Databricks Travel and Hospitality Germany

Eurowings

Data Lake / Data Warehouse Modernization · Enterprise Knowledge Search · Real-time Analytics

Before unifying its data strategy, Eurowings faced significant challenges with siloed data across disparate systems. Technical barriers in vector storage also slowed development, as the team had to manually manage storage units and make complex adjustments to hybrid search.

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By implementing semantic search powered by Databricks AI Search and Model Serving, Eurowings achieved a 109% lift in search relevance.

By moving to AI Search, the company increased search relevance by 109% and improved its recall rate from 50% to over 75%.

Technical performance also improved dramatically, with search latency dropping from 6 seconds to under 2 seconds, ensuring a faster experience for travelers.

This transformation has reduced latency by over 60% and improved search recall from 50% to more than 75%.

Reported impact

Business outcome

Eurowings achieved measurable business impact on Databricks, including By implementing semantic search powered by Databricks AI Search and Model Serving, Eurowings achieved a 109% lift in search relevance.; This transformation has reduced latency by over 60% and improved search recall from 50% to more than 75%.; By moving to AI Search, the company increased search relevance by 109% and improved its recall rate from 50% to over 75%..

Solution

In search relevance achieved by replacing keyword-based search with semantic search powered by Databricks AI Search on eurowings.com Eurowings, a subsidiary of Lufthansa Group and a leader in the travel industry, aims to be the ultimate travel companion by providing seamless digital experiences for its customers. To achieve this, the company needed to overcome technical barriers that prevented users from finding relevant information quickly on its website. By implementing semantic search powered by Databricks AI Search and Model Serving, Eurowings achieved a 109% lift in search relevance. This transformation has reduced latency by over 60% and improved search recall from 50% to more than 75%.

Data flow

Operational, customer, and transactional data lands in Databricks and is transformed into curated datasets before being served through Databricks AI Search

Solution architecture

3 components · 3 layers

Compute
- Agent Bricks Supports agentic or GenAI applications that Eurowings uses to automate or accelerate work.
Serving
- Databricks AI Search Powers semantic retrieval and search relevance improvements for Eurowings.
Orchestration
- MLflow Tracks experiments and manages model lifecycle workflows for Eurowings's ML teams.

Architecture clues

Cloud: Azure
Key product: Agent Bricks
Key product: Databricks AI Search
Key product: MLflow
Medallion architecture
Real-time or streaming data processing

Evidence from the source

By implementing semantic search powered by Databricks AI Search and Model Serving, Eurowings achieved a 109% lift in search relevance.

By moving to AI Search, the company increased search relevance by 109% and improved its recall rate from 50% to over 75%.

In search relevance achieved by replacing keyword-based search with semantic search powered by Databricks AI Search on eurowings.com

Technical performance also improved dramatically, with search latency dropping from 6 seconds to under 2 seconds, ensuring a faster experience for travelers.

This transformation has reduced latency by over 60% and improved search recall from 50% to more than 75%.

Transparency Evidence & methodology 95/100 reference value · 92% confidence · 2 sources

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 value95/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth20/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
4 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence92%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability23/25
11 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency9/10
2 source references document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards 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.

Databricks
Eurowings customer story

First-party
https://www.databricks.com/customers/eurowings

Source type
Vendor-published customer story

Published
Jul 8, 2026
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Lufthansa Group
Eurowings | Annual Report 2025

First-party
https://report.lufthansagroup.com/2025/annual-report/en/combined-management-report/business-segments/passenger-airlines-business-segment/eurowings/
Employees as of 31 Dec number 5,481.

Source type
Official company website
Supports
- Company size

Date evidence

Published on source page Jul 8, 2026 · Databricks · First-party
Open date source

Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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