Users no longer just "search" for hotels — they simply "ask AI". When a traveler asks ChatGPT "a boutique hotel for couples near the Bund in Shanghai" or "family-friendly resorts in Tokyo", does your hotel get recommended? GEOScope uses an 8-dimension entity score plus a market-level AI probe to measure your hotel's recommendation rate, competitor performance and citation sources across ChatGPT, Gemini, Doubao, DeepSeek, Perplexity and more — and hands you a free "you vs 3 competitors" snapshot.
Definition
Key Findings
Comparison Table (sample, demo data)
| Hotel | AI Visibility | Share of Voice | Strongest Intent |
|---|---|---|---|
| Your hotel (sample) | 37 | 46% | Family 67% |
| Competitor A (sample) | 64 | 92% | Luxury 88% |
| Competitor B (sample) | 62 | 77% | Business 81% |
| Competitor C (sample) | 64 | 100% | Romantic 90% |
Above is V1 demo data (generated by a deterministic market-level probe) to illustrate the scoring model and comparison structure. Enter your real hotel name + city and we generate a dedicated snapshot from your Entity Graph and competitor set.
Travel Intents
The same hotel can look wildly different across intents — which is exactly why the "strongest / weakest intent" split in your snapshot matters.
AI Engines
GEOScope's market-level probe covers multiple engines in one pass, avoiding the misjudgment of "testing only one engine". The free snapshot covers ChatGPT / Gemini / Doubao by default; paid plans expand to more engines.
Free Snapshot
Enter your hotel name + city, and GEOScope will:
① Resolve your GEOScope Entity (globally unique entity ID);
② Automatically discover 3 direct competitors in the same market;
③ Run 15 market-level AI probes to produce your recommendation rate, Share of Voice, per-engine mentions and strongest / weakest intent vs those 3 competitors.
No login, free. Upgrade to Starter to unlock 6 deep attribution factors for "why competitors get recommended more".
Methodology
Coming online · City AI Visibility Index
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