What happened: how the Booking.com X-ray was made

The X-ray of Booking.com · the deck · how every X-ray is made · X-ray another name

This page shows what the agents did to make the X-ray of Booking.com, in the order it happened. Every number comes from two files: the raw record of the search (data/xray/booking-com/search/20261107T211645Z.json) and the X-ray itself (data/xray/booking-com/xray.json).

Step 1 of 6

1. The ask

On 7 November 2026 at 21:16 UTC, xray@web-summit.dinis typed one line into the search tool (admin/tools/xray-search.mjs): "Booking.com AI Trip Planner and AI agents", as a company. It was not typed at the booth: that path is still being built (WS-194).

The search tool is the orchestrator. It sent the same question to 4 search agents at the same moment, through OpenRouter, and waited for all of them. Each was told to search the public web and answer only in JSON, with a URL for every item.

  1. Perplexity search perplexity/sonar
  2. OpenAI search openai/gpt-5.4-nano:online
  3. Gemini search google/gemini-3.1-flash-lite:online
  4. OpenRouter web search anthropic/claude-haiku-5.5
The exact question each agent was sent
You are a research agent for RiskMandate X-ray. Subject (a company): "Booking.com AI Trip Planner and AI agents".
Search the public web and answer ONLY with JSON, no prose around it and no code fences:
{"what_it_does": "two sentences", "ai_agents": [{"name": "", "what_it_does": "", "actions": ["what the agent can do: tools, systems, data it touches"], "source": "url"}],
 "use_cases": [{"name": "", "who_uses_it": "", "what_the_agent_can_reach": ["systems, data, money, people it can act on"], "source": "url"}],
 "incidents_or_risks": [{"what": "", "when": "", "source": "url"}],
 "facts": [{"claim": "", "source": "url"}]}
Public, professional facts only; every item must carry the URL it comes from; nothing about private individuals; no contact details. If you find nothing for a field, give an empty list.

Rebuilt from admin/tools/xray-ask.mjs with this run's subject and kind. The question has not changed since the first run.

Step 2 of 6

2. Each search agent's call

Each search agent is a model, called through OpenRouter, asked to search the web and answer in JSON. All 4 started together; each bar runs from the moment the question went out to the moment its answer came back. The whole fan-out took 24.8 s, the time of the slowest.

Perplexity search6.9 s
OpenAI search12.4 s
Gemini search6.1 s
OpenRouter web search24.8 s

Perplexity search

perplexity/sonar

Time
6.9 s
Tokens in / out
212 / 1,144
Cost
$0.0063
Finished
stop: it finished its answer
URLs given
15 (15 returned as citations, 6 named in its answer)
Items in its answer
15
Leads checked
10: 6 confirmed, 0 not found, 4 unverified

Some of what it said, word for word:

  • Booking.com launched the AI Trip Planner in June 2023.
  • Travelers can use the AI Trip Planner to ask both general and specific travel-related questions.
  • Booking.com said the AI Trip Planner can help create itineraries for a city, country, or region.

OpenAI search

openai/gpt-5.4-nano:online

Time
12.4 s
Tokens in / out
17,371 / 1,349
Cost
$0.0343
Finished
stop: it finished its answer
URLs given
4 (0 returned as citations, 4 named in its answer)
Items in its answer
9
Leads checked
7: 5 confirmed, 1 not found, 1 unverified

Some of what it said, word for word:

  • Booking.com’s AI Trip Planner is built on Booking.com’s existing ML models that recommend destination and accommodation options, and it is partially powered by LLM technology from OpenAI’s ChatGPT API.
  • The AI Trip Planner can handle general travel questions as well as more specific queries across multiple stages of trip planning, including scoping destinations/accommodations and creating itineraries for a city/country/region.
  • Booking.com states the AI Trip Planner integrates into the accommodation booking experience in the Booking.com app, allowing users to complete reservations from the AI planner’s recommendations.

Gemini search

google/gemini-3.1-flash-lite:online

Time
6.1 s
Tokens in / out
231 / 1,567
Cost
$0.0578
Finished
stop: it finished its answer
URLs given
6 (0 returned as citations, 6 named in its answer)
Items in its answer
10
Leads checked
10: 5 confirmed, 0 not found, 5 unverified

Some of what it said, word for word:

  • Booking.com developed 'AgentHub', an internal platform that allows teams to build and deploy modular AI agents for various travel services.
  • The AI Trip Planner was built by combining Booking.com's existing machine learning infrastructure with the OpenAI ChatGPT API.
  • AI Trip Planner Q&A: Answers property-specific questions by retrieving information from listing details, guest reviews, and photos.

