Original research

AI Local Visibility Index

We asked ChatGPT, Claude, Gemini and Google AI Overviews for local recommendations across 48 US markets, then checked their answers against the businesses Google Maps actually returns for the same query. 11.94% got named. The rest are invisible.

Measured and written by Abdullah İskifoğlu, founder of LocalSeen. Collected 2026-08-15/16.

88.1%

of the businesses Google Maps returns were named by no AI engine at all

4,657

businesses in the denominator, across 48 markets

1,440

AI answers collected, 0 dropped

Ask an assistant for the best dentist in your city and it names two or three businesses. Google Maps returns a hundred. This study measures the gap between those two numbers, in one direction only: of the businesses Google is willing to show, how many does an AI ever say out loud?

Across 48 markets the answer is 11.94%. Roughly one business in eight. The other seven exist, rank, collect reviews, and never enter the conversation.

By engine

Each engine was asked the same three questions per market. A business counts once per engine no matter how many times it was named.

EngineBusinesses namedShare of 4,657
Google AI Overviews3768.07%
ChatGPT2064.42%
Gemini1342.88%
Claude921.98%
Named by at least one55611.94%

What we found

Google's own AI is the most generous, and it still leaves out 92%. AI Overviews named 376 of the 4,657 businesses, 4.1 times as many as Claude, the most selective engine. If you assume the assistants broadly agree with each other, the data says otherwise.

Category decides more than city does. Across eight cities the spread was narrow, but between categories it was not: the most-named category reached 14.38% while the least reached 9.71%. Being a hair salon is a harder starting position than being a gym, everywhere we looked.

Chat models describe; Google's AI names. When we ran the same answers through a looser matcher, the count more than doubled for the chat models and barely moved for AI Overviews. Chat assistants talk about businesses in prose more often than they name them, which matters if you are trying to be the name that gets said.

And a result against us. Our own product matcher, applied to these answers, reported 26.11% instead of 11.94%. We found that while running this study, published both numbers, and fixed the matcher. The lower number is the one we stand behind.

How we measured

The rules below were written down and frozen before any data was collected. They did not change after we saw the result. One amendment was made mid-study and is recorded with its reason and its date.

The question
Of the businesses Google Maps returns for a buyer query, what share does an AI assistant name when asked the same thing? Not “what share of all local businesses” — that denominator is unknowable without a registry, and a number whose denominator is unstated cannot be checked.
Markets
8 mid-size US cities × 6 categories = 48 markets. Austin, Denver, Portland, Nashville, Columbus, Raleigh, Tucson and Boise; dentist, gym, plumber, coffee shop, law firm and hair salon.
Denominator
One Google Maps query per market (“category in city”, pinned to the city coordinate, depth 100), deduplicated by name. 4,657 businesses in total.
Numerator
Three fixed question phrasings per market, asked to four engines. Three samples per question for the chat models, which are not deterministic; one for AI Overviews, which is a search result and repeats itself. 1,440 answers, 0 dropped.
What counts as named
The business name has to appear in the answer as a phrase. Named once, in any sample, counts: the question is whether a business is reachable through AI at all.
Cost
$1.40 of API spend for the published pass, under a hard cap that stops the collector before it can overspend.

The complete dataset, including every business name and which engine named it, is published as a single JSON file. Raw model answers are not republished: they contain the models' own evaluative claims about named real businesses, and we are not willing to amplify statements we cannot verify. They are available on request for methodology review.

Every market

All 48 rows, so the headline number can be recomputed from the parts. “Named” is the strict count; the looser matcher is shown beside it.

CategoryCityGoogle returnsNamedShareUpper bound
coffee shopAustin, TX931010.75%21.51%
gymAustin, TX961717.71%32.29%
dentistAustin, TX9699.38%33.33%
plumberAustin, TX981111.22%17.35%
dentistDenver, CO1001414.00%36.00%
hair salonAustin, TX991616.16%28.28%
law firmAustin, TX10077.00%25.00%
gymDenver, CO951616.84%33.68%
coffee shopDenver, CO871314.94%20.69%
plumberDenver, CO991313.13%34.34%
law firmDenver, CO10099.00%23.00%
hair salonDenver, CO10099.00%20.00%
dentistPortland, OR1001010.00%31.00%
plumberPortland, OR971212.37%28.87%
gymPortland, OR921516.30%35.87%
coffee shopPortland, OR931313.98%19.35%
law firmPortland, OR1001313.00%18.00%
dentistNashville, TN1001111.00%35.00%
hair salonPortland, OR9988.08%19.19%
plumberNashville, TN951212.63%32.63%
gymNashville, TN971010.31%27.84%
coffee shopNashville, TN961212.50%15.63%
law firmNashville, TN9999.09%25.25%
dentistColumbus, OH991010.10%27.27%
hair salonNashville, TN991111.11%26.26%
plumberColumbus, OH961313.54%31.25%
gymColumbus, OH961313.54%31.25%
hair salonColumbus, OH9788.25%22.68%
dentistRaleigh, NC991313.13%38.38%
law firmColumbus, OH991010.10%21.21%
coffee shopColumbus, OH941313.83%23.40%
plumberRaleigh, NC971010.31%27.84%
gymRaleigh, NC971212.37%25.77%
law firmRaleigh, NC1001313.00%21.00%
coffee shopRaleigh, NC941819.15%28.72%
hair salonRaleigh, NC1001010.00%18.00%
dentistTucson, AZ981212.24%29.59%
gymTucson, AZ881314.77%26.14%
plumberTucson, AZ1001111.00%27.00%
law firmTucson, AZ10088.00%20.00%
hair salonTucson, AZ9977.07%18.18%
coffee shopTucson, AZ871517.24%21.84%
dentistBoise, ID981212.24%30.61%
gymBoise, ID971313.40%28.87%
law firmBoise, ID1001111.00%23.00%
plumberBoise, ID981313.27%25.51%
coffee shopBoise, ID941010.64%23.40%
hair salonBoise, ID10088.00%22.00%

What this does not tell you

  • The denominator is capped. Google Maps was queried at depth 100 and several markets came back with exactly 100 rows, meaning the real market is larger than what we counted. The share we publish is therefore an upper bound against “all local businesses”, and it is not comparable to third-party figures that use a wider, unstated denominator.
  • One country, one language. US cities, English questions. It says nothing about other markets.
  • One day. A single collection pass. No trend claim is possible from it.
  • Google Maps is not a registry. It is itself a ranked, filtered surface. The denominator is “what Google shows”, not “what exists”.
  • Cheap model tiers. Each engine was queried at the tier we run in production. A larger model may answer differently.

Update history

Pass 1 — collected 2026-08-15 (superseded)

12.22% named, 26.54% under the looser matcher. Recorded the per-engine breakdown with the looser matcher only, and asked the question without naming the country. Kept here because a study that hides its earlier run is asking to be trusted rather than checked.

Pass 2 — collected 2026-08-15/16 (published)

11.94% named, 26.11% under the looser matcher. Per-engine strict counts, country named in the question, raw answers retained.

The two passes differ by 0.28 percentage points. That gap is the best evidence we can offer that the measurement is stable rather than a lucky draw.

Find out where your business sits

Run the same questions against your own category and city.