GenAI Contact Center on AWS with Amazon Bedrock
Delivered by Converg-IN
01Summary
A contact center and BPO services provider partnered with Converg-IN to bring production Generative AI to its quality-assurance and agent operations on AWS. Using Amazon Bedrock with Amazon Nova models, the solution automatically analyzes every customer call to produce summaries, QA scorecards, sentiment, CSAT and compliance flags, grades sales calls, alerts supervisors to risk, and gives agents real-time answers grounded in each client’s approved SOPs, scripts and FAQs. Each client’s data and knowledge are kept securely separated.
02About the Customer
The customer is a contact center and BPO services provider that runs customer-support and sales programmes for clients in the United States and other international markets, delivered from its operations centers in India. Its client programmes span industries such as healthcare, financial services, telecommunications and retail.
The customer combines experienced contact-center agents with Generative AI to improve service quality and efficiency. Its services include omnichannel customer support, quality assurance and call analytics, sales support, agent enablement and process automation.
03The Challenge
Like most contact centers, the customer relied on QA analysts manually reviewing a small sample of calls. Most conversations were never reviewed, so compliance risks, poor customer experiences and coaching opportunities were found late or missed entirely, and scoring varied between reviewers. Agents also had to search through client SOPs, scripts and FAQs during live calls, which increased handling time and slowed ramp-up for new agents. As client programmes grew, scaling manual QA with headcount was not cost-effective.
The customer needed a secure, scalable and cost-efficient Generative AI capability to analyze customer conversations and give agents accurate answers grounded in each client’s approved knowledge, in order to:
- Automate call analysis instead of relying on a small manual sample.
- Generate call summaries, QA scorecards, sentiment and tone analysis, key topics and keywords, automated CSAT scores and compliance flags.
- Grade sales calls against defined sales-quality parameters.
- Alert supervisors about low-scoring calls and compliance risks.
- Provide real-time AI Agent Assist using approved SOPs, scripts and FAQs.
- Keep each client’s data and knowledge separate and secure.
04The Solution
Converg-IN designed and implemented production Generative AI capabilities on AWS using Amazon Bedrock with Amazon Nova models, applying AWS best practices for security, responsible AI and scalability. Two Generative AI use cases are in production:
GenAI Quality Control & Conversation Intelligence
analyzes every call transcript and generates summaries, QA scorecards, sentiment and tone, keywords, topics, CSAT and compliance flags. Sales calls are evaluated against sales-quality parameters for sales enablement and revenue intelligence, and low-score or compliance conditions trigger supervisor alerts.
AI Agent Assist
gives agents real-time, grounded answers from approved client SOPs, scripts and FAQs using retrieval-augmented generation (RAG).
How AWS was used
Foundation models
Amazon Bedrock with Amazon Nova Pro performs detailed conversation analysis and produces structured JSON QA scorecards; Amazon Nova 2.0 Lite with prompt caching delivers fast Agent Assist responses; Amazon Nova Multimodal Embeddings creates knowledge-base and query vectors. Models are selected based on quality, latency, token consumption, request volume and cost.
Retrieval and data
Amazon RDS for PostgreSQL (Multi-AZ) with pgvector stores call details, QA rubrics, results and knowledge vectors; Amazon S3 stores call-recording archives and knowledge documents; Amazon ElastiCache Serverless (Valkey) caches sessions and results.
Processing
Amazon API Gateway and AWS Lambda ingest and validate transcripts and call metadata; Amazon SQS (FIFO and standard queues) provides reliable asynchronous processing; Amazon ECS on AWS Fargate runs the GenAI QC workers and platform APIs; Amazon EventBridge schedules jobs; Amazon SES and Amazon SNS deliver alerts and reports.
Application access
AWS Amplify hosts the web front end with Amazon Cognito authentication, protected by Amazon CloudFront, AWS WAF, Amazon Route 53 and Application Load Balancers.
Security and operations
IAM, AWS KMS, AWS Secrets Manager, Amazon Inspector, AWS CloudTrail, Amazon CloudWatch and Amazon DevOps Guru provide security, audit, monitoring and operational insight, with GenAI consumption and cost tracked by use case and Region in AWS Cost Explorer.
Delivery automation
AWS CodeCommit, AWS CodePipeline and AWS CodeBuild build and release container images to Amazon ECR for deployment to Amazon ECS.
05Architecture
The primary production workload runs in AWS US East (N. Virginia). Call recordings and transcripts originate from a telephony environment in US East (Ohio) and are ingested through Amazon API Gateway and AWS Lambda into Amazon SQS. GenAI QC workers on Amazon ECS combine each transcript with the client’s QA rubric and send it to Amazon Nova Pro on Amazon Bedrock, storing the resulting scorecard in Amazon RDS for PostgreSQL and raising alerts for low-score or compliance conditions. For Agent Assist, queries are embedded with Amazon Nova Multimodal Embeddings, matched against client knowledge vectors in pgvector, and answered by Amazon Nova 2.0 Lite. An India delivery environment in Asia Pacific (Mumbai) supports in-Region processing and internal applications.
06Results and Outcomes
Quantitative results
Business outcomes
Faster, more consistent and objective quality scoring.
Earlier identification of compliance risks, low-scoring calls and coaching opportunities.
Improved visibility into customer sentiment, CSAT, call topics and sales-call quality.
Real-time Agent Assist with answers grounded in approved client knowledge.
Secure separation of client data and knowledge.
Scalable asynchronous processing for high-volume workloads, with centralized monitoring of processing time, model latency, errors, queue depth and token usage.
Cost-aware model selection based on quality, latency, token consumption, request volume and cost.
07AWS Services Used
- Amazon Bedrock (Amazon Nova Pro, Amazon Nova 2.0 Lite, Amazon Nova Multimodal Embeddings and Amazon Nova Lite)
- Amazon ECS on AWS Fargate
- Amazon EC2
- AWS Lambda
- Amazon ECR
- Amazon RDS for PostgreSQL with pgvector
- Amazon ElastiCache Serverless (Valkey)
- Amazon S3
- Amazon API Gateway
- Amazon SQS
- Amazon EventBridge
- Amazon SES
- Amazon SNS
- AWS Amplify
- Amazon Cognito
- Amazon CloudFront
- Amazon Route 53
- AWS WAF
- Application Load Balancer
- Amazon VPC
- NAT Gateway
- AWS CodeCommit
- AWS CodePipeline
- AWS CodeBuild
- AWS IAM
- AWS KMS
- AWS Secrets Manager
- Amazon Inspector
- AWS CloudTrail
- Amazon CloudWatch
- Amazon DevOps Guru
- AWS Cost Explorer
08About Converg-IN
Converg-IN Private Limited is an AWS Advanced Tier Services Partner that delivers cost-effective cloud and Generative AI solutions built on AWS Well-Architected principles. Our team of 15+ certified cloud architects covers the full cloud lifecycle: consulting and cost optimization, migration (including SAP and VMware), security, modernization, DevOps, and 24x7 managed services.
Our Modernization & GenAI practice helps customers move Generative AI from experimentation to production using Amazon Bedrock and the wider AWS AI/ML stack. We have delivered GenAI workloads for customers in sports technology, education, life sciences analytics, and AI-driven contact centers. We design every solution to be simple to deploy, secure by default, and cost-efficient to operate.
We deliver with an automation-first approach, using infrastructure-as-code to stand up environments quickly and repeatably. Security and governance are central to every engagement. With a proven track record across industry verticals, Converg-IN is a trusted partner for organizations adopting Generative AI on AWS.
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