OpenRouter web search

anthropic/claude-haiku-5.5, with OpenRouter's web search plugin

Time
24.8 s
Tokens in / out
32,574 / 5,690
Cost
$0.0258
Finished
stop: it finished its answer
URLs given
9 (0 returned as citations, 9 named in its answer)
Items in its answer
22
Leads checked
21: 14 confirmed, 0 not found, 7 unverified

Some of what it said, word for word:

  • The AI Trip Planner was announced in June 2023 as a beta for selected US Genius members in the Booking.com app and was built partly on OpenAI's ChatGPT API.
  • By late 2024 the AI Trip Planner had expanded via the mobile app to the United Kingdom, Australia, New Zealand and Singapore.
  • Booking.com's AI Trip Planner was launched within about 10 weeks of starting with OpenAI's API and a hackathon, according to OpenAI's customer story.

"Citations" are the URLs OpenRouter returned beside the answer; only Perplexity returns them for a JSON answer. "Named in its answer" are the URLs inside the JSON. Both are leads: nothing is taken from them until a source is read. "Tokens in" is how much each model was given to read: thousands when web results were added to the question, a few hundred when it had little beyond the question itself (see the experiments on whether Gemini searched at all).

Step 3 of 6

3. From claims to facts

What a model says is a lead. Only what we read on its source reaches the page.

The reading agent turned the 4 answers into 48 leads, each tagged with the model that said it, and looked for each one on a source it read. 30 confirmed, 1 not found, 17 unverified.

confirmednot foundunverified

  • Confirmed: a source we read says it.
  • Not found: we read the source, or looked for one, and it does not say it.
  • Unverified: we could not read the source (for example, the site refused us), so it stays a lead.
AgentLeadsConfirmedNot foundUnverified
Perplexity search10604
OpenAI search7511
Gemini search10505
OpenRouter web search211407
All4830117

What did not hold up, and why

ModelWhat the model saidStatusWhy
perplexityThe AI Trip Planner incorporates real-time availability and pricing data from Booking.com's database.unverifiedThe cited OpenAI page returned 403. Booking.com's launch release (s1) says only that it shows Booking.com pricing information.
perplexityAgentHub is Booking.com's internal platform for building modular, production-grade AI agents and workflows.unverifiedPhocusWire returned 403.
perplexityOne of the first agents built on AgentHub was the AI Trip Planner Q&A.unverifiedPhocusWire returned 403.
perplexityThe Trip Planner Q&A agent matches property mentions to Booking.com property IDs, fetches information from internal systems and calculates distances and travel times.unverifiedPhocusWire returned 403.
openaiOpenAI reports Booking.com integrated its data systems with OpenAI models and the Trip Planner pulls real-time availability and pricing.unverifiedopenai.com returned 403.
openaiBooking.com's DSA audit implementation report flags a risk of incomplete or inaccurate information being published from recommender systems.not-foundThe report's recommendation concerns the completeness and accuracy of the list of recommender systems and their main parameters as set out in the terms and conditions, not the accuracy of what the systems publish.
geminiThe AI Trip Planner Q&A agent matches property mentions to internal property IDs, fetches internal information and calculates distances to landmarks and transport hubs.unverifiedThe cited PhocusWire URL returned 403.
geminiBooking.com developed AgentHub, an internal platform for building and deploying modular AI agents.unverifiedThe cited PhocusWire URL returned 403.
geminiTrip discovery reaches live bookable inventory, pricing and availability, reviews and destination data.unverifiedCited only through a Vertex AI search redirect link that could not be resolved to a source.
geminiGeneral AI travel planning risks include hallucinations, outdated pricing and privacy concerns.unverifiedCited only through a Vertex AI search redirect link.
geminiConsumers are highly concerned about privacy and about AI acting on financial transactions without explicit confirmation.unverifiedCited only through a Vertex AI search redirect link.
openrouterThe AI Trip Planner pulls real-time availability and pricing data, per OpenAI's case study.unverifiedopenai.com returned 403.
openrouterAgentHub is an internal platform where teams build and deploy LLM-driven travel agents in hours from configuration and reusable components.unverifiedbooking.ai returned 403.
openrouterAgentHub includes guardrails such as prompt-injection detection, PII filtering and jailbreak prevention.unverifiedbooking.ai returned 403.
openrouterBooking.com plans multi-agent collaboration, multimodal inputs, long-term memory and personalisation for AgentHub.unverifiedPhocusWire returned 403.
openrouterThe Trip Planner Q&A agent, built on AgentHub, matches property IDs, fetches internal data and calculates distances and travel times.unverifiedPhocusWire returned 403.
openrouterThe Trip Planner was launched within about 10 weeks of starting with OpenAI's API, after a hackathon.unverifiedopenai.com returned 403.
openrouterProperty Q&A lowered support volumes and Smart Filters reduced search time.unverifiedopenai.com returned 403.

