Restaurants & food

What does AI tell diners about you?

More and more diners open an AI assistant instead of a search tab when they ask "where should we eat". If the answer doesn't name you, you never enter the conversation — and unlike a search ranking, there's no dashboard telling you.

AI is the new restaurant guide

People planning a night out, a birthday dinner, or a trip to a new city ask AI for recommendations. The response they get is often the shortlist they work from. No scrolling through Google Maps reviews. No checking multiple sites. Just "AI said these are the best" and a booking.

Your visibility in these AI responses depends on your online presence — reviews, website content, press coverage, social media. But each AI model weighs these differently, and the results change as models are updated.

What AI visibility tracking tells a restaurant

Whether you're on the shortlist at all

Assistants typically name three to five places, not ten blue links. That is a far shorter list than page one of Google, so the difference between being included and excluded is much starker than a drop in search ranking.

Which neighbours are taking your covers

Every run names the restaurants recommended in your place. Over a few weeks you get a clear picture of the handful of local venues AI treats as the default answer in your area and cuisine.

Whether the details are right

Assistants confidently state cuisines, price bands, opening hours and whether you take walk-ins. When that information is out of date it costs you bookings quietly, and it is worth catching. Every response is stored so you can check.

Which sources the answer came from

On the web-grounded models we capture the URLs behind each recommendation. That tells you whether it is your own site, a review platform or a local listicle deciding what AI says about you.

What actually drives restaurant visibility

Hospitality is a third-party category. Assistants answering "where should we eat" lean on sources about you far more than on anything you publish yourself, which makes the fix list different from a typical business.

Review platforms and maps listings. Volume, recency and the actual wording of reviews all feed the picture. A venue with fifty recent, specific reviews reads very differently to a model than one with two hundred old generic ones.
Local roundups and press. "Best small plates in Leeds" articles are disproportionately powerful, because they are exactly the pages a grounded model retrieves when asked the same question.
A crawlable site that states the obvious. Cuisine, neighbourhood, price band, dietary options and booking policy in plain text, not baked into an image or a PDF menu. Models cannot read your beautiful menu graphic.
Consistency across listings. Conflicting hours or addresses between your site, maps and booking platforms make a model less likely to state anything about you confidently — and a hedged mention rarely converts.

Prompts that matter for restaurants

"Best [cuisine] restaurants in [your area]"

"Where to eat in [your city] for a special occasion"

"Good restaurants near [local landmark or attraction]"

"Restaurants in [your area] with outdoor seating"

Run these across 13+ models and see which restaurants AI recommends in your area.

See what AI tells your diners

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Common questions from restaurants

Do AI assistants really influence restaurant bookings?

They influence the shortlist, which is the part that matters. An assistant asked where to eat typically names three to five venues rather than returning a page of links, and diners tend to work from that shortlist rather than starting a fresh search. You do not need AI to replace search for this to matter; you only need it to be the first place a proportion of your customers ask.

I am a single independent restaurant. Is this relevant, or is it for chains?

It is arguably more relevant for independents. Chains are named because they are ubiquitous; independents are named because specific third-party sources describe them well. That means the levers are within reach — reviews, a local roundup, a clear site — rather than requiring national brand spend. The free audit will tell you where you stand in about thirty seconds without any signup.

How is this different from just Googling my restaurant?

Searching once tells you what one model said on one day in response to one phrasing. Assistants vary their answers by model, by wording and over time, so a single check is close to meaningless. Cited Monitor runs a fixed set of prompts across models on a schedule so you can see the pattern and, more importantly, the trend.

Which prompts should a restaurant track?

Start with four to six that map to real booking intent: your cuisine plus your area, occasion-led phrasings such as birthday or anniversary dinners, a proximity prompt tied to a nearby landmark or venue, and one dietary or format prompt if it applies to you, such as vegan or private dining. Broad prompts like "best restaurants in London" are usually a waste of a run — you will not appear, and the answer tells you nothing you can act on.

AI got my opening hours wrong. Can I fix that?

Not directly, because there is no mechanism to correct a model. What you can do is fix the sources it reads, since incorrect details almost always trace back to a stale listing on a maps profile, a booking platform or an old page on your own site. Cited Monitor stores the full response and, on grounded models, the source URLs, so you can find which one is wrong instead of guessing.

What does it cost?

There is a free tier, and paid plans start at £29 a month. You connect your own API keys and pay the model providers directly at their published rates, which for a handful of restaurant prompts run weekly is typically a matter of pence. There is also a free audit that needs no signup and no API key at all.

Does AI recommend your restaurant?

When diners ask AI where to eat, find out whether it names you.

Free · no credit card · no API key needed · results in ~30 seconds