AI search visibility is not a rank tracker with a new label.

The useful question is narrower: when a buyer asks an AI system who they should compare, does your company appear, do competitors appear, and which sources make that answer likely?

That is the shape of Answerproof.

For a first pass, I start with one or more buyer questions that should plausibly exist before a demo request:

Which vendors should I compare for this problem?
What are the best tools or services in this category?
How does one named option compare with another?
What should a technical buyer know before choosing?

Then I check the answer surface for four things:

  1. Whether the target is mentioned.
  2. Whether competitors or adjacent firms are mentioned.
  3. Which domains are cited as evidence.
  4. Whether the cited sources explain the gap.

A small pre-check is not a full audit. It is a triage layer. If competitors show up and the target does not, the next step is a focused diagnostic: more buyer questions, more competitor domains, a source trail, and a 30-day implementation plan.

That is the part most dashboards skip. A founder or growth lead does not only need to know that visibility is low. They need to know what to change first: comparison pages, entity coverage, schema, third-party citations, technical SEO, partner listings, category explainers, or proof pages.

The offer is simple:

  • $1,500 diagnostic.
  • $2,500/month monitoring.
  • $5,000/month build support.

The wedge is also simple: prove the gap before asking anyone to buy a dashboard.

See the Answerproof sample teardown or start with the prompt-map tool.