LLM SEO: how a model decides which clinician to name.
A patient asks an assistant who treats their condition in their city. Something has to come back. Here is what decides whether it is one of your consultants, and what cannot be promised.
Four questions.
What is LLM SEO?
It is the work of making a source likely to be retrieved and quoted by a large language model answering a question. The levers are different from ranking: a model has to resolve who you are before it can decide whether to cite you, so identity comes before content.
How do language models decide which doctor to name?
They resolve the entity first and retrieve about it second. A clinician who exists as a clear, consistent, verifiable entity — same name, same registration, same affiliation everywhere — can be named. One who exists as a stock photograph on a hospital page cannot, however good they are.
Is LLM SEO the same as answer engine optimization?
They overlap almost entirely. The distinction people draw is that answer engine optimization includes Google’s AI Overviews and similar features, while LLM SEO is usually meant to describe chat assistants. The work is the same work.
Can you guarantee a model will name our consultants?
No, and anybody who guarantees it is selling something. Model behaviour changes between training cycles and between retrieval configurations. What can be done is to remove every reason a model has for being unable to identify your clinician.
A model resolves who you are before it decides whether to quote you.
This is the part most content strategies miss. Retrieval is not the first step. Before a system can retrieve anything about Dr Sharma, it has to establish which Dr Sharma, and whether that entity is the kind of thing worth citing on a medical question.
An entity that appears inconsistently — one spelling here, different qualifications there, an affiliation the hospital’s own site does not confirm — does not resolve cleanly. Everything published under it accrues to an ambiguous node.
We know this problem intimately because we created it for ourselves. We chose a company name three other companies already held, and published the whole repair as a build log rather than quietly fixing it.
The honest limits of this work.
- We cannot guarantee a model names you. Behaviour changes between training cycles and between retrieval settings, and nobody outside the labs can see the mechanism.
- We cannot show you a ranking report for it. There are no positions. There is citation or no citation, and measuring it means running the same queries repeatedly and recording what comes back.
- Model recall lags search. Grounded systems that browse update within weeks. Ungrounded recall may take a year or may never arrive, and we do not count it as a success condition.
- We have published a protocol, not a completed study. An earlier claim that doctors were named in under 20% of answers was withdrawn. The study has not been run. The correction and prospective protocol are published together.
Scope. This page is marketing and search analysis written by a non-clinician. It contains no medical advice and no clinical guidance. Decisions about tests, treatment and care belong with a registered medical practitioner. Search figures are from Ahrefs, India locale, pulled 21 October 2026; volumes are twelve-month averages carrying normal estimation error.
Before a model can name your consultant, it has to be sure who they are.
The audit checks each of your consultants for the four things above, and tells you how many currently resolve.