Hotel visibility evidence

Be the hotel AI recommends.

Put your property front and centre when travellers ask AI where to stay.

Ask

booking scenarios

Use structured booking-decision scenarios, not keyword traffic.

Compare

named rivals

See which hotel AI can defend first.

Fix

source gaps

Know what proof to tighten before the next run.

Create account View pricing

Live signupNo access request

Start here

Create account. Add hotel. Run benchmark.

Choose Entry or Pro during setup. The workspace opens as soon as your account is ready.

1. Account

2. Hotel

3. Benchmark

Create account Compare plans

Sample benchmark deliverable

Not just who won. Why AI chose them, what proof mattered, and what to fix next.

The sample shows the service layer behind Rose-Brook: multi-model agreement, competitor pressure, prompt evidence, source gaps and an operator-ready action brief.

Booking-decision scenario

"Best Manchester hotel for a family theatre weekend with parking near the Palace Theatre?"

Fictional sample data, real hotel names

AI Booking Preference

46/100

Would an assistant choose your hotel for this stay?

Market rank

#3 of 6

Direct comparison against hotels guests also consider.

Model agreement

1 of 3

Only one model could defend your hotel first.

Commercial verdict

Your hotel appears in the answer, but competitors are easier for AI to recommend.

The deciding issue is not whether the hotel is good. It is whether public evidence makes the stay easier to explain, compare and defend.

1. The Midland Manchester

AI chose first

78/100

Parking, central arrival and event-weekend proof were easiest to defend.

2. Kimpton Clocktower Hotel

Strong rival

64/100

Landmark location and station access were clearer in the answer.

3. Your hotel

Lost choice

46/100

Visible, but AI could not confidently defend parking and family-stay proof.

Model agreement

Same question, different assistants

Fragmented

ChatGPT

Chose The Midland

Clearer theatre access and parking language.

Gemini

Chose Kimpton Clocktower

Station proximity and landmark proof stood out.

Claude

Considered your hotel

Good location fit, but family-room evidence was thin.

What the service adds

Demand segments

See which stay occasions you win, lose or only partly support.

Prompt evidence

Open the exact prompts and model answers behind the verdict.

Source diagnosis

Separate hotel quality issues from public-evidence problems.

Action brief

Turn the benchmark into fixes for website copy, listings and proof.

Evidence gaps

The benchmark shows why the recommendation broke.

Parking proof

Weak source support

AI found mentions, but not the arrival detail a family would need.

Family stay proof

Competitor clearer

Rivals had easier-to-repeat wording around rooms and weekend fit.

Theatre-weekend positioning

Missing comparison hook

The hotel was visible, but not framed as the safest answer.

Fix-first brief

A practical action plan before the next run.

Add a theatre-weekend landing block with parking, arrival and family-room specifics.

Make the same proof consistent across the hotel website, Google profile and OTA descriptions.

Run the next benchmark against the same competitor field to see whether the gap closes.

What changes after this

The next benchmark tests the same stay intent and competitor field, so movement is visible instead of anecdotal.

Plans

Choose monthly or weekly evidence.

£39 Entry

Monthly ChatGPT benchmark.

£99 Pro

Weekly multi-model monitoring.

£79 Founder rate

Launch offer for early hotels.

Detailed limits and billing notes live on the pricing page.

Compare plans

Hotel visibility evidence

Be the hotel AI recommends.

Put your property front and centre when travellers ask AI where to stay.

Ask

booking scenarios

Use structured booking-decision scenarios, not keyword traffic.

Compare

named rivals

See which hotel AI can defend first.

Fix

source gaps

Know what proof to tighten before the next run.

Create account View pricing

Live signupNo access request

Start here

Create account. Add hotel. Run benchmark.

Choose Entry or Pro during setup. The workspace opens as soon as your account is ready.

1. Account

2. Hotel

3. Benchmark

Create account Compare plans

Sample benchmark deliverable

Not just who won. Why AI chose them, what proof mattered, and what to fix next.

The sample shows the service layer behind Rose-Brook: multi-model agreement, competitor pressure, prompt evidence, source gaps and an operator-ready action brief.

Booking-decision scenario

"Best Manchester hotel for a family theatre weekend with parking near the Palace Theatre?"

Fictional sample data, real hotel names

AI Booking Preference

46/100

Would an assistant choose your hotel for this stay?

Market rank

#3 of 6

Direct comparison against hotels guests also consider.

Model agreement

1 of 3

Only one model could defend your hotel first.

Commercial verdict

Your hotel appears in the answer, but competitors are easier for AI to recommend.

The deciding issue is not whether the hotel is good. It is whether public evidence makes the stay easier to explain, compare and defend.

1. The Midland Manchester

AI chose first

78/100

Parking, central arrival and event-weekend proof were easiest to defend.

2. Kimpton Clocktower Hotel

Strong rival

64/100

Landmark location and station access were clearer in the answer.

3. Your hotel

Lost choice

46/100

Visible, but AI could not confidently defend parking and family-stay proof.

Model agreement

Same question, different assistants

Fragmented

ChatGPT

Chose The Midland

Clearer theatre access and parking language.

Gemini

Chose Kimpton Clocktower

Station proximity and landmark proof stood out.

Claude

Considered your hotel

Good location fit, but family-room evidence was thin.

What the service adds

Demand segments

See which stay occasions you win, lose or only partly support.

Prompt evidence

Open the exact prompts and model answers behind the verdict.

Source diagnosis

Separate hotel quality issues from public-evidence problems.

Action brief

Turn the benchmark into fixes for website copy, listings and proof.

Evidence gaps

The benchmark shows why the recommendation broke.

Parking proof

Weak source support

AI found mentions, but not the arrival detail a family would need.

Family stay proof

Competitor clearer

Rivals had easier-to-repeat wording around rooms and weekend fit.

Theatre-weekend positioning

Missing comparison hook

The hotel was visible, but not framed as the safest answer.

Fix-first brief

A practical action plan before the next run.

Add a theatre-weekend landing block with parking, arrival and family-room specifics.

Make the same proof consistent across the hotel website, Google profile and OTA descriptions.

Run the next benchmark against the same competitor field to see whether the gap closes.

What changes after this

The next benchmark tests the same stay intent and competitor field, so movement is visible instead of anecdotal.

Plans

Choose monthly or weekly evidence.

£39 Entry

Monthly ChatGPT benchmark.

£99 Pro

Weekly multi-model monitoring.

£79 Founder rate

Launch offer for early hotels.

Detailed limits and billing notes live on the pricing page.

Compare plans