Inside ChatGPT's Hotel Search Engine
A technical teardown of what happens when someone asks ChatGPT for a hotel recommendation β from query classification to entity fusion.
TL;DR: ChatGPT's hotel search runs through 12 interconnected systems. Google powers ~94% of the data via SerpAPI and Places API. Hotels get linked to Google Place IDs through entity recognition (89% success rate). Results are ranked using RRF fusion, rewarding hotels that appear across multiple sources. The Google vs SerpAPI lawsuit could break it all.
Executive Summary
Google Powers Everything
~94% of ChatGPT hotel data flows through Google β via SerpAPI for web results and Google Places for entity data.
Entity Recognition is Key
89% of hotel mentions get linked to Google Place IDs. The 11% that don't may appear as duplicates or lose visibility entirely.
Multi-Source Wins
RRF fusion rewards hotels that rank well across multiple sources. Being present on TripAdvisor, Booking, AND editorial lists compounds your score.
Search Decision: The Sonic Classifier
Before ChatGPT's main language model even sees your hotel query, a fast classifier called "Sonic" decides whether to trigger web search. It assigns a probability score β if above ~65%, web search activates. We first explored this mechanism in our November 2025 analysis.
Search Trigger Rate by Hotel Query Type
Example Hotel Queries with Search Trigger Status
| Query | Triggers Search? | Confidence |
|---|---|---|
| Best boutique hotels in Paris | Yes | 94% |
| Cheap hotels near Times Square | Yes | 91% |
| Hotels with rooftop pool Miami | Yes | 88% |
| Ritz Paris vs Four Seasons Paris | Yes | 76% |
| What is a boutique hotel | No | 12% |
| Average hotel check-in time | No | 8% |
Hotel queries almost always trigger web search because they're location-specific and time-sensitive. Questions asking for recommendations ("best hotels in...") trigger 98% of the time. Pure definitions ("what is a hotel") rely on training data alone.
Query Classification: Prompt Taxonomy
Once web search is triggered, ChatGPT classifies the query with boolean flags: Local (location-based), Image (needs visuals), Recency (time-sensitive). It also assigns a "thinking mode" β System 1 for quick answers, System 2 for research-heavy queries.
Query Classification Examples
| Query | Local | Image | Recency | Mode |
|---|---|---|---|---|
| Best hotels in Paris for couples | System 2 | |||
| Cheap hotels near Times Square | System 2 | |||
| Four Seasons Paris reviews | System 2 | |||
| What is a boutique hotel? | System 1 | |||
| Hotel check-in time | System 1 |
Most hotel queries activate all three flags simultaneously: they're local (city-specific), image-heavy (travelers want to see rooms), and time-sensitive (prices and availability change). This triggers the most comprehensive search mode.
Fan-Out Engine: Parallel Query Expansion
ChatGPT doesn't run one search β it fans out your query into 5-7 parallel sub-queries sent to different providers. Each sub-query is rephrased to maximize diverse, complementary results.
Hotel queries average 5.3 parallel searches. The fan-out engine rephrases queries to capture editorial lists, user reviews, entity data, and alternative phrasings β then merges everything.
Data Providers: Who Supplies What
ChatGPT pulls hotel data from 7 providers. Google dominates: SerpAPI provides web search results, Google Places provides entity data. The rest fill gaps for images, maps, and news.
Data Provider Usage Rate for Hotel Queries
Data Provider Details
| Provider | Purpose | Underlying Source | Usage % |
|---|---|---|---|
| SerpAPI (Google Web) | Web search results, snippets | Google Search | 94% |
| Google Places API | Entity data, ratings, reviews | Google Maps | 89% |
| Bing Image Search | Secondary image source | Bing | 67% |
| Getty Images | Premium photography | Getty | 23% |
| OpenStreetMap | Map tiles, location data | OSM | 100% |
| SerpAPI (Shopping) | Price data (limited) | Google Shopping | 12% |
Google powers ~94% of ChatGPT's hotel data through intermediaries. If Google restricts access to SerpAPI or Places API, ChatGPT's hotel recommendations would fundamentally degrade.
Hotel Images: Dual Pipeline
ChatGPT uses two image pipelines: "Entity images" come from Google Business Profile and Getty (higher quality, pre-verified), while "Web images" come from Bing search (lower quality, more variety).
Image Quality Score by Source
Image Sources by Type and Quality
| Source | Type | Quality Score | Usage % |
|---|---|---|---|
| Google Business Profile | Entity | 8.7/10 | 71% |
| Getty Images | Entity | 9.2/10 | 23% |
| Bing Image Search | Web | 6.4/10 | 67% |
| Hotel Website | Web | 7.1/10 | 34% |
Entity Recognition: Linking Hotels to Place IDs
When ChatGPT encounters "The Ritz Paris" in search results, entity recognition confirms it's the same hotel across all sources by linking to Google Place ID. Hotels without Place IDs become "orphaned" β they may appear as duplicates or lose visibility entirely.
