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AWS Commercial and Professional Services India

Infosys

Customer Service Automation

According to the Glauco Volpe, head of cloud services and infrastructure at Infosys, one of the project's goals is the smart use of the knowledge produced and stored within the company.

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Reported impact

Business outcome

Infosys, one of the global leaders in consulting and next-generation digital services, will use an artificial-intelligence (AI)-based solution to provide first-level support to its clients.

  • Customer Experience
  • Productivity Improvement

Solution

Infosys, one of the global leaders in consulting and next-generation digital services, will use an artificial-intelligence (AI)-based solution to provide first-level support to its clients. Powered by AWS cloud and developed by the AWS business partner MadeinWeb , the application should increase service quality and efficiency. The solution proposed by MadeinWeb, in collaboration with AWS, was built based on a robust architecture. The process began by creating a secure repository in the Amazon Simple Storage Service ( Amazon S3 ), providing fast, secure, and cost-effective access to thousands of documents related to Infosys calls, knowledge bases, and technical standards. Native integration with Amazon S3 objects made it easy to implement the Amazon OpenSearch Service , creating a fully vectorized database.

Data flow

The process began by creating a secure repository in the Amazon Simple Storage Service ( Amazon S3 ), providing fast, secure, and cost-effective access to thousands of documents related to Infosys calls, knowledge bases, and technical standards. Vinicius Machado, MadeinWeb Cloud, Data & AI director, points out that after extracting this data, the semantic capacity of a foundational model was used to understand the calls and structure the solution to user demands, achieving significant assertiveness in complex problems. Subsequently, the switchover to Amazon Bedrock brought

Solution architecture

5 components · 3 layers
  1. Compute
    • Amazon Bedrock Runs the foundation model layer for the customer and agent support workflows.
    • AWS Lambda Executes serverless processing and workflow glue for the customer and agent support workflows.
  2. Storage
    • Amazon S3 Stores source data, documents, or generated artifacts used by the customer and agent support workflows.
  3. Serving
    • Amazon OpenSearch Service Indexes content and powers search or analytics for the customer and agent support workflows.
    • Amazon API Gateway Exposes APIs that front the customer and agent support workflows.

Architecture clues

  • The architecture was then built using key AWS services, including Amazon API Gateway AWS Lambda Function, and the strategic use of Amazon SageMaker for data ingestion and specific tests.

Evidence from the source

"We are global partners of AWS, which is our cloud provider and offers the solutions we provide to our customers.
Initially, customized models were implemented with Amazon SageMaker , providing substantial computational power.
Native integration with Amazon S3 objects made it easy to implement the Amazon OpenSearch Service , creating a fully vectorized database.
Powered by AWS cloud and developed by the AWS business partner MadeinWeb , the application should increase service quality and efficiency.
The process began by creating a secure repository in the Amazon Simple Storage Service ( Amazon S3 ), providing fast, secure, and cost-effective access to thousands of documents related to Infosys calls, knowledge bases, and technical...
The solution proposed by MadeinWeb, in collaboration with AWS, was built based on a robust architecture.
Transparency Evidence & methodology 66/100 reference value · 85% 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 value66/100
  • 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 specificity16/20
    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 confidence85%
  • 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
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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. AWS

    Infosys customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/infosys-case-study
    Source type
    Vendor-published customer story
    Published
    Jan 1, 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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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