- Visibility28
- Citations2
- Sentiment73
- Site quality87
- AI readiness43
AI assistants name Sabre in 26.7% of answers, slightly more often than the typical company in the travel sector. Its overall score is 43 out of 100, which ranks Sabre 183rd of 2,047 companies. The Sabre website passes 5 of 14 technical checks for how ready it is for AI tools.
We asked 15 unbranded questions about the travel market: none named Sabre. AI assistants named it in 16 of 60 answers (26.7%). The typical travel company is named in 21.7% of answers. By how often it is named, Sabre ranks 29 of 75 in its sector.
Every dot is a company in the study. Hover one to see it; click to open its report.
This companySame sectorEvery other companyNever named
Five parts, each scored out of 100.
How often AI assistants name the company
How often AI uses the company's own website as a source
How positively AI describes the company
How well built the website's own pages are
How easily AI tools can read and use the website
Visibility is calculated from the answers on this page. How the score is built.
75 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“What are the main differences between GDS and NDC-based distribution models for airlines?”
Short answer: GDS (Global Distribution Systems) are legacy, intermediary-led distribution channels that sell airline fares and inventory through standardized, catalog-like content and ticketing flows. NDC (IATA’s New Distribution Capability) is an XML/API-based standard designed to let airlines retail directly — exposing dynamic offers, rich content, ancillaries, and personalized pricing to sellers. NDC shifts distribution from product-centric to offer/retail-centric; GDS emphasizes universal reach, stability, and standardized booking/ticketing.
Detailed differences (by topic)
Basic model …
The AI’s answer as we received it, shortened and not checked for accuracy.
How the mentions in these answers were split between this company and its rivals
Answers naming it, out of answers received
What this website publishes for an AI agent to read, use and buy from, on a scale of six levels.
- 0
- 1
- 2
- 3
- 4
- 5
Level 0: Not yet agent-ready. Agents struggle to find basic information about the site.
- 2 of robots.txt, Sitemap, Link headers
Show every check, for your technical teamHide the checks
- study: 92.0%robots.txtPassed
A valid robots.txt with crawl rules at the site root.
- study: 86.8%SitemapNot met
Serve an XML sitemap and reference it from robots.txt.
- study: 0.3%Link headersNot met
Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.
- study: 5.5%DNS for AI DiscoveryNot present
Advertise agent endpoints with SVCB records under _agents.
- study: 1.9%Markdown negotiationNot met
Return a markdown version when asked with Accept: text/markdown, for example at the CDN.
- study: 25.1%llms.txtPassed
An llms.txt that tells language models what the site offers.
- study: 93.4%AI bot rulesPartly met
Add explicit robots.txt groups for the main AI crawlers.
- study: 1.4%Content SignalsNot met
Add a Content-Signal line for search, ai-input and ai-train to robots.txt.
- study: 0.2%Web Bot AuthNot present
Only relevant if you run your own agents or crawlers: publish a signing key directory.
- study: 0.3%API CatalogNot met
List public APIs in a linkset at /.well-known/api-catalog.
- study: 11.8%OAuth discoveryNot met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourceNot met
Serve protected resource metadata at /.well-known/oauth-protected-resource.
- study: 0.0%auth.mdNot met
Publish an auth.md describing how agents sign in.
- study: 0.2%MCP Server CardNot met
Offer an MCP server and publish its server card under /.well-known.
- study: 0.1%A2A Agent CardNot met
Publish an agent card at /.well-known/agent-card.json.
- study: 0.2%Agent SkillsNot met
Publish a skills index with the main tasks agents can do.
- study: 33.7%WebMCPCould not check
Register key actions such as search or cart as WebMCP tools.
- study: 0.1%ARD manifestNot met
Publish an ai-catalog.json listing every agent interface.
Score 27 of 100 · 97% of checks could run · checked on October 5, 2026 · www.sabre.com · 2,047 companies scanned
5 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.
These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.
Show the 14 checksHide them
- Passedrobots.txt publishedstudy: 85.0%
- PassedXML sitemapstudy: 73.2%
- PassedHTTP Link headers (RFC 8288)study: 29.0%
- Not metMarkdown content negotiationstudy: 8.3%
- Passedllms.txt publishedstudy: 21.7%
- PassedToken budget (page weight)study: 95.2%
- Not metExplicit AI bot rulesstudy: 13.0%
- Not metAPI Catalog (.well-known/api-catalog)study: 0.2%
- Not metOAuth Authorization Server discoverystudy: 10.1%
- Not metOAuth Protected Resource discoverystudy: 9.3%
- Not metMCP Server Cardstudy: 0.0%
- Not metA2A Agent Cardstudy: 0.0%
- Not metWebMCP toolsstudy: 4.2%
- Not metAgent Skills declaredstudy: 0.0%
This website sets no rules for any AI crawler by name, like 84% of the websites whose rules we could read. Every AI crawler follows its general rules.
- GPTBotpartly
- ClaudeBotpartly
- Anthropic-AIpartly
- Google-Extendedpartly
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBotpartly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBotpartly
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-Userpartly
- Claude-Userpartly
- Perplexity-Userpartly
- DuckAssistBotpartly
- MistralAI-Userpartly
Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.
How many times each website was listed as a source in these answers
In the study as Consumer brands.
- What are the main differences between GDS and NDC-based distribution models for airlines?
- What options are available for startups seeking open-source travel distribution technology?
- What are the best travel distribution platforms for airlines looking to expand digital retailing capabilities?
- Which travel technology solutions offer open and modular platforms for agencies in the US?
- How do travel agencies resolve integration issues with global distribution platforms when onboarding new APIs?
- What factors influence the pricing tiers for cloud-based travel technology solutions used by agencies?
- Are AI-powered travel technology platforms worth the investment for midsize airline carriers?
- What features should airline developers look for in a travel retailing platform to support dynamic pricing?
- Is it beneficial for travel suppliers to adopt modular travel distribution platforms instead of traditional solutions?
- What can cause syncing errors between airline inventory systems and travel agency booking platforms?
- Which travel agency platforms offer modern APIs for custom booking solutions aside from traditional GDS?
- How much do enterprise-level travel distribution platforms typically cost per year for airlines?
- How do cloud-based travel management systems differ from on-premise solutions for travel agencies?
- What are some alternatives to legacy global distribution systems for travel suppliers?
- How can open travel technology platforms help corporate travel buyers manage policy compliance?
Columns, left to right: OpenAI, Claude, Perplexity, Gemini. The AI assistants often disagree. Across the study, on 40% of the questions where a company is named, only one of the four names it.
A badge of this result that you can show on your own website. Copy the code below.
<a href="https://aeo-audit.rezolve.com/us/sabre.com"><img src="https://aeo-audit.rezolve.com/us/badges/sabre.com.svg" alt="Overall score 43 of 100, US AI Visibility Study 2026" width="280" height="64"></a>