AI visibility for restaurants
For restaurants and the agencies that market them · industry guide, covers 15 US metros · part of the AI Visibility Index data series
When a customer asks ChatGPT, Gemini or Perplexity for a restaurant recommendation, the assistant answers from the public data layer it can see: business profiles, listings, reviews, and the words on your website. If that layer is thin or inconsistent, the answer names someone else — and the loss is invisible, because nobody tells you what the AI said.
FoundNow measures that layer for restaurants. We ask the assistants the questions real buyers type, several times each, and report how often a business is mentioned as a rate with a 95% confidence interval — never a single-number claim. Every sample records which sources the assistant cited and which model version produced the answer.
What a report shows for restaurants
- Mention rate reported per engine, each as a rate with a 95% confidence interval
- The exact engines and model versions used are listed in the report itself
- The sources each assistant actually cited — profiles, directories, review sites, your own site
- The same measurement for one competitor, so the gap is concrete
- A fix list ordered by impact and effort: profile completeness, listings consistency, schema and on-site answers
Questions restaurant customers ask AI assistants
Three examples from the restaurants question set (the full set is part of the product, not this page):
- “best brunch spots downtown”
- “family friendly restaurants near me”
- “best steakhouse in”
Metros we sample for restaurants
Every metro below has a dedicated page describing what we measure there:
restaurants in Austin, TX · restaurants in Dallas, TX · restaurants in Houston, TX · restaurants in Denver, CO · restaurants in Phoenix, AZ · restaurants in Seattle, WA · restaurants in Portland, OR · restaurants in Chicago, IL · restaurants in Atlanta, GA · restaurants in Miami, FL · restaurants in Charlotte, NC · restaurants in Nashville, TN · restaurants in Columbus, OH · restaurants in Philadelphia, PA · restaurants in Sacramento, CA
Frequently asked questions
How do AI assistants decide which restaurant to recommend?
Assistants answer from the public data layer they can see when the question is asked: business profiles, listings, reviews, and the words on your website. We do not speculate about each assistant's internal ranking - we measure the outcome directly: how often a specific business is named, in how many runs, with which sources cited.
What does a FoundNow report show for a restaurant?
Mention rate by engine - each reported as a rate with a 95% confidence interval, with the exact engines and model versions used listed in the report itself; the sources the assistants cited; the same measurement for one competitor, so the gap is concrete; and a fix list ordered by impact and effort - profile completeness, listings consistency, schema and on-site answers.
Which cities are covered?
The restaurants question set currently covers 15 US metropolitan areas, listed on this page. A one-time scan or audit covers one business location; monthly plans cover 1 to 40 locations.
What questions do restaurant customers type into AI assistants?
Buyer language such as "best brunch spots downtown", "family friendly restaurants near me" or "best steakhouse in". Our question sets are built from this real buyer language - the three samples on this page come from the restaurants question pack, and the full set is part of the product, not this page.
Method details (sampling, intervals, model versions): methodology page.