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Microsoft Energy Türkiye

Aydem Energy

Customer Service Automation · Enterprise Knowledge Search · Real-time Analytics

Seasonal challenges, like hot summers or high number of travelers during vacation periods, lead to increased demand while windy winters cause disruptions.

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90% of inquiries are fully resolved, boosting agent
90% of inquiries are fully resolved, boosting agent and customer satisfaction and creating new revenue opportunities.
Aydem Energy brings electricity to around 6 million customers across five cities in Türkiye, managing generation, distribution, and retail to keep energy...
Aydem Energy serves 6 million customers in Türkiye.

Reported impact

Business outcome

90% of inquiries are fully resolved, boosting agent and customer satisfaction and creating new revenue opportunities.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction

Solution

Aydem Energy used Azure OpenAI Service, Power Platform to replace disconnected manual workflows with a more consistent digital operating model. Low-code automation and copilot capabilities orchestrate tasks, route work, and surface context to agents or business users without requiring separate point solutions. Where generative AI is involved, Azure OpenAI handles the language understanding layer while connected data and workflow services keep responses grounded in enterprise context. It also connects with surrounding systems such as WhatsApp, CRM and ERP systems, Call-center telephony, which lets the new Azure-based workflow fit into existing operations rather than standing apart from them. 90% of inquiries are fully resolved, boosting agent and customer satisfaction and creating new revenue opportunities.

Data flow

Content and operational context are prepared in the application data layer, passed to Azure OpenAI for generation or extraction, stored in Azure storage services, and returned through the user-facing application to employees or customers with grounded responses.

Solution architecture

2 components · 2 layers
  1. Compute
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
  2. Orchestration
    • Power Platform Provides the low-code automation and application platform for workflow modernization.

Architecture clues

  • 6 million customers
  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

90% of inquiries are fully resolved, boosting agent and customer satisfaction and creating new revenue opportunities.
After reviewing the outcomes, they concluded that Azure OpenAI Service was the best choice, and Softtech was the ideal partner for implementation.
Aydem Energy and Softtech used Azure OpenAI Service to create an AI assistant for WhatsApp, providing real-time updates and handling meter readings, bill checks, and claims.
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Seasonal challenges, like hot summers or high number of travelers during vacation periods, lead to increased demand while windy winters cause disruptions.
Transparency Evidence & methodology 100/100 reference value · 89% 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 value100/100
  • 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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 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

    Aydem Energy customer story

    First-party
    https://www.microsoft.com/en/customers/story/20845-aydem-energy-azure-open-ai-service
    Source type
    Vendor-published customer story
    Published
    Dec 27, 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 Dec 27, 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.

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