tool plus hands
A visibility tool tells you Gemini dropped you in March. Someone still has to publish the quotable content, fix the stale sources, and build the citations that bring you back. Teams with content bandwidth can buy the measurement alone; teams without it should buy the loop. Aethon ships both: the tool and the team that acts on what it finds.
Try before deciding
Bring ten real buyer questions from sales calls to any demo, ours included. A credible AI visibility tool baselines them live, shows who wins each answer today, and tells you why. Judge the category on that conversation, not the feature grid.
