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Microsoft Financial Services India

Aditya Birla Capital

Customer Service Automation · Enterprise Knowledge Search · Fraud Detection

It wanted to streamline the business and deliver a more connected customer experience using generative AI.

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Financial services provider Aditya Birla Capital has a massive market presence with more than 40 million customers, nine business lines
Financial services provider Aditya Birla Capital has a massive market presence with more than 40 million customers, nine business lines, and multiple business...
It reduced the amount of latency from 30 seconds to less than a second by anchoring to Azure AI Search and other Azure AI services , as well as Azure API...
Powered by more than 55,000 employees, the businesses of Aditya Birla Capital have a nationwide reach with more than 1,470 branches

Reported impact

Business outcome

The overarching goal of using Azure infrastructure is to build company-wide generative AI platforms to serve as copilots for different functions, including one that's already improved contact center agent productivity by 20%.

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

Solution

Aditya Birla Capital used Azure OpenAI Service, Azure AI Search, Azure Cosmos DB to replace disconnected manual workflows with a more consistent digital operating model. 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 Call-center telephony, which lets the new Azure-based workflow fit into existing operations rather than standing apart from them. The overarching goal of using Azure infrastructure is to build company-wide generative AI platforms to serve as copilots for different functions, including one that's already improved contact center agent productivity by 20%.

Data flow

Content and operational context are prepared in Azure AI Search, passed to Azure OpenAI for generation or extraction, stored in Azure Cosmos DB, and returned through the user-facing application to employees or customers with grounded responses.

Solution architecture

5 components · 3 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 AI Services Supplies prebuilt AI capabilities such as language, speech, or vision services within the application flow.
  2. Storage
    • Azure Cosmos DB Persists low-latency application data, conversation state, or operational records.
  3. Serving
    • Azure AI Search Indexes enterprise content and retrieves grounding context for assistants and search experiences.

Architecture clues

  • 40 million customers
  • Applies retrieval-based grounding with Azure AI Search before answer generation.
  • Runs scalable containerized services on Azure Kubernetes Service.
  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

It reduced the amount of latency from 30 seconds to less than a second by anchoring to Azure AI Search and other Azure AI services , as well as Azure API Management .
It wanted to streamline the business and deliver a more connected customer experience using generative AI.
The SimpliFi chatbot uses Microsoft Azure app, data, and AI services—such as Azure OpenAI Service, Azure API Management, and Azure Cosmos DB—to simplify financial services information and offers through intelligent search and proactive...
The overarching goal of using Azure infrastructure is to build company-wide generative AI platforms to serve as copilots for different functions, including one that's already improved contact center agent productivity by 20%.
The result is SimpliFi, a customer-facing generative AI chatbot built on the Microsoft Azure purpose-built stack for AI applications including Azure OpenAI Service , cloud-scale databases, and app services.
Transparency Evidence & methodology 90/100 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 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 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

    Aditya Birla Capital customer story

    First-party
    https://www.microsoft.com/en/customers/story/20596-aditya-birla-financial-shared-services-azure-open-ai-service
    Source type
    Vendor-published customer story
    Published
    Jan 8, 2025
    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 Jan 8, 2025 · 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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