AI Visibility · Dental Groups & DSOs
When someone asks an AI engine for a recommendation, one to five names come back. We measure whether yours is one of them, then build the presence that puts it there.
Dental Groups & DSOs specifically
The method below is the same whichever industry you are in. These are the parts that are not — and they are usually what decides whether the programme works.
"Emergency dentist open now" is resolved by hours, open-now status and phone availability far more than by page content. A practice with wrong or missing hours on Bing Places is invisible for the highest-intent query in dentistry.
Patients ask for a dentist who takes their plan. Engines pull accepted-insurance data from Zocdoc, Healthgrades and Business Profile attributes. A practice that has not published it is excluded from a large share of otherwise qualified answers.
A six-practice group typically has six differently formatted names, two duplicate Business Profiles and three unclaimed Bing listings. Engines resolve entities by matching, so inconsistency reads as several weak businesses rather than one strong one.
The method
Brands are around six times more likely to be cited through third-party sources than their own domain. For local businesses the split is starker still: engines resolve recommendations from Google Business Profile, Bing Places, Foursquare, Yelp and review platforms, then rank what they find by review quality and recency. A site redesign does not move any of it.
ChatGPT resolves through Bing and Foursquare. Gemini and AI Overviews weight Google Business Profile. Perplexity leans heavily on community discussion. Copilot draws on Bing plus editorial coverage. An agency optimising only for Google is working on one surface out of four, and its reporting will look perfectly healthy while three of them fail.
Because the four engines are not reading the same internet. A practice can be named in three quarters of Gemini answers and almost none from ChatGPT — which is not a content problem, it is an unclaimed Bing Places listing. Diagnosing that requires measuring each engine separately, which is why we never report a single blended number.
Every engine ends up weighting reviews, because reviews are the closest available proxy for the question being asked: which of these is actually good. Observed practice puts the entry point near thirty reviews at 4.3 stars, and above a hundred in competitive metros. Recency counts about as heavily as volume — two hundred reviews with nothing in six months loses to sixty updated weekly.
Every month we re-run your prompt set across all four engines and report the First Five Score, the per-engine breakdown, your share of voice against named local competitors, and the citation source behind each mention. The number moves, and you can see exactly what moved it. That is the difference between a programme and a campaign.
Category specifics
The method does not change between industries. The questions your patients actually ask, the sources the engines trust, and the rules you have to advertise inside all do.
Prompts we run for this category
Rules this category advertises inside
Citation layer
Each engine resolves a local recommendation through a different set of sources. These are the ones that decide whether a dental group gets named — and most of them are not your website.
Scope
Specifics rather than adjectives — and an equally specific list of what it does not cover, because unclear scope is the most common cause of a bad first quarter.
Included
Not included
What to expect, when
5 questions
Directory and structured-data fixes can register within two to four weeks. Review velocity and third-party presence compound over three to six months. We report monthly either way, so progress is visible before it is complete.
No, and treat anyone who does with suspicion. No AI platform publishes how it selects sources. We work from measured output and correlations documented across the industry, we report honestly, and we re-baseline the source map quarterly because these systems change fast.
Traditional SEO gets a page ranked. This gets a business named. The inputs barely overlap — most of the work sits in directories, review platforms, community and editorial coverage rather than on your site.
Each location is measured separately, because engines resolve them separately. Multi-location groups get per-location scoring, and the common failure is a group whose brand is strong nationally but whose individual locations are not resolved as distinct entities.
We report it and explain what changed. These systems shift underneath everyone — community content is weighted differently quarter to quarter, and engines change retrieval without notice. A report that only ever contains good news is not a report.
Before any retainer, we run fifty prompts across four engines and show you exactly where you stand against the operators being named instead.
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