Google ranks you fourth in Matthews. The AI still didn't name you.
That sentence is a finding you can bill against, and no rank tracker or AI tracker can produce it on its own. Cite AI puts both facts on the same pin, at every point of a market.
One reading per metro hides the answer
Ask for a plumber from uptown and from the suburbs and you get different businesses — the answer moves with where the searcher is standing. Grid tracking measures a prompt at every point of a grid across your market, so you see the neighbourhoods where your client is named and the ones where they are not, rather than one number for the whole city.
We ran one prompt — “emergency plumber near me” — at nine points across Charlotte, twice at each point. Twenty-one different businesses came back across the grid, and eight of them appeared at only one of the nine points. Two runs at the same point returned mostly the same businesses; two different points shared about a third as much, and seven of the thirty-six pairs of points shared none at all. Points near each other overlapped three times more than points far apart — noise does not do that.
One prompt, one industry, one metro, measured on Gemini’s map results with two runs per point.
Pins sit at their true bearing and distance from the centre with distance rings around them, each carrying the name of the place it stands for. There is no basemap behind them in this version — the geography is in the names and the rings.
- An engine named you here
- Answered here, didn’t name you
- Not measured yet
What each one means for the work
| On the map | What it usually means | Which lever |
|---|---|---|
| An engine named you here | ChatGPT or Gemini recommended this business by name at this point. The pin shows which. | Nothing to fix. This is the point to screenshot. |
| Answered here, didn’t name you | An engine answered at this point and named somebody else. The Google Maps position beside it says which kind of problem it is. | Ranked poorly, or absent from Maps, is the Google Business Profile — service area, category, completeness. Ranked well and still not named is the harder case: on Gemini, review depth is the lever we have measured (29% named under 50 reviews against 75% past 200, holding rank at 1-10). On ChatGPT nothing we have tested separates the businesses it names from the ones it lists and passes over — not reviews, rating, photos or website — so treat that one as diagnosis rather than prescription. |
| Not measured yet | No engine has produced a reading here — a new point, or a run that has not happened. An engine missing from a pin means it did not answer there. | Nothing. Explicitly not a miss; it fills in after the next weekly run. |
Two facts on one pin
Rank tools tell you where a business sits in Google’s map pack. AI trackers tell you whether an answer engine mentioned them. Neither tells you the thing you actually need, which is what happens when those two disagree.
- Ranked fourth and still not named: the listing is fine, the web presence behind it is not
- Named without ranking well: the answer came from the written surface, not the map card
- Neither, at one point but not others: usually a service area or category boundary
Real place names, not coordinates
Every point resolves to a named neighbourhood or town before anything runs. A finding reads “eighth in Pineville”, which is what goes in the client email — not “eighth at 35.086, −80.892”, which goes nowhere.
- Points landing on water or open farmland are dropped, and the map says how many
- If too few resolve, you are told on save and the prompt runs as a single city-level reading
- Points closer than five miles tend to repeat, so the radius and density are yours to set
Set it up once per project
Market reach goes to local, you attach the client’s Google Business Profile, then pick a grid size and radius. After that each prompt opts in individually — nothing fans out until you say so.
- The Google Business Profile is required: AI answers name local businesses without linking to them, so we match on the listing rather than the domain
- Grid size 3 × 3 or 5 × 5 — 9 or 25 points — with a 3 to 25 mile radius
- One map per gridded prompt, so grid the prompts that drive calls rather than the whole panel
Which engines, and how precisely
A local answer is two things stitched together: a written recommendation citing websites, which content and structure move, and a map card drawn from Google's business listings, which the Google Business Profile moves. We measure both and label which is which. How agencies use that split →
Google rank gets your client into the running
Across 5,130 ranked businesses, a business in Google’s top three was named by ChatGPT 48% of the time and by Gemini 76%. Ranked 51st or lower, both named it 1% of the time. Local rank is not a separate game from AI visibility — it is the gate in front of it. If your client is not ranking, that is the first job, and it is the one most agencies already know how to do.
Ranking is not enough, and the gap is what we measure
Even in Google’s top three, ChatGPT skips the business more than half the time. That gap is the whole product: we show you which points, which engines and which competitors got named instead — so the conversation moves from “we are ranking” to “we are ranking and still not being recommended, here is where”.
The two engines do not behave the same way
When the map fires, Gemini answers purely from Google’s listings and cites no web pages at all. ChatGPT runs both at once — it reads the map and the web in the same answer, and puts about ten businesses on its map card while writing only two to four into the prose a customer actually reads. Tracking one engine tells you about that engine.
| Engine | Local precision | What we can tell you locally | Lever |
|---|---|---|---|
| ChatGPT | Point-levelMeasured at every grid point | Which businesses ChatGPT names, and which sites it cites, for a searcher standing at a specific set of coordinates. Run at every point of a grid, nine or twenty-five readings across one metro rather than one. | Website content and structure for the citations; the Google Business Profile for the businesses it names off the map. |
| Google AI Overviews | City-level | The AI answer at the top of Google, captured once from within the market you name — the same answer a searcher standing there gets. (Google’s map pack is read separately at every grid point; that is where your ranked position comes from, and it counts under the map surface rather than under this engine.) | Website content and structure; the local pack alongside it is driven by the Google Business Profile. |
| Google AI Mode | City-level | Google’s conversational search, run against a named location rather than a generic national query. | Website content and structure. |
| Google Gemini | Point-levelMeasured at every grid point | Two surfaces, measured differently. The businesses Gemini surfaces from Google’s listings are read at every point of the grid — its Maps grounding takes coordinates, and in our nine-point test across one metro it named a substantially different set at each. The websites it cites are read once for the market: we have not measured whether that layer varies inside a metro, so we do not grid it. | Google Business Profile, and reviews specifically. Holding Google rank fixed at 1-10, Gemini named 29% of businesses under 50 reviews and 75% of those past 200, and the pattern held in crowded and thin markets alike. It is the one lever we have measured moving an engine. |
| Claude | Not run locally | Nothing local. Claude’s API returns cited links and no place data — the map results you see in the Claude app come from Google Places, which the API does not expose. We drop it from local projects rather than bill you for a reading that cannot see a place. It runs in full on national projects. | Website content and structure, at national scope. |
| Perplexity | Not run locally | Nothing local. Perplexity has no map surface, and its location signal did not separate one metro from another in our testing — so we drop it from local projects rather than present national results as local ones. It runs in full on national projects. | Website content and structure, at national scope. |
A local project measures four of the six engines. Claude's API returns no place data, and in our own testing Perplexity's answers didn't differ between one metro and the next — so neither runs on local projects. Both run in full on national ones. Where an answer draws on Google's business listings rather than the open web, the lever is your client's Google Business Profile, not their website.
Find the neighbourhoods where the AI names someone else.
Grid tracking is on Pro and Agency · A gridded prompt counts as one tracked prompt