OpenAI Software / SaaS Japan

LY Corporation

Enterprise Knowledge Search · Marketing Personalization

LY Corporation needed to modernize flagship consumer platforms and internal operations with AI while safely handling large volumes of user and enterprise information and maintaining trust in user-facing experiences.

32 AI-driven use cases as of December 2024
700,000–800,000 annual working hours reduced
Annual sales increases of ¥110B yen (roughly $715M USD)
Productivity improvements valued at ¥10B yen ($65M USD) annually

Reported impact

Business outcome

LY Corporation created 32 AI-driven use cases, cut hundreds of thousands of work hours through internal productivity gains, improved consumer search and assistant experiences, and projected material revenue and productivity upside from AI-powered features.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

LY Corporation built a shared OpenAI-powered foundation for both employee productivity and consumer-facing product features. Internally, the company rolled out SeekAI, an in-house tool built on the OpenAI API that uses retrieval-augmented generation over internal workspaces and documentation databases to answer employee questions quickly and reduce verification work across departments. Externally, LY uses GPT-4o in the LINE AI Assistant so users can access conversational help inside the messaging app they already use every day, and it added GPT-4 and GPT-4o features to Yahoo! JAPAN Search to summarize reviews and generate travel plans. The rollout was wrapped in governance, ethics, and employee-education controls, including zero-data-retention guarantees and clear rules around model inputs and outputs, enabling 32 production use cases by late 2024.

Data flow

Internal documents and workspace content are indexed for SeekAI, which retrieves and synthesizes answers for employees. On the consumer side, LINE and Yahoo! JAPAN user requests are routed through GPT-4-series models, and the resulting summaries or plans are refined with user feedback and LY’s product data.

Solution architecture

4 components · 2 layers
  1. Compute
    • OpenAI API Provides the model interface underlying LY Corporation’s internal and consumer AI features.
  2. Serving
    • SeekAI Uses retrieval-augmented generation to answer employee questions against internal workspaces and documentation.
    • LINE AI Assistant Delivers GPT-4o-powered conversational assistance inside LINE for consumer users.
    • Yahoo! JAPAN Search AI features Summarize reviews and generate travel plans directly in Yahoo! JAPAN Search experiences.

Architecture clues

  • Governance model emphasized ethics, employee education, and safe use of generative AI
  • LY Corporation acts as a beta tester for OpenAI’s new products
  • OpenAI API rollout included zero data retention and no secondary use of employee input data
  • SeekAI uses RAG over internal workspaces and documentation databases

Evidence from the source

"In July 2024, LY Corporation implemented a company-wide rollout of SeekAI, an in-house productivity tool developed on OpenAI’s API."
"LY Corporation estimates a reduction of 700,000–800,000 annual working hours"
"SeekAI leverages RAG to provide answers to employee inquiries by referencing internal workspaces and in-house documentation databases."
"The LINE AI Assistant is an AI tool that feels like a friend. Powered by GPT‑4o in text interactions"
Transparency Evidence & methodology 100/100 reference value · 89% 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 confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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

    LY Corporation customer story

    First-party
    https://openai.com/index/ly-corporation/
    Source type
    Vendor-published customer story
    Published
    Feb 27, 2025
    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

  • Published on source page Feb 27, 2025 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.