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IBM Commercial and Professional Services Japan

Mitsubishi Research Institute DCS

Application Refactoring · Sales Automation

Mitsubishi Research Institute DCS needed to improve proposal quality and development speed because competitive IT-consulting work required faster customer-tailored recommendations and more efficient code generation.

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10% anticipated boost to proposal acceptance rate
three-month proof of concept
~60% cut to development hours expected

Reported impact

Business outcome

The firm expects more successful proposals, faster code generation, and a stronger competitive position for consulting and delivery work built around generative AI.

  • Productivity Improvement
  • Revenue Growth
  • Speed to Market

Solution

Mitsubishi Research Institute DCS ran a three-month proof of concept with IBM watsonx.ai to address two related bottlenecks: proposal development and code generation. For consulting support, IBM Client Engineering helped build a solution that analyzes meeting transcripts, quantifies participant excitement and interest, and combines those signals with related metrics such as speaking time so the firm can draft more compelling proposals. In parallel, the teams applied the same generative AI platform to software-delivery work by validating and generating code from existing design documents while honoring internal development rules. IBM also built an Eclipse IDE extension that lets engineers invoke code generation directly from their working environment through an API. This architecture turns transcripts and design artifacts into structured AI inputs, then routes the resulting recommendations and code back into consulting and engineering workflows so teams can pursue more business with less manual effort.

Data flow

Meeting transcripts and design documents are analyzed by watsonx.ai, proposal insights and generated code are returned to consulting and engineering teams, and the Eclipse extension lets developers consume the code-generation output directly in their workflow.

Solution architecture

3 components · 3 layers
  1. Compute
    • IBM watsonx.ai Analyzes transcripts for proposal intelligence and generates code from design documents for development workflows.
  2. Serving
    • Eclipse IDE Hosts the custom extension that lets developers call the code-generation API from their day-to-day development environment.
  3. Orchestration
    • IBM Client Engineering Co-developed the proof of concept and validated how generative AI could be embedded into consulting and coding work.

Architecture clues

  • Code generation is invoked directly from Eclipse through a custom extension and API.
  • The proposal workflow uses transcript analysis plus conversation metrics such as speaking time.
  • The same watsonx.ai foundation supports both business and engineering workflows.

Evidence from the source

"10% anticipated boost to proposal acceptance rate"
"built an extension for the Eclipse IDE that could support direct code generation through an API"
"cut the time needed by its staff to generate code by roughly 60%"
Transparency Evidence & methodology 90/100 reference value · 84% 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 value90/100
  • 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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence84%
  • 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 evidence15/20
    3 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. IBM

    Mitsubishi Research Institute DCS customer story

    First-party
    https://www.ibm.com/case-studies/mitsubishi-research-institute-dcs
    Source type
    Vendor-published customer story
    Published
    May 28, 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.

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