Perplexity is the most useful engine for a practice, not because of its audience size but because it shows its work. It cites the sources behind every answer, which turns it into a free diagnostic for the question you otherwise cannot answer: which sites are actually shaping what AI says about us.
How it differs
Perplexity is search-first. It retrieves live sources, composes an answer, and lists the citations. It leans less on training data than ChatGPT does, which has two consequences: answers reflect the current web, so recent corrections show up faster, and the sources are visible rather than inferred.
Using it as a diagnostic
This is the real value. Run a set of questions and study the citations rather than the prose:
- Ask who provides your treatments in your city, then look at which sites are cited.
- Ask about your practice by name and note which sources describe you.
- Ask for your address, phone, and hours, then check which source supplied each fact.
- Ask which insurance you accept and see whether the cited source is current.
- Ask about a competitor and note which sources describe them that do not describe you.
That last question is the most instructive exercise in this chapter. If a competitor is being assembled from a vertical directory, a professional society listing, and a treatment locator, and you are absent from all three, you have just been handed your directory priority list with evidence.
Reading the citations
Sort what you see:
- Your own site cited. Good. Check the specific page, since it tells you which pages are doing the work.
- Directories cited. Note which ones, and verify each is accurate. These are your highest-priority listings, empirically rather than theoretically.
- A source you did not know existed. Very common. Go and correct it.
- A stale source cited. This is how you find where a wrong fact originates.
- No citation for your practice. A coverage problem: the retrievable material about you is too thin.
What improves your position
Because it is retrieval-driven, the levers are direct:
- Be present in retrievable sources. The map profiles and the significant directories from chapter four.
- State facts explicitly in text. Retrieval extracts sentences. A page saying plainly that you provide a named treatment at a named address for adults, with a stated session length, is easy to quote. A page relying on imagery and mood is not.
- Avoid burying facts in images or PDFs. Your address in a graphic is invisible.
- Use clear headings and question-shaped subheadings, which makes extraction more reliable.
- Keep pages current and dated, since a citation-first engine favours what looks maintained.
Fixing an inaccurate answer
The citations make this tractable in a way it is not on other engines. Read the cited source, identify the wrong fact, correct it at that source, and correct it upstream if the source is fed by an aggregator or your NPI record. Then re-run the same prompt in a couple of weeks and confirm the answer changed.
That loop, prompt, read citations, fix source, re-prompt, is the only genuinely closed feedback loop available in AI visibility. Use it, and treat what you learn as applying to the other engines too, since they draw on overlapping material.
Copilot is next, and its dependency is simpler.