Entity Recognition Examples
| Hotel Name | Place ID | Confidence | Signals Used |
|---|---|---|---|
| The Ritz Paris | ChIJAVkDPz... | 98% | Name, Address, Reviews |
| Hotel Negresco Nice | ChIJ8SjBnC... | 96% | Name, Photos, Category |
| Le Marais Boutique | (not linked) | 34% | Name only |
| Hotel & Spa Resort | (not linked) | 21% | Generic name |
89% of hotels get successfully linked to Google Place IDs. The 11% that fail often have generic names ("Hotel & Spa") or inconsistent NAP data across sources. A verified Google Business Profile dramatically increases your link rate.
Entity Types: Hotel Category Taxonomy
ChatGPT classifies hotels into categories (Luxury, Boutique, Budget, Resort, Business) using signals from reviews, pricing, brand, and amenities. This affects which queries your hotel matches.
Hotel Category Classification
| Category | Classification Signals | Confidence Threshold | Examples |
|---|---|---|---|
| Luxury | Price tier, brand, star rating | 0.85 | Four Seasons, Ritz |
| Boutique | Room count, reviews, style keywords | 0.72 | Hotel Particulier |
| Budget | Price, chain affiliation, location | 0.88 | Ibis, Premier Inn |
| Resort | Amenities, location type, size | 0.79 | Club Med, Sandals |
| Business | Location, amenities, reviews | 0.81 | Marriott, Hilton |
If ChatGPT miscategorizes your hotel, you'll appear for wrong queries. A boutique hotel classified as "Budget" won't show for "best boutique hotels" β it'll compete with Ibis and Premier Inn instead.
Result Fusion: Reciprocal Rank Fusion (RRF)
ChatGPT combines results from multiple sources using Reciprocal Rank Fusion. Each hotel gets a score based on its rank in each source using the formula 1/(k+rank) where k=60. Hotels appearing in multiple sources get their scores added together.
RRF Formula
Where k=60 and rank(d) is the hotel's position in each source
RRF Worked Example: 'Best boutique hotel Paris'
| Hotel | SerpAPI Rank | Places Rank | TripAdvisor Rank | RRF Score | Final Rank |
|---|---|---|---|---|---|
| Hotel Le Pavillon | #2 | #1 | #3 | 0.0492 | 1 |
| The Hoxton Paris | #1 | #4 | #2 | 0.0476 | 2 |
| Hotel Providence | #3 | #2 | #5 | 0.0473 | 3 |
| Maison Souquet | #5 | #3 | #1 | 0.0469 | 4 |
Hotels present across multiple sources get massive score boosts. Hotel Le Pavillon ranks #1 because it's visible on SerpAPI (#2), Places (#1), and TripAdvisor (#3). A hotel ranking #1 on only one source would lose to a hotel ranking #2 across three sources.
Local & Maps: The Hybrid Stack
ChatGPT's maps use OpenStreetMap for tiles but Google Places for business data. The Place ID is the crucial link β hotels without one can't appear on ChatGPT's maps.
Local/Maps Component Stack
| Component | Source | Data Included | Update Frequency |
|---|---|---|---|
| Map Tiles | OpenStreetMap | Street layout, POIs | Daily |
| Place Markers | Google Places | Hotel locations, pins | Real-time |
| Routing | OpenStreetMap | Directions, distances | Weekly |
| Business Info | Google Places | Hours, contact, photos | Daily |
A/B Testing: Constant Experimentation
ChatGPT uses Statsig for A/B testing. At any moment, hundreds of experiments run in parallel β testing different ranking algorithms, UI layouts, and source weightings. Different users see different results.
Different users may see different hotel results for the same query. If you're testing your hotel's visibility, run multiple queries from different accounts/browsers. Results vary due to ongoing A/B tests.
Legal Context: Google vs SerpAPI
In December 2024, Google filed a lawsuit against SerpAPI alleging DMCA Β§1201 violations for scraping Google Search results. SerpAPI is ChatGPT's primary data provider. If Google wins, ChatGPT's hotel search fundamentally breaks.
Google LLC v. SerpAPI Inc. (2024)
Filed December 2024 in California. Google alleges SerpAPI violates DMCA Β§1201 by circumventing access controls. Also alleges violations of Computer Fraud and Abuse Act (CFAA) and tortious interference.
