Cohere Commercial and Professional Services Japan

Fujitsu

Document Processing · Enterprise Knowledge Search

Fujitsu needed a highly accurate Japanese large language model for customers in regulated industries that handle confidential data and strict compliance requirements, but building that model required advanced infrastructure, large language datasets, and specialized AI expertise it wanted to accelerate through a trusted partner.

Reported impact

Business outcome

Takane is now part of Fujitsu's digital transformation offerings and gives Japanese businesses in regulated industries a secure way to automate document-heavy workflows, improve decision-making, and deliver better customer experiences while Fujitsu accelerates development of high-impact AI applications.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Fujitsu partnered with Cohere to build Takane, a privately deployed Japanese large language model based on the Cohere Command series. Cohere provided training support and technical expertise so Fujitsu could create a model tuned for Japanese enterprise language and capable of world-class performance on the JGLUE benchmark. Takane is designed for high-stakes legal, finance, healthcare, government, and manufacturing workflows where accuracy and security matter, and it processes long text with enough precision to streamline document-heavy operations. The model can extract key details and transform unstructured information into JSON, HTML, Markdown, and CSV, while also supporting multilingual work in languages such as German, Chinese, and Portuguese. Fujitsu now delivers Takane through Fujitsu Kozuchi and its Data Intelligence PaaS so regulated customers can adopt secure generative AI inside broader digital-transformation programs.

Data flow

Regulated-industry text and business documents enter Takane, the model applies Japanese-centric language understanding and reasoning to process or extract the content, outputs structured formats such as JSON or CSV when needed, and Fujitsu exposes those capabilities through Kozuchi and DI PaaS for downstream enterprise workflows.

Solution architecture

4 components · 3 layers
  1. Compute
    • Cohere Command series Provides the base model family and technical foundation used to build Takane.
  2. Serving
    • Takane Serves as Fujitsu's Japanese large language model for secure enterprise workflows in regulated industries.
    • Fujitsu Kozuchi Distributes Takane as part of Fujitsu's broader AI service portfolio for business users.
  3. Orchestration
    • Fujitsu Data Intelligence PaaS (DI PaaS) Packages Takane into Fujitsu's all-in-one operational platform for enterprise deployment.

Architecture clues

  • Takane can transform unstructured information into JSON, HTML, Markdown, and CSV and perform entity linking.
  • Takane is privately deployed for customers that require secure, customized deployment.
  • The model supports Japanese first while also handling German, Chinese, and Portuguese.

Evidence from the source

Built on the Cohere Command series, and privately deployed
Fujitsu partnered with Cohere to build Takane, a state-of-the-art Japanese LLM for businesses in regulated industries
Takane is also being integrated into Fujitsu’s AI service Fujitsu Kozuchi and offered through the all-in-one operation platform Fujitsu Data Intelligence PaaS (DI PaaS).
Takane processes extended text inputs with precision, streamlining complex, document-heavy workflows
Takane transforms unstructured information into ready-to-use formats like JSON, HTML, Markdown, and CSV.
Transparency Evidence & methodology 75/100 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 value75/100
  • 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 impact0/25
    0 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
    5 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 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. Cohere

    Fujitsu customer story

    First-party
    https://cohere.com/customer-stories/fujitsu
    Source type
    Vendor-published customer story
    Published
    May 20, 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 May 20, 2025 · Cohere · 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.