Microsoft Automotive Japan

Denso Corporation

Customer Service Automation · Real-time Analytics · Security and Compliance

Takeshi Narisako, Senior Director Research & Development Center, Denso Corporation Human-like robots acting autonomously based on human language Denso’s robot control technology that uses generative AI aims to overcome this challenge.

Denso operates in 35 countries and regions around the world under our corporate philosophy of “contributing to a better world by creating value together with a vision for the future.” Denso, is Japan’s largest automotive parts manufacturer and second in the world in sales. As the automotive industry is being transformed by digitalization, the company is working to create new value in four areas with an eye on the future: electrification, advanced safety/automated driving, connected driving, and non-vehicle business (factory automation/agriculture).

Reported impact

Business outcome

As a result, this became a barrier to improving the precision of its movements.

  • Compliance
  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth

Solution

Denso Corporation, a Automotive organization in Japan, modernized this customer service automation workload with Azure OpenAI Service, Azure, Azure App Service, Azure AI Services, and GitHub. Takeshi Narisako, Senior Director Research & Development Center, Denso Corporation Human-like robots acting autonomously based on human language Denso’s robot control technology that uses generative AI aims to overcome this challenge. Azure OpenAI Service runs the large language model workloads that power summarization, generation, or conversational reasoning in the solution. Azure provides the scalable cloud foundation hosting the customer’s modernized applications, data, and AI workloads. Azure App Service hosts the web applications and APIs that deliver the business service to users. Data from operational systems is ingested into Azure OpenAI Service, processed across Azure, and surfaced through Azure App Service so teams can act on fresher operational insight.

Data flow

Data from operational systems is ingested into Azure OpenAI Service, processed across Azure, and surfaced through Azure App Service so teams can act on fresher operational insight.

Solution architecture

5 components · 3 layers
  1. Compute
    • Azure OpenAI Service runs the large language model workloads that power summarization, generation, or conversational reasoning in the solution.
    • Azure provides the scalable cloud foundation hosting the customer’s modernized applications, data, and AI workloads.
    • Azure AI Services supplies packaged AI capabilities such as language, speech, or document intelligence for the application flow.
  2. Serving
    • Azure App Service hosts the web applications and APIs that deliver the business service to users.
  3. Orchestration
    • GitHub supports code collaboration and developer workflow automation around the solution.

Evidence from the source

As a result, this became a barrier to improving the precision of its movements.
In the manufacturing world, a trend toward high-mix low-volume manufacturing is gaining speed against a backdrop of market maturity and diversifying needs. The Denso Group has already developed multifunctional robots that can perform multiple tasks using AI. However, when the new robot is completed, it will be expected to be able to perform an even wider range of tasks, and to enable new forms of human-robot collaborations, for example, that of a robot that records various tasks and gives advice to humans for each step of the process. Verbal work instructions will lower the hurdles for using the robot significantly, and the robot can be expected to be used not only in factories, but also in stores and in towns, and so on.
Takeshi Narisako, Senior Director Research & Development Center, Denso Corporation Human-like robots acting autonomously based on human language Denso’s robot control technology that uses generative AI aims to overcome this challenge.
The range of possibilities for generative AI and robot combinations is rapidly expanding, and even though it is less than a year since development began, they include assembling and ringing a bell, having a transport robot and a barista robot work together to serve coffee to customers, and making cocktails through dialogue. Other initiatives have also begun, including having robots work with humans as sales staff in stores, recommending products, and providing other customer services.
They chose to use Azure OpenAI Service, Azure AI and Machine Learning, and Azure App Service.
Transparency Evidence & methodology 76/100 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 value76/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric 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. Microsoft

    Denso Corporation customer story

    First-party
    https://www.microsoft.com/en/customers/story/15692-denso-corporation-azure-open-ai-service
    Source type
    Vendor-published customer story
    Published
    Oct 8, 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

  • Published on source page Oct 8, 2024 · Microsoft · 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.