Multi-Location · Industry benchmark
Stage 0 · Framework live · benchmark pending

'Best biryani in Indiranagar' is asked of AI thousands of times a month. Is your kitchen in the answer?

CiteRank measures who AI engines recommend in restaurants — across ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, Microsoft Copilot, xAI Grok and Meta AI, in both grounded and ungrounded modes — and fixes why it isn't you.

Vertical benches currently run 3 engines; full tracking covers all 8.

8 × 2
Engines × modes (grounded + ungrounded)
250+
Restaurants buyer prompts per run
Weekly
Re-runs · 95% confidence bands
Who AI recommends today

The restaurants leaderboard, measured.

Top-5 brand mention rate, first-mention rate and Share of Voice — straight from the citerank-bench scoring tables, recomputed weekly.

Benchmark for Restaurants runs with our first customer in this vertical. The leaderboard below is an illustrative sample built from the blueprint's brand registry — numbers are placeholders, not measurements.

#
Brand
Mention rate
First-mention
Share of Voice
01
Meghana Foods
62%
31%
34%
02
Truffles
48%
22%
24%
03
Toit
39%
14%
18%
04
Byg Brewski
27%
9%
14%
05
Zomato (aggregator capture)
18%
5%
10%

Illustrative sample — benchmark not yet completed.

Sample prompt set

Real restaurants prompts, eight engines, one question: did you get cited?

Buyer prompt
ChatGPT
Gemini
Claude
Perplexity
AI Overviews
Microsoft Copilot
xAI Grok
Meta AI
You cited?
"best biryani in Indiranagar"
Not yet
"romantic rooftop restaurants in Bangalore under ₹3,000 for two"
Not yet
"vegan-friendly cafes in Koramangala open now"
Not yet
"{restaurant} — is parking available and how busy on Saturday night?"
Not yet
Where the citations leak

Three failure modes specific to multi-location.

01

Platform capture: Zomato, Urban Company, Practo intercept high-intent neighbourhood prompts.

02

Per-location entity hygiene is broken — the flagship is cited, the other 23 outlets aren't.

03

Hallucinated hours, prices and quotes leak trust before a guest ever walks in.

Methodology

How a restaurants benchmark gets built.

01
Onboard

URL, competitors, prompt seed list. 5 minutes.

02
Audit

250+ prompts × 8 engines × 2 modes, 10–30 replays each.

03
Fix

Schema, llms.txt, entity hygiene, content briefs — shipped.

04
Re-run

Weekly re-runs, deltas with 95% confidence bands.

Illustrative pattern

What a 90-day citation delta looks like.

Illustrative pattern based on our methodology — first customer results publish Q4 2026.

Before
1 / 25

Cited in 1 of 25 high-intent restaurants prompts across 8 engines (4%).

After · 90 days
9 / 25

Cited in 9 of the same 25 prompts (36%) — schema, llms.txt and entity fixes shipped over 3 weekly re-runs.

Case study

Restaurants case study publishes after the first 90-day window.

We don't ship anonymous success stories. Every case study card on this page becomes a real logo, real prompts and real deltas after the customer's 90-day window closes.

Insights

Coming in the Restaurants visibility report.

Article · in the restaurants report

Neighbourhood-prompt leaderboards: who AI feeds your customers to

Notify me when published
Article · in the restaurants report

Multi-outlet GEO: getting every location cited, not just the flagship

Notify me when published
FAQ

Restaurants questions, answered.

How is CiteRank different from Google Business Profile / aggregator listings?+

Those surfaces optimise for their own funnel. CiteRank measures how often ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, Microsoft Copilot, xAI Grok and Meta AI actually name your brand for restaurants buyer prompts, then fixes the schema, entity and content gaps so the engines have something to cite besides the aggregator.

Which segments / cities do you cover for this vertical?+

We start with your 250+ highest-intent buyer prompts — segmented by city, persona and price band as relevant — and expand the prompt graph weekly. Coverage scope is set during the 5-minute onboarding.

How fast to first new citation?+

First measurable citation lift is typically 3–5 weeks after the first fix pass (schema, llms.txt, entity, content briefs). Re-runs are weekly with 95% confidence bands so you can attribute deltas to specific fixes.

Is our data sent to LLMs?+

Only the public buyer prompts we run on your behalf reach the engines — exactly what a prospective customer would type. We do not send your CRM, customer data, or internal documents to any LLM.

How to start

Start with one snapshot. Scale into weekly monitoring.

Snapshot Audit
One-time

250+ prompts × 8 engines × 2 modes. Brand-mention report and gap analysis.

Monitor
Ongoing

Weekly re-runs, deltas with confidence bands, alerting on rank/citation changes.

Visibility + Fixes
Ongoing

Monitor + shipped fixes: schema, llms.txt, entity, content briefs and re-measurement.

Keep exploring

See the depth model and adjacent verticals.