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UKA Group

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UKA needed a secure, data protection-compliant generative AI tool that employees could use with company data and internal documents while retaining control of data under GDPR and German data protection law.

Reported impact

Business outcome

UKA gave employees a secure multilingual generative AI tool for research, information procurement, and content creation, reducing repetitive effort, accelerating workflows, and shortening the path from AI use case to productive deployment while maintaining data sovereignty.

  • Compliance
  • Productivity Improvement
  • Risk Reduction
  • Speed to Market

Solution

UKA adopted Deutsche Telekom's browser-based Business GPT as a secure enterprise interface for generative AI use across its workforce. The application is hosted in European cloud environments and was tested for IT security and data protection so UKA staff can enter company data securely from all end devices while keeping data control under GDPR and German law. Business GPT provides the user-facing layer through UKA's own intranet and corporate design, and it supports integration of internal company documents to ground employee queries and daily knowledge work. For model execution, the solution uses Microsoft Azure OpenAI Services in a shielded area, specifically exposing GPT 3.5 and GPT 4.0 models for multilingual research, information procurement, and content creation. Telekom MMS operates the deployment, combining secure access, model availability, and enterprise controls so UKA can move from AI business case to productive usage quickly.

Data flow

UKA employees access Business GPT through the company intranet on their end devices, submit prompts and company data, and optionally work with internal company documents. Business GPT routes requests into a shielded Microsoft Azure OpenAI Services environment in Europe, where GPT 3.5 or GPT 4.0 generates responses that are returned to employees through the browser-based interface.

Solution architecture

4 components · 2 layers
  1. Compute
    • Microsoft Azure OpenAI Services Supplies the shielded model runtime used to serve generative AI capabilities in European cloud environments.
    • GPT 3.5 Acts as one of the language models available to UKA users through Business GPT.
    • GPT 4.0 Acts as one of the language models available to UKA users through Business GPT.
  2. Serving
    • Business GPT Provides the secure browser-based application UKA employees use for generative AI and internal document-supported work through the company intranet.

Architecture clues

  • Accessible through UKA's own intranet with company branding
  • Application tested for IT security and data protection
  • Data remains under UKA control in accordance with GDPR and German data protection law
  • Hosted in European cloud environments
  • Multilingual tool
  • Telekom MMS ensures secure handling of company data
  • Usable on all end devices

Evidence from the source

"For the international energy park developer UKA, the multilingual tool is particularly useful for efficient research and information procurement in day-to-day work as well as content creation."
"Thanks to secure operation on cloud environments in Europe, companies can use the GPT 3.5 and GPT 4.0 models from Microsoft Azure OpenAI Services in a shielded area."
"The UKA Group now has access to a secure, data protection-compliant tool for generative AI."
"The browser-based Business GPT can be accessed at UKA via the company’s own intranet, complete with the company’s own corporate design."
"The solution offers generative AI and the option of integrating internal company documents."
"This allows UKA to retain control of its data in accordance with the GDPR and German data protection law."
Transparency Evidence & methodology 75/100 reference value · 92% 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 confidence92%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 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. UKA Group

    UKA Group customer story

    First-party
    https://www.telekom.com/en/newsroom/latest-updates/media-information/2024/3/business-gpt
    Source type
    Customer-published story
    Published
    Mar 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

  • Published on source page Mar 7, 2024 · Deutsche Telekom AG · 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.