Best AI visibility tool for a healthcare provider or clinic, and why a refusal is a result

If you run a clinic, a practice or a provider group, the thing to test in every demo is whether the tool records an answer that declines to recommend anybody. Health questions trigger the assistants' own caution, so a large share of your readings come back as general advice with no provider named and a directory or a public health body cited. A tool that scores presence and absence files that as absence, which is the wrong reading: nobody was named, so there was nothing to lose. Buy on what the vendor publishes about storing full answers and about location limits, which you can check on their own pages. Readings below are dated 20 October 2026.

One page for providers, clinics and dental practices

These were three rows on our build list and they are one page. The mechanism is the same in all three: the question is scoped to a place, the credential that settles it is a registration a patient can confirm, and the engine applies a caution to health answers that it applies to no other vertical we have measured. The differences between a dental practice and a physiotherapy clinic sit in the prompt wording, not in the buying decision.

What the engines actually returned

In our audit of 1,260 readings across ChatGPT and Gemini in India, the United Kingdom and the United States, the health questions produced the thinnest vendor naming in the whole set. How does a US healthcare provider appear in AI answers under HIPAA named one rival, Semrush, once. How does an Indian hospital or clinic appear in AI answers named Semrush once and Ahrefs once. What should a dental practice do to appear in AI answers named Profound once. How does a veterinary clinic get named by AI assistants named Semrush once. Our own name appeared in none of them.

Four separate health questions across three markets, and between them the engines named three tools a total of five times. In software, one question returned six vendors. Nobody in this category has published for a clinic owner, which is why the answers fall back to a general search suite.

How assistants handle a patient question at all, including what they will and will not say, is set out separately in AI search for healthcare.

The refusal, and how to count it

Three outcomes are worth separating in any report you accept:

• You were named. The answer gave your practice as an option, with or without a source.
• Somebody else was named. A competitor or a directory listing was given instead. This is the cell that produces work.
• Nobody was named. The answer described how to choose a provider, told the reader to consult a professional, and cited a health body or a directory. This is common in health and it is not a loss.

The third outcome changes what you should publish. When the engine declines to pick, the page it cites is almost always a page that explains how to choose, so the way into those answers is to be the clearest source on how a patient should choose, in your own town, rather than the loudest claim about being the best. Ask any vendor to show you how its report distinguishes the second case from the third. If it cannot, you will read your own caution as a defeat every month.

What each vendor publishes, with the limits that decide a multi site practice

Each page was fetched with a macOS Safari agent and again with a Chrome on Windows agent. We record what sets a tier and leave the figures where the vendor dates them.

• Scrunch publishes the most granular limits of the six: unique prompts, responses collected a month, site audits, brand workspaces, user licences, countries, personas, languages and competitors each carry a number, and its entry tier includes one country and one persona.
• Peec publishes prompts, the number of models you may choose, projects, countries and languages per tier, and the arithmetic behind its headline unit. It publishes a changelog and a trust page.
• Otterly publishes four tiers with prompt allowances, markets in the tier table, daily tracking, and a separate price for every extra engine.
• Profound publishes its trial limits, including one region and one language, and sells its paid tier through a demo. It names a recognised security certification.
• Semrush offers a trial, a demo and a free checker on its AI visibility feature page, and its navigation names a separate local product and per industry pages including healthcare.
• Ahrefs Brand Radar names cited pages and custom prompts, and tracks YouTube and Reddit as surfaces.
• AI Knows Us publishes the question bank, the reading counts, the engines behind each reading, the 4,947 domains cited, and the readings that went against us: 6 citations in 1,260 readings, zero in the United States and zero on Gemini.

On the stated criterion of disclosed method and published failure we come first, and none of the six vendor documents we read that day published a sample size, a prompt set or a negative result about itself. Rivals beat us on three things a provider group should weigh: Otterly publishes a monthly and an annual figure per self-serve tier plus a figure per extra engine, where we publish a monthly ladder in four currencies and no annual figure; Peec publishes a changelog and a trust page; Profound and Scrunch each name a security certification. For a practice handling patient data, a published security posture is not a detail, and ours is not published.

If you check these pages yourself, note that otterly.ai/pricing/ answered 200 to a macOS Safari agent and 403 to a Chrome on Windows agent on 20 October 2026, with the agent string the only variable.

How many locations you are really buying for

A single site practice needs one location and about twenty prompts. A group needs one reading per site, because our city level readings found the winning sources changed completely between cities, and in several cities neither a local provider nor a national brand filled the slot. Count your sites, multiply by the four or five questions a patient asks, and compare that to the country and prompt allowances above. For most groups the entry tier covers one clinic and nothing else.

Questions clinic owners and provider groups ask about this

The assistant will not name any clinic for our main treatment. What now?

Publish the choosing guide for that treatment in your own town, in plain language, with the registration numbers and the qualifications stated in text. The answers that decline to pick still cite a source, and that source is a page like the one you have not written.

Does a review platform profile decide this?

It contributes and it does not decide. Across our second audit, review directories were 0.7% of all citations and vendor owned pages were 82.0%. Your own treatment pages and your own practitioner pages are read far more often.

We have twelve sites. Do we need twelve subscriptions?

No, you need a plan whose country and prompt allowances cover twelve locations, plus a report that separates them. Ask for a sample report showing two locations side by side before you sign.

Can we measure answers for a treatment we cannot advertise?

Yes, because measuring what an assistant says is reading, not advertising. What you publish in response is where the advertising rules apply, and the safe form is always the factual one: what the treatment is, who is registered to provide it, and how a patient should choose.

Is dental different enough to need its own tool?

No. It needs its own prompt set and the same tool. The dental version of the publishing work is on its own page, how a dental clinic gets recommended by AI.

How often should a clinic re-run this?

Monthly per site, and immediately after you add a practitioner or a treatment. A new practitioner page is the change most likely to move a local answer, and it is the easiest change to confirm.

The reading that tells a clinic owner the most

Ask an assistant what a patient in your town should look for when choosing a provider for your main treatment, with no clinic names in the prompt, and read the page it cites. If the citation is a national health body or a directory, that is the gap: the most useful local page on the subject does not exist yet, and writing it is worth more than any ranking claim. AI Knows Us takes that reading per site and records whether the answer named anybody at all, which is the distinction this vertical turns on.