Microsoft Manufacturing Germany

elunic

Customer Service Automation · Document Processing · Sales Automation

But there's still a long way to go before this vision of the future is realized. Although machines already deliver several terabytes of data every day, the potential within that data isn't yet being exploited. "Some of our customers are sitting on huge troves of data they're not using

Checking performance, managing knowledge or requirements-in the life of a machine, a lot of information and data needs to be documented and evaluated.
Fast, always available, flexible, and reliable: these qualities are what sets shopfloorGPT apart. Various agents are available within the solution, and each is specialized in a different area of application: service
This solution leads to increased productivity for customers by saving 15 minutes per request. It also enables a comprehensive integration of the AI agents into all existing environments and provides a central
elunic supports its customers in digitizing for Industry 4.0. The shopfloor.io platform is used for data collection and evaluation as well as for networking.

Reported impact

Business outcome

This solution leads to increased productivity for customers by saving 15 minutes per request. It also enables a comprehensive integration of the AI agents into all existing environments and provides a central

  • Compliance
  • Productivity Improvement
  • Revenue Growth

Solution

But there's still a long way to go before this vision of the future is realized. Although machines already deliver several terabytes of data every day, the potential within that data isn't yet being exploited. "Some of our customers are sitting on huge troves of data they're not using, simply because they lack the capacity and expertise," explains Claudio Gusmini elunic supports its customers in digitizing for Industry 4.0. The shopfloor.io platform is used for data collection and evaluation as well as for networking. To use the data more efficiently, the digital machine portal was extended to shopfloor.GPT based on Azure OpenAI Service. Specialized and customizable AI agents support elunic's customers as autonomous problem solvers: fast, reliable, and flexible. This solution leads to increased productivity for customers by saving 15 minutes per request.

Data flow

Source content is gathered from the organization's documents and knowledge repositories, Azure OpenAI Service interprets or generates responses from that content, and the resulting answers or assessments are surfaced to employees or citizens through the application workflow.

Solution architecture

2 components · 1 layer
  1. Compute
    • Azure OpenAI Service Runs the large-language-model features for classification, summarization, retrieval, or conversational responses in the solution.
    • Azure Kubernetes Service Runs the customer's containerized applications and AI services on a managed Kubernetes platform.

Architecture clues

  • Azure Kubernetes Service: Runs the customer's containerized applications and AI services on a managed Kubernetes platform.
  • Azure OpenAI Service: Runs the large-language-model features for classification, summarization, retrieval, or conversational responses in the solution.
  • Checking performance, managing knowledge or requirements-in the life of a machine, a lot of information and data needs to be documented and evaluated.
  • This solution leads to increased productivity for customers by saving 15 minutes per request. It also enables a comprehensive integration of the AI agents into all existing environ...
  • To use the data more efficiently, the digital machine portal was extended to shopfloor.GPT based on Azure OpenAI Service.
  • elunic supports its customers in digitizing for Industry 4.0. The shopfloor.io platform is used for data collection and evaluation as well as for networking.

Evidence from the source

But there's still a long way to go before this vision of the future is realized. Although machines already deliver several terabytes of data every day, the potential within that data isn't yet being exploited.
This solution leads to increased productivity for customers by saving 15 minutes per request. It also enables a comprehensive integration of the AI agents into all existing environments and provides a central
To use the data more efficiently, the digital machine portal was extended to shopfloor.GPT based on Azure OpenAI Service. Specialized and customizable AI agents support elunic's customers as autonomous problem solvers: f...
elunic supports its customers in digitizing for Industry 4.0. The shopfloor.io platform is used for data collection and evaluation as well as for networking.
Transparency Evidence & methodology 90/100 reference value · 92% confidence · 2 sources

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 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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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 transparency9/10
    2 source references 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. Microsoft

    elunic customer story

    First-party
    https://www.microsoft.com/en/customers/story/18819-elunic-azure-kubernetes-service
    Source type
    Vendor-published customer story
    Published
    Sep 30, 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
  2. elunic

    elunic

    First-party
    https://www.elunic.com

    Als Technologie-Partner mit Sitz in München verbinden wir strategische Beratung, erprobte Implementierungsmethodik und die eigene Open Source KI-Plattform eluna Multi Agent.

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • Published on source page Sep 30, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.