Reported impact
Business outcome
Faster incident detection and resolution
- Customer Experience
- Productivity Improvement
- Speed to Market
MongoDBLogisticsIT Operations AutomationData PlatformDevOpsGenerative AI / LLMCustomer ExperienceProductivity ImprovementSpeed to MarketUndated MongoDBMDB#00684aLogisticsGlobalAmadeus needed a faster, more intelligent way to investigate recurring IT issues across vast volumes of system logs from millions of daily transactions.Additionally, it saw a turnover of more than €5 billion in 2024.Archived · Jun 18, 20254IT Operations AutomationMongoDB Atlas · MongoDB Atlas Search · MongoDB Atlas Vector Search0 2025-06-18 4
Amadeus
IT Operations Automation
Amadeus needed a faster, more intelligent way to investigate recurring IT issues across vast volumes of system logs from millions of daily transactions.
Additionally, it saw a turnover of more than €5 billion in 2024.
Amadeus records one million transactions per second and employs 18,000 people of 150 different nationalities.
Essentially serving as the booking platform for these organizations, it serves 170 airlines and helps support more than two billion passengers each year.
Solution
Amadeus needed a faster, more intelligent way to investigate recurring IT issues across vast volumes of system logs from millions of daily transactions. Using MongoDB Atlas Search, Vector Search, and OpenAI, Amadeus built a smart incident investigation tool that matches issues to historical context across unstructured data. Amadeus improves incident investigation with MongoDB Atlas and OpenAI The Challenge Amadeus needed a faster, more intelligent way to investigate recurring IT issues across vast volumes of system logs from millions of daily transactions. Our Solution Using MongoDB Atlas Search, Vector Search, and OpenAI, Amadeus built a smart incident investigation tool that matches issues to historical context across unstructured data.
Data flow
Documents, logs, or product records are stored in MongoDB Atlas, embedded or indexed for MongoDB Atlas Vector Search, and queried by Amadeus's application workflows to return relevant results in real time.
Solution architecture
- Compute
- OpenAI Supplies AI models used to interpret text and create richer retrieval workflows.
- Storage
- MongoDB Atlas Stores the operational data and provides the managed document database foundation for Amadeus's workload.
- Serving
- MongoDB Atlas Search Provides integrated full-text and faceted search across Amadeus's application data.
- MongoDB Atlas Vector Search Indexes and retrieves vector embeddings for semantic search and generative-AI workflows.
Architecture clues
- Full-text or faceted search runs inside the MongoDB platform.
- The deployment integrates with OpenAI.
- Vector search is part of the retrieval layer for AI and semantic search workloads.
Evidence from the source
Amadeus needed a faster, more intelligent way to investigate recurring IT issues across vast volumes of system logs from millions of daily transactions.
Amadeus records one million transactions per second and employs 18,000 people of 150 different nationalities.
Essentially serving as the booking platform for these organizations, it serves 170 airlines and helps support more than two billion passengers each year.
Faster incident detection and resolution
Using MongoDB Atlas Search, Vector Search, and OpenAI, Amadeus built a smart incident investigation tool that matches issues to historical context across unstructured data.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value82/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity12/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact15/251 metric describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance0/10The publication date has no documented provenance.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.mongodb.com/solutions/customer-case-studies/amadeusMongoDBFirst-party
Amadeus customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
Date evidence
- First archived Jun 18, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.Open date source
- Sitemap last modified Nov 14, 2025 · MongoDB · Publisher metadata Publisher-reported modification timestamp; not a publication date.Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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