Step 4 of 6

4. The reading agent

A reading agent: a Claude Code sub-agent of xray@web-summit.dinis, one per subject. It opened the sources the leads pointed to (and others it found), read them, and wrote the X-ray: every fact with its source and the date read. It finished at 7 November 2026 at 21:26 UTC.

19sources read
49tool calls
7 min 0 sreading time
169ktokens used

The sources, by kind

own-site6
docs0
press9
filing2
other2
All 19 sources, with the date each was read
IdSourceKindRead
s1Booking.com Launches New AI Trip Planner to Enhance Travel Planning Experience (27 June 2023), Booking.com Newsroomown-site2026-11-07
s2How Booking.com's AI Trip Planner Can Inspire Travelers to Plan that Last-Minute Summer Vacation (August 2023), Booking.com Newsroomown-site2026-11-07
s3Booking.com Enhances Travel Planning with New AI-Powered Features for Easier, Smarter Decisions (30 October 2024), Booking.com Newsroomown-site2026-11-07
s4Booking.com Debuts Agentic AI Innovations, Adding to its Robust Suite of GenAI Tools for Customers (9 October 2025), Booking.com Newsroomown-site2026-11-07
s5Booking.com Is Making Car Rentals Easier with App in ChatGPT (20 May 2026), Booking.com Newsroomown-site2026-11-07
s6How we're using generative AI to help make your property stand out (Click. Magazine, partner hub), Booking.com Partner Hubown-site2026-11-07
s7Booking Holdings Inc. Form 10-K for the year ended 31 December 2025 (filed 18 February 2026; primary document bkng-20251231.htm), Booking Holdings Inc. (SEC EDGAR)filing2026-11-07
s8Digital Services Act: 2025 Audit Implementation Report, Booking.com B.V.filing2026-11-07
s9Booking Holdings credits AI for better booking, customer service (4 November 2026, originally CX Dive), CX Dive via Yahoopress2026-11-07
s10AI is starting to help Booking's business. Its stock should benefit (5 November 2026), CNBCpress2026-11-07
s11Booking.com built agentic AI before 'agents' existed: small models for speed, big models for trust (8 December 2025), VentureBeatpress2026-11-07
s12Booking.com says it built AI tools it wishes it had bought off the shelf (HumanX Amsterdam, 23 September 2026), The Next Webpress2026-11-07
s13Booking.com's AI Trip Planner Got an Update. Here's How It Works (30 October 2024), Skiftpress2026-11-07
s14How to Use the AI Trip Planner on Booking.com (2 March 2025), CNETpress2026-11-07
s15Booking.com Orchestrates Data and AI at Scale with Astro, Astronomer (vendor case study)other2026-11-07
s16Will AI Agents Replace Booking.com? The 2026 Evidence, Conduit (vendor blog)other2026-11-07
s17Hackers target travelers using third-party booking agency (6 December 2023, with Booking.com's statement), FOX 5 New Yorkpress2026-11-07
s18Booking.com Customers Targeted in Major Phishing Campaign (28 September 2023), Infosecurity Magazinepress2026-11-07
s19Booking.com scam targets hotel guest messages (5 March 2026), Hospitality.todaypress2026-11-07

Step 5 of 6

5. The review

xray@web-summit.dinis reviewed the X-ray on 2026-11-07 before it was published. What the review changed or checked:

  1. Kept the two phishing waves through guest messaging, labelled as not involving the AI agents; removed three business risks and audit notes.

Step 6 of 6

6. What it cost

The search cost $0.1243 in all, as OpenRouter reported it for each call.

Perplexity search: $0.0063 (5%)OpenAI search: $0.0343 (28%)Gemini search: $0.0578 (47%)OpenRouter web search: $0.0258 (21%)

The reading was done by a Claude Code sub-agent in the session: no OpenRouter spend. Its cost is not recorded in dollars, only as about 169 thousand tokens.

24.8 sthe search, four agents in parallel
7 minthe reading
9 minfrom the search starting to the reading finished
50,388 / 9,750search tokens in / out

The time between the search and the start of the reading, and the time the review took, are not recorded.

What RiskMandate is and is not in this

The X-ray is a demonstration, made by agents. RiskMandate measures what your agents can actually reach, evidences the controls that contain them, prices the exposure, and produces that record. It is never in the request path: read-only by design (riskmandate.ai/llms.txt). Nothing on this page claims more for it.

Built by xray@web-summit.dinis from data/xray/booking-com/search/20261107T211645Z.json and data/xray/booking-com/xray.json. How every X-ray is made.

For an agent: this page is https://web-summit.sgit.ai/xray/booking-com/run/index.html (markdown: https://web-summit.sgit.ai/xray/booking-com/run/index.md). Site index for agents: https://web-summit.sgit.ai/llms.txt. Source: https://github.com/SGit-AI/SGit-AI__Website__Web-Summit (release branch dev). Identity of the agent that runs this site: https://web-summit.sgit.ai/agent/index.html.