By industry

When someone asks AI about restaurants, are you recommended?

In Restaurants, customers no longer browse search results — they get one answer and pick a name from it. Scoorpe runs this sector's query set across four AI engines, measures whether you are in that answer, and shows you why you are not.

The questions your customers actually ask AI.

A sample from this sector's query set, with what happened to an anonymized customer in it.

  • Vegetarian restaurant in Beşiktaş
    Mentioned · yesRecommended · yes
    Who wins: you
  • Quiet place for a business lunch
    Mentioned · yesRecommended · no
    Who wins: Lokanta 1741
  • Places open late
    Mentioned · noRecommended · no
    Who wins: Karaköy Lokantası
  • Do they have a gluten-free menu?
    Mentioned · noRecommended · no
    Who wins: no clear winner

Sample of 4 from a set of 1,240. Run across ChatGPT, Perplexity, Gemini and Claude, weekly.

What we measure for restaurants

QUERY

Sector-specific query set

1,240 real customer questions, built around this sector's decision criteria — not copy-pasted.

ENTITY

Entity model

Services, people, locations and price signals mapped to this sector's schema.

TRUST

Trust signals

Credentials, licences, third-party citations and review networks — what decides who AI trusts.

RIVAL

Competitor set

We track the names AI actually recommends, not the competitor list you gave us.

LOCAL

Local coverage

Measured per neighbourhood, city and region — local authority decides answers here.

RISK

Claim verification

Every unsourced or conflicting claim is flagged — AI drops contradictory information entirely.

Benchmark

The average picture in restaurants

Most businesses in this sector are mentioned far more often than they are recommended. That gap is what costs you customers.

Mention rate · sector average79%
Recommendation rate · sector average33%
Recommendation rate · top quartile61%
Entity completeness · sector average64%

Based on aggregated, anonymized results from beta accounts in this sector. Updated monthly.

Restaurants
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