What challenges did Infosys face before AI implementation?

Support agents needed to locate relevant past call transcripts and provide guidance to meter technicians. 60-70% of issues were repetitive, average handling time for top categories was over 5 minutes, and hiring and training more agents was costly and not scalable.

Source

Evidence: Low35/100

How Infosys Topaz leverages Amazon Bedrock to transform technical help desk operations

Use case typeKnowledge managementUpdated Jun 13, 2026

Infosys built an AI-powered technical help desk for a large energy supplier to ingest past and new call transcripts, summarize conversations, classify relevance, retrieve similar issues, and help support agents resolve meter technician calls faster. The system uses Amazon Bedrock with Claude Sonnet, Amazon S3, AWS Step Functions, AWS Lambda, Amazon OpenSearch Serverless, Amazon Titan Text Embeddings, AWS Secrets Manager, AWS KMS, AWS IAM, DynamoDB, and a Streamlit frontend. It implements role-based access control, asynchronous transcript processing, caching, FAQ retrieval, and a RAG-style knowledge base for agent assistance.

Organization: Infosys
Industry: Energy & Utilities
Location: India
Published: August 2025

Reported outcomes

Impact: 30–40%

Other quantified impact

Time: More than 5 minutesTime: More than 2 minutesTime: More than 60% lowerCustomer experience: +30%

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 70%

AWS Machine Learning BlogAug 21, 2025Blog postInferred claimLow evidence strength

The enterprise AI assistant now handles 70% of previously human-managed calls.

Normalized claim

Time: 5 minutes decrease

AWS Machine Learning BlogAug 21, 2025Blog postInferred claimLow evidence strength

Average handling time for the top 10 issue categories dropped from over 5 minutes to under 2 minutes, a 60% improvement.

Normalized claim

Time: 2 minutes decrease

AWS Machine Learning BlogAug 21, 2025Blog postInferred claimLow evidence strength

Average handling time for the top 10 issue categories dropped from over 5 minutes to under 2 minutes, a 60% improvement.

Normalized claim

Time: 60% decrease

AWS Machine Learning BlogAug 21, 2025Blog postInferred claimLow evidence strength

Average handling time for the top 10 issue categories dropped from over 5 minutes to under 2 minutes, a 60% improvement.

Normalized claim

Quantified impact: 30-40%

AWS Machine Learning BlogAug 21, 2025Blog postInferred claimLow evidence strength

Issues requiring human intervention fell from 30-40% to 20% within 6 months.

Normalized claim

Quantified impact: 30% increase

AWS Machine Learning BlogAug 21, 2025Blog postInferred claimLow evidence strength

Customer satisfaction scores increased by 30%.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Infosys, PPL
Provider: AWS
Maturity: Unknown
Linked source: AWS Machine Learning Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Customer Support
2Knowledge Management
3RAG

Build a knowledge base from call transcripts stored in S3.
Use Claude Sonnet on Amazon Bedrock to summarize and classify transcripts as relevant or irrelevant, then generate embeddings with Amazon Titan Text Embeddings and store them in Amazon OpenSearch Serverless for retrieval.
Use Step Functions and Lambda to orchestrate near-real-time ingestion, implement role-based access control, maintain metrics in DynamoDB, and expose the assistant through Streamlit.

Technologies

Amazon Bedrock Anthropic Claude Sonnet Amazon S3 AWS Step Functions AWS Lambda Amazon OpenSearch Serverless Amazon Titan Text Embeddings V2 AWS Secrets Manager AWS KMS AWS IAM DynamoDB Streamlit

Architecture

A generative AI technical help desk architecture ingests call transcripts from Amazon S3, uses AWS Step Functions and AWS Lambda for orchestration, applies Anthropic Claude Sonnet on Amazon Bedrock to summarize and classify transcript relevance, stores embeddings in Amazon OpenSearch Serverless using Amazon Titan Text Embeddings, secures data with AWS Secrets Manager, AWS KMS, and AWS IAM, tracks user and query data in DynamoDB, and presents the assistant in Streamlit with role-based access control and caching.

Sources & evidence1

Evidence: Low35/100Evidence strength