OpenAI Travel and Hospitality Europe & UK

Virgin Atlantic

Application Refactoring · IT Operations Automation

Virgin Atlantic needed to ship a customer-facing mobile app under a fixed holiday deadline, improve software quality, and refactor legacy systems faster without increasing launch risk for operational airline services.

30 minutes to refactor legacy codebases, down from 2 weeks
78–80% reduction in codebase size on legacy refactors
Zero P1 defects at launch
~100% unit test coverage on new app

Reported impact

Business outcome

Virgin Atlantic launched its app on time with near-complete test coverage and zero P1 defects, dramatically reduced legacy refactoring effort, and enabled more teams to prototype internal tools without waiting on a central engineering queue.

  • Productivity Improvement
  • Risk Reduction
  • Speed to Market

Solution

Virgin Atlantic embedded Codex into digital engineering and data workflows to improve delivery speed and confidence on both customer apps and legacy systems. For its revamped mobile app, the team used Codex to strengthen test coverage, refactor code, and reach near-complete unit testing before releasing first in beta over Christmas and then in production weeks later with zero P1 defects. The same assistant is now used to shrink and modernize long-lived codebases, turning refactors that once took weeks into work that can finish in under an hour. Codex also helps analyst teams and other business units build prototype applications directly on top of the airline’s core data warehouse, extending AI-assisted development beyond engineering into planning, customer-experience, and maintenance functions.

Data flow

Mobile-app requirements, legacy code, and migration tasks are fed into Codex so teams can generate refactors and strengthen test coverage before launch. Analysts also connect warehouse-backed data work to Codex-generated prototypes that business teams then review and expand.

Solution architecture

1 components · 1 layer
  1. Orchestration
    • Codex Generates, refactors, and tests application code while helping teams prototype new software against Virgin Atlantic data systems.

Architecture clues

  • Analyst teams prototype internal applications directly against the core data warehouse
  • App launched in beta over Christmas and moved to production weeks later
  • Codex is used for both app delivery and legacy-code modernization
  • Lead front-end developer built a working front-end application from a Figma prototype in a week

Evidence from the source

"Codebases the team had maintained for years are now being refactored in hours instead of weeks."
"We’re seeing in some instances anywhere from 78 to 80% reduction in codebase size from utilizing Codex"
"With the help of Codex, the team was able to hit the window with near-complete unit test coverage and zero P1 defects at launch."
"You can develop that data through to a prototype in a matter of literally a couple of hours"
Transparency Evidence & methodology 100/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    Virgin Atlantic customer story

    First-party
    https://openai.com/index/virgin-atlantic/
    Source type
    Vendor-published customer story
    Published
    May 22, 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

Date evidence

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