The 15 pages ChatGPT reads before it recommends anyone in your category — and you're on none of them.
Give a brand and a category. We ask the same buying questions a real customer would, record which brands the answers name, rank every source those answers cite, and show you what each of those pages says about the brands that got named — in a Telegram chat, not a dashboard.
- 01revupdental.com — 27 brands named on one page15×
- 02medixdental.com14×
- 03curvedental.com — a competitor’s own blog14×
- 04overjet.com14×
- 05planetdds.com — a competitor’s own domain14×
- 06layer3labs.io13×
- 07guideflow.com13×
- 08withcherry.com12×
- 09nexhealth.com11×
- 10reddit.com/r/Dentistry — one thread — not read: Reddit blocks automated access11×
- 11topelevens.com10×
15 buyer questions · 76 sources ranked · 294 citations. Example brand, real run — every count came back from grounded search.
Open the full map — this is the $49 deliverable →Run it again. You get the same fifteen URLs.
A category's source landscape is stable, not a random sample: ten differently-worded buyer questions converge on the same domains, in the same order, every time we run it. That is the entire trust argument — not a methodology page, a rerun. Every row in your map is checkable: ask the same question yourself and see if the source comes back.
Two lists. The line between them is the whole product.
- 01
The brands AI recommends in your category, ranked by how often they come back. In dental practice software, Curve Dental and Planet DDS were named in 15 of 15 answers; Dentrix Ascend, Open Dental and Oryx Dental in 14. Models do not rank, they enumerate — there is a consensus set they name almost every time, and everyone else is named rarely or never. The first thing you learn is whether you are in it.
- 02
The pages those names came from — and what each page says about each brand. Every source cited across the run, ranked by citations, and underneath each one the brands that page mentions with the actual claim made about them: "Ranked #3; dentist-built, flat-fee, praised for data ownership, criticised for the learning curve." Where a page could not be read — Reddit blocks automated access — it says so instead of showing a blank.
That second list is what you are buying. A visibility score tells you that a competitor is recommended and you are not. This tells you how they got there — the exact page carrying the mention, and the exact sentence that earned it. Every one of those pages is a target you can act on: a roundup to be included in, a thread to answer, a comparison that has you missing or wrong. You are not guessing at what AI likes. You are reading what already worked for the brands it names, and doing the same thing on the same pages.
Three of your competitors already did the thing that gets cited.
- 01
Publish your own category roundup. In dental practice software, Curve Dental's own blog is cited 14 times, Planet DDS 14, NexHealth 11 — vendors citing themselves as the authority. Zero platform risk, no gatekeeper, and your map shows you exactly which page to write.
- 02
Find the one thread doing most of the work. Eight Reddit threads were cited in the dental run, and one of them — r/Dentistry, a 2023 question about practice management software — was cited in 11 of 15 answers on its own. The same shape shows up in HVAC. Your map names the URL: a target you can answer this afternoon, instead of "be active on Reddit."
- 03
See what it takes to out-cite the market leader. In one run, a small vendor publishing two roundup pages was cited 17× — more than the category leader's own site at 5×. You don't need their budget. You need two well-made pages on your own domain.
The buyer who never types your name still ends up on your site.
Being named is a shortlist slot, not awareness. The buyer who used to open ten tabs and compare now asks one question and gets three names back. If you are not one of the three, you were never in the running: no impression, no click, no chance to be compared on price or features. The answer is the funnel now, and it is handed to someone who already decided they are buying.
The set is small and it holds. Five to seven names carry a category, and the same list comes back on every rerun. That is brutal while you are outside it and durable once you are in — a slot you earn keeps paying out instead of resetting every time someone changes an algorithm.
The entry price is pages, not budget. GEO retainers for small brands run $1,500–$5,000 a month. What actually moved the needle in our runs was a small vendor with two roundup pages out-citing the category leader's entire site. The map costs $49 and names the pages. What comes after is writing, not spending.
What we will not claim: how fast. Nobody has measured how long after a page goes up before models start citing it, and we are running that measurement on our own domain in the open rather than guessing at it. The map tells you where to act. It does not promise a date.
Give us a brand and a category. Get the map back in a chat.
Free teaser first · $49 for the full map, sources and all