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OpenAI Retail Asia-Pacific & Oceania

Rakuten

Application Refactoring · IT Operations Automation · Security and Compliance

Rakuten needed to ship faster, recover from incidents faster, and increase autonomous execution across a large engineering organization without relaxing security or reliability standards.

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3-4x faster potential build time for projects, from quarters to weeks
30,000 employees worldwide
50% reduction in MTTR

Reported impact

Business outcome

Rakuten reduced incident recovery time, accelerated project delivery, made automated safety checks part of everyday shipping, and created a clearer division of labor where engineers define intent and verification while Codex handles more execution.

  • Productivity Improvement
  • Risk Reduction
  • Speed to Market

Solution

Rakuten made Codex a core part of its engineering stack and aligned its rollout around three operational goals: build faster, build safer, and operate smarter. For speed, Codex works with KQL-based monitoring and diagnostics so engineers can move from alert to root-cause analysis and remediation more quickly, cutting mean time to recovery roughly in half. For safety, Codex is invoked directly in CI/CD pipelines for code review and vulnerability checks against Rakuten's internal coding principles, allowing guardrails to run before code reaches production. For autonomy, Codex is used on larger, ambiguously specified projects, where it can move from partial requirements to working implementations, as shown by a full-stack mobile project with a Python/FastAPI backend and a Swift/SwiftUI app. Rakuten pairs these workflows with workshops across teams, shifting engineers toward writing better specifications and verification standards rather than reviewing every line by hand.

Data flow

Logs and telemetry flow through KQL-based monitoring into Codex-assisted diagnosis and remediation. Source changes move through a CI/CD workflow where Codex applies internal review principles and vulnerability checks, while larger project requirements can be translated by Codex into working service implementations.

Solution architecture

4 components · 2 layers
  1. Serving
    • KQL-based monitoring Supplies operational telemetry and queries that Codex uses for diagnosis and remediation.
    • AI agent service Represents the web-based service whose mobile version Codex implemented end to end from a single specification.
  2. Orchestration
    • Codex Acts as the primary agent for incident response, code review, vulnerability checks, and autonomous project execution.
    • CI/CD workflow Runs Codex-based code review and vulnerability checks before production deployment.

Architecture clues

  • Ambiguous specifications can still be turned into working full-stack implementations
  • Codex is embedded in KQL monitoring, CI/CD review, and end-to-end project delivery
  • Internal coding principles are fed into Codex for automated standards checking

Evidence from the source

"Codex conducts code review and vulnerability checks before changes reach production"
"Codex cut the development time for this project from one quarter to weeks."
"Our role is to define clearly what we want and establish how to verify it."
"Rakuten has used Codex to fix problems twice as fast when something breaks"
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
    2 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

    Rakuten customer story

    First-party
    https://openai.com/index/rakuten/
    Source type
    Vendor-published customer story
    Published
    Mar 11, 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.

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