The shelf is built. It is not full yet.
Insights is where we publish what we learn running AI visibility work for medical, legal, and wealth practices: what moved a number, what did not, and the methods behind both. There are no articles here yet. The first pieces are being written now, and this page will not pretend otherwise until they are up.
Four rules, decided before the first piece.
This field produces an enormous amount of writing that exists to fill a content calendar. We would rather publish less and be quotable, so the rules are on the page before the articles are.
Every piece comes from work we actually did: a client engagement with permission and the identifying details removed, a change we made to our own site, or a probe we ran across engines. No roundups of other people's takes.
A probe that moved nothing is more useful than one that worked, because everyone publishes the second kind. When a piece reports what we tried, it carries the attempts that went flat alongside the ones that moved. The flat ones are the whole reason to trust the rest.
If a piece contains a rate, it carries how many runs stand behind it, from which market, on which engines, and over what window. A number without that is decoration, and we would have to argue with it ourselves.
A physician or an attorney reading this at the end of a long day should be able to work out what it means for their practice. No jargon that a definition would not fix, and no advice we would not give a client we liked.
What will live here, once it exists.
These are the categories the writing is organized into. Each one is empty today. When pieces publish they appear under their category, newest first, with the date on them.
How AI visibility gets measured properly: run counts, frozen question sets, mentioned versus cited, confidence and its limits, and the places our own method is weaker than we would like. The technical spine of everything else here.
What we see inside specific verticals: how a dermatology patient and a bankruptcy client ask AI differently, which surfaces the engines lean on per field, and what actually moved for practices in each. Identifying details removed, always.
What the engines appear to read and reward, based on what we can observe from the outside, with the uncertainty stated plainly. Nobody outside a model lab knows the internals, and pieces here will say so rather than imply otherwise.
Where board and bar advertising rules meet AI answers: testimonials, superlatives, outcome claims, and the awkward fact that the most compliant version of your marketing is usually the version engines trust most.
On the desk, not yet on the shelf.
These are in progress rather than published, so there is nothing to click. They are listed because a hub with nothing in it tells you less than a hub that says what is coming.
The arithmetic behind a sound polling cadence, worked through step by step: how wide the confidence band is at five runs, at twenty, at a hundred, and where the extra runs stop being worth the money for a local practice.
Why a single negative review from years ago keeps surfacing when someone asks an engine about a named practice, what that looks like across engines, and the correction paths that are actually available to an owner.
Our working list per vertical, how we assembled it, and how often it changes. The useful version of this is short, which is the opposite of what the fifty-directory submission services would like you to believe.
The same question asked from three cities, with the answers side by side. The argument for geo-varied probing, made with the results rather than with adjectives.
No dates promised. A piece publishes when the underlying work is finished and checkable, not when a calendar says it should. If you want to be told when the first ones land, write to human@aeoptim.com and we will put you on a short list that does nothing else.
Three things here already worth your time.
The writing is behind the work, not ahead of it. Until it catches up, these are the pages that show you how we think.
A live engine asked the question your clients ask, with the answer returned word for word. No card, no call. It is the fastest way to see whether any of this matters for your practice.
Run it →Our measurement method, published in full: how we probe, how many runs stand behind a rate, what counts as mentioned versus cited, and where the method has limits.
Read the method →The questions your clients ask an engine, answered on your own pages in the format the engines quote, with paste-ready schema for each. The fastest paid step from knowing to fixed.
See the kit →The most useful thing on this site is still free.
Whatever ends up published here, it will not tell you as much about your own practice as one live answer will.
Run your free check →Want a heads up when the first pieces land? human@aeoptim.com