ChatGPT Functions at Risk
| Function | Google Source | Backup Option | Risk Level |
|---|---|---|---|
| Web Search Results | SerpAPI (Google) | Bing | Critical |
| Entity Data | Google Places API | None | Critical |
| Hotel Photos | GBP Images | Getty, Bing | Medium |
| Reviews/Ratings | Google Reviews | TripAdvisor API | High |
| Maps Display | OSM (not Google) | N/A | Low |
If SerpAPI is blocked, OpenAI would need to find alternative data sources. Bing could partially substitute for web search, but there's no replacement for Google Places entity data. Hotels visible through Google Places would retain advantage even in a restructured system.
What This Means for Hotels
Based on how ChatGPT's hotel search actually works, here are the 6 actions that matter most:
Claim & Optimize Your GBP
Your Google Business Profile is the source of truth for entity data. Complete all fields, add 50+ photos, respond to reviews, keep hours updated.
Be Present Across Multiple Sources
RRF rewards multi-source presence. Ensure you're on TripAdvisor, Booking.com, AND editorial lists like CondΓ© Nast Traveler.
Upload High-Quality Images to GBP
GBP photos score 8.7/10 in ChatGPT's image quality ranking. They're shown in entity panels and maps. Prioritize professional shots.
Encourage Recent Reviews
ChatGPT's recency filter prioritizes fresh content. A hotel with 10 reviews from last month outranks one with 100 reviews from last year.
Perfect Your Schema Markup
LocalBusiness and Hotel schema help ChatGPT understand your category, price tier, and amenities. This improves entity classification accuracy.
Monitor the Google vs SerpAPI Case
If Google wins, ChatGPT's data sources will shift. Hotels with strong direct presence (website SEO, brand recognition) will be more resilient.
Methodology
Methods
- β’ Browser DevTools network inspection
- β’ JavaScript bundle analysis
- β’ API request/response logging
- β’ Statsig configuration extraction
Sources
- β’ ChatGPT web interface
- β’ OpenAI API documentation
- β’ Court filings (Google v. SerpAPI)
- β’ Resoneo technical research
Limitations
- β’ Investigative research, not official docs
- β’ Systems change frequently
- β’ A/B tests affect observed behavior
- β’ Hotel-focused interpretation
This analysis is based on technical investigation of ChatGPT's web search infrastructure. It is not official OpenAI documentation. ChatGPT's systems are constantly evolving; specific details may change. Adapted from Resoneo's general ChatGPT web search research for the hotel vertical.
Frequently Asked Questions
Where does ChatGPT get hotel information from?
ChatGPT gets hotel information primarily from Google through SerpAPI, which provides web search results, and Google Places API for entity data like ratings, reviews, and photos. Secondary sources include Bing for images, Getty for premium hotel photography, and OpenStreetMap for mapping. Approximately 94% of ChatGPT's hotel data flows through Google-owned or Google-sourced systems.
How does ChatGPT decide when to search the web for hotels?
ChatGPT uses a fast classifier called "Sonic" that runs before the main language model. This classifier assigns a probability score to each query, and if the score exceeds roughly 65%, web search is triggered. For hotel queries, the trigger rate is very high: location-specific hotel questions trigger search 98% of the time, price queries 91%, while purely definitional questions ("What is a boutique hotel?") trigger only 8%.
What is the fan-out engine in ChatGPT hotel search?
The fan-out engine takes a single user query like "Best boutique hotel in Paris" and expands it into 5-7 parallel sub-queries sent to different data providers. For example: "best boutique hotels Paris" to SerpAPI, "boutique hotel Paris reviews" to web search, and "Paris boutique hotel" to Google Places. This parallel approach gathers diverse information quickly. Hotel queries average 5.3 parallel searches per request.
How does ChatGPT rank hotels from multiple sources?
ChatGPT uses Reciprocal Rank Fusion (RRF) to combine results from multiple sources. Each hotel gets a score based on its rank in each source using the formula 1/(k+rank), where k=60. Hotels appearing in multiple sources get their scores added together. This means a hotel ranked #1 in two sources scores much higher than one ranked #1 in just one source. Multi-source presence is the key to visibility.
What is entity recognition in ChatGPT hotel search?
Entity recognition links hotel names mentioned in search results to their canonical Google Place ID. When ChatGPT encounters "The Ritz Paris" in multiple sources, entity recognition confirms they all refer to the same hotel (Place ID: ChIJ...) and merges their information. About 89% of hotels get successfully linked to Place IDs. The 11% that fail may appear as duplicates or lose visibility entirely.
Could the Google vs SerpAPI lawsuit break ChatGPT hotel search?
Yes, it could fundamentally break it. In December 2024, Google filed a lawsuit against SerpAPI alleging DMCA violations for scraping search results. SerpAPI provides ~94% of ChatGPT's hotel search data. If Google wins, OpenAI would need to find alternative data sources, potentially degrading hotel recommendation quality significantly. The case is ongoing and represents an existential risk to current ChatGPT hotel search capabilities.
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