Insights, analysis, and updates from the world of AI and hospitality
13 articles found
A deep dive into OpenAI’s Agentic Commerce Protocol (ACP) vs Google’s Universal Commerce Protocol, exploring how AI agents shift control over trust, intent, and transactions, and what this means for the future of digital commerce.
Since Nov 28, Hotel Ranque received 15 booking enquiries attributed to AI search. Some are niche (cycling), others are persona-driven (families). AI behaves as an early discovery and shortlist layer, and AEO also boosted SEO with 3 leads from Google.
Grok doesn’t “discover” hotels. Across 200 runs for a Bastille 3-star coffee prompt, it triangulates with 3–4 fanout searches, heavily constrained to TripAdvisor and Booking, then expands “coffee” into “breakfast” and “reviews”. Here’s what that means for hotel AEO and how to measure it.
We tracked ChatGPT’s fan-out searches for “chess, yoga, cycling near Ledru-Rollin” and used them as a blueprint to adapt the AEO strategy of Hotel Ranque.
We wired GPT-5.2 into our HotelRank tracking stack. In three days, we’re already seeing a reshuffle in AI visibility, a big jump in how many sources the model reads, and a clear cost bump for anyone building on top of GPT-5.2.
In Episode 3 of the Hotel Ranque series, I stop thinking in “keywords” and start from prompts. I show how we rebuilt the whole site, schema and FAQs around three ultra-specific AI queries and then tracked, per model, how often Hotel Ranque became the answer.
Using a ChatGPT app wired to Hotel Ranque, I prototype a full DIRECT-booking flow from natural-language search to room selection and confirmation, and share what this means for hotels in AI Search.
Over two days I created a website for a boutique hotel in Paris, wired it into the Google, and tracked how fast AIs picked it up. Episode 1 of the Hotel Ranque series shows how Hotel Ranque went from 0 to #1 in ChatGPT for niche trip queries in roughly 48 hours.
Hotelrank’s 2025 deep dive into how many French hotels actually have a Wikipedia page (just 2.4%), why they’re mostly palaces and landmarks, and what this bias means for SEO and AI visibility.
![Anatomy of a ChatGPT Hotel Web Search — How LLMs Rank, Cite & Retrieve [Nov 2025]](https://cdn.sanity.io/images/g4atoftp/production/d7dc579aef8546dadc6d2670f614c512787f5308-3446x1808.png?rect=116,0,3214,1808&w=400&h=225)
This teardown shows, line by line, how a simple query — “best hotels in New York” — triggers a dual search system that fuses editorial authority (Condé Nast, TripAdvisor) with structured Google Maps entities. Each hotel is now a verified data object (an "Entity"), complete with Place ID (from Google, lol), rating, and coordinates.
OpenAI is bringing apps inside ChatGPT — interactive interfaces directly within the chat, starting with partners like Booking.com, Expedia, and soon TripAdvisor (with many more to come).
It’s always a delicate exercise to promote your own solution. But some fundamental differences make me genuinely believe this is the most suitable tool for hoteliers. Whether compared to traditional SEO tools (Ahrefs, Semrush, …) or to generalist AI search trackers, our focus on the hotel vertical is what sets us apart. Here’s an overview.
Perplexity Travel showed the way, and now OpenAI is testing interactive maps for hotel recommendations. A glimpse into the future of AI-powered travel.
