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Microsoft Retail Türkiye

Boyner

Customer Service Automation · Document Processing · Enterprise Knowledge Search

Challenging IT complexity and cost Boyner's Information Technology (IT) department is primary to the company's operations, ensuring that both internal systems and customer-facing services run smoothly.

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3x more performance, Boyner has seen a rise in customer satisfaction, engagement, conversion rate
Boyner Group, with its six group companies, more than 250 stores, e-commerce sites, mobile applications
Boyner Group, with its six group companies, more than 250 stores, e-commerce sites, mobile applications, and more than 8,000 employees, maintains its...
With enhanced data analytics, Boyner can now draw meaningful insights in 10 minutes instead of eight hours, helping to make informed decisions in real time.

Reported impact

Business outcome

With enhanced data analytics, Boyner can now draw meaningful insights in 10 minutes instead of eight hours, helping to make informed decisions in real time.

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

Solution

Boyner used Azure OpenAI Service, Azure AI Foundry, Azure Kubernetes Service 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. With enhanced data analytics, Boyner can now draw meaningful insights in 10 minutes instead of eight hours, helping to make informed decisions in real time.

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

5 components · 2 layers
  1. Compute
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
    • Azure Kubernetes Service Hosts scalable APIs, microservices, and model-serving workloads in containers.
    • Azure Synapse Analytics Runs integrated SQL and big-data analytics across enterprise datasets.
  2. Orchestration
    • Azure AI Foundry Provides the managed environment for model orchestration, experimentation, and responsible AI operations.
    • Power Platform Provides the low-code automation and application platform for workflow modernization.

Architecture clues

  • Runs scalable containerized services on Azure Kubernetes Service.
  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

"We made extensive use of various Azure services, including Azure Application Gateway for load balancing, Azure Virtual Machines and Azure Kubernetes Service for scalability, Azure SQL Managed Instance for service stability, and Azure...
"We've built a seamless integration within and across Azure Synapse Analytics and Azure OpenAI.
Challenging IT complexity and cost Boyner's Information Technology (IT) department is primary to the company's operations, ensuring that both internal systems and customer-facing services run smoothly.
We rely on Azure OpenAI Service to enable better production, deployment, and interpretation of data science models," adds Fulya Tezel, Data Director at Boyner.
With enhanced data analytics, Boyner can now draw meaningful insights in 10 minutes instead of eight hours, helping to make informed decisions in real time.
Transparency Evidence & methodology 95/100 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 value95/100
  • 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
    3 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

    Boyner customer story

    First-party
    https://www.microsoft.com/en/customers/story/1777364400660400778-boyner-azure-retailers-en-turkiye
    Source type
    Vendor-published customer story
    Published
    Jun 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 Jun 7, 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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