OpenAI Retail Asia Pacific

Rakuten

Enterprise Knowledge Search · Marketing Personalization

As Rakuten seeks to provide ever-increasing value to its customers, it’s hard to overstate the importance of data. This includes the vast amounts of transactional and behavioral data from Rakuten’s ecosystem of services, down to internal business data preserved in PDFs or Word documents.

The Rakuten Group has more than 1.8 billion members from around the world interacting with its ecosystem of over 70 online services in e-commerce, fintech, digital content, and communications....

Reported impact

Business outcome

Previously, users had to wait days to get a response to a customer service ticket. "By using OpenAI’s API with RAG on our internal knowledge base, we’re now able to respond to and help users automatically," Kaji said.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

As Rakuten seeks to provide ever-increasing value to its customers, it’s hard to overstate the importance of data. This includes the vast amounts of transactional and behavioral data from Rakuten’s ecosystem of services, down to internal business data preserved in PDFs or Word documents. "For us, the data asset is the key corporate asset, " said Yusuke Kaji, General Manager of AI for Business at Rakuten Group, Inc. The Rakuten Group has more than 1.8 billion members from around the world interacting with its ecosystem of over 70 online services in e-commerce, fintech, digital content, and communications. Rakuten also operates as a B2B company in numerous ways, including helping more than 57,000 Japanese merchants sell online to Japanese customers; providing e-commerce and advertising solutions to large enterprises in international markets; and building and operating mobile networks.

Data flow

The Rakuten Group has more than 1.8 billion members from around the world interacting with its ecosystem of over 70 online services in e-commerce, fintech, digital content, and communications. Rakuten also operates as a B2B company in numerous ways, including helping more than 57,000 Japanese merchants sell online to Japanese customers; providing e-commerce and advertising solutions to large enterprises in international markets; and building and operating mobile networks.

Solution architecture

4 components · 2 layers
  1. Compute
    • OpenAI API Embeds model capabilities inside the customer’s application or internal workflow.
    • GPT-3.5 Supports earlier generation and classification tasks in the deployment.
    • GPT-3 Supports early experimentation before newer models took over the workflow.
  2. Serving
    • ChatGPT Enterprise Provides a governed assistant environment for employees working across the deployment’s core workflows.

Architecture clues

  • Embeds model capabilities inside the customer’s application or internal workflow.
  • Provides a governed assistant environment for employees working across the deployment’s core workflows.
  • Rakuten was quick to get started with OpenAI’s API: They began building with GPT‑3.5, and even created their own internal chatbot for employees before the launch of ChatGPT...
  • The Rakuten Group has more than 1.8 billion members from around the world interacting with its ecosystem of over 70 online services in e-commerce, fintech, digital content, and...
  • With AI, Rakuten is finding innovative ways to leverage this asset: "We believe AI models, such as those developed by OpenAI, are the way to amplify the impact we can make on top...

Evidence from the source

"By using OpenAI’s API with RAG on our internal knowledge base, we’re now able to respond to and help users automatically,"
"For us, the data asset is the key corporate asset,"
"We believe AI models, such as those developed by OpenAI, are the way to amplify the impact we can make on top of the data,"
As Rakuten seeks to provide ever-increasing value to its customers, it’s hard to overstate the importance of data.
Transparency Evidence & methodology 70/100 reference value · 85% 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 value70/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • 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 specificity8/10
    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-2024/
    Source type
    Vendor-published customer story
    Published
    Aug 7, 2024
    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.