How it works
How AI visibility works for healthcare practices
There is no “submit to ChatGPT” button.
An AI assistant may answer from knowledge learned during training, retrieve current web sources before it responds, or combine both. None exposes a ranking formula we can control. The practical work is to improve the public evidence available to those systems now and over time.
How AI assistants find practices: two paths, one constraint
We do not claim to see inside a model’s proprietary decision process. We work from the evidence we can inspect.
- Answers from learned knowledge
A model may rely on patterns absorbed during training. Those updates are not immediate, and no one can request a fresh training crawl.
- Answers with web retrieval
When search or browsing is active, the system may retrieve current pages, profiles, and other sources, then synthesize a response. Citations can be inspected; the ranking logic cannot.
- What we can control
Crawlable service and location pages, consistent practice data, specific provider qualifications, accurate structured data, and credible third-party corroboration.
- What no one controls
Which path a model uses, its hidden candidate set, the final wording of an answer, or permanent placement in the recommendations.
What we actually do to get your practice recommended
01Establish the baselineRun a fixed set of high-intent prompts across the models, service areas, and treatments that matter. Record who appears, in what position, and which sources are cited.
02Find the evidence gapsReview the site and the wider web for vague treatment pages, conflicting names or locations, thin clinician credentials, missing proof, and sources that describe the practice inaccurately.
03Make the right claims verifiableStrengthen service pages, entity details, provider expertise, location context, structured data, and credible third-party corroboration. Every change ties back to a target prompt.
04Retest the same questionsRepeat the prompt set on a schedule. Keep what moves visibility, correct what remains ambiguous, and separate a one-off mention from a recommendation that holds across repeated tests.
The scorecard: share of tested answers, recommendation position, citation rate, factual accuracy, competitor frequency, and change from the original baseline. Each result is logged with the model, prompt, location context, and test date.