Customer Support Chat & Voice Bot – Technical Documentation

This article details the architecture, workflow, operational considerations, and future scope for the Utility Customer Support Chatbot and Voicebot application. The system leverages AWS services, including Amazon Lex, Bedrock, Connect, Cognito, and OpenSearch Serverless, to provide seamless text and voice based customer support.The architecture is designed for enterprise scalability and aligns with the AWS Well-Architected Framework.

Use Case

A Leading energy provider in UK with 10000+ household connections, set out to transform its customer support experience by deploying AI powered chatbots and voicebots. With a growing customer base, inquiries spanned billing issues, prepayment meter support, outage updates, and general utility service FAQs. Traditional support channels struggled with long wait times and inconsistent service quality. The company needed a solution that could deliver fast, reliable, and personalized customer support across both digital and voice channels while maintaining scalability and cost efficiency.

Challenges Faced

• High Call Volume and Long Wait Times: Customer support agents were frequently overwhelmed by repetitive queries (billing dates, meter top ups, outages), leading to delays and customer dissatisfaction.
• Inconsistent Service Quality: Responses varied depending on agent expertise, causing inconsistencies in tone and accuracy of information.
• Limited Scalability: Peak periods (e.g., billing cycles, outages) caused service backlogs, as scaling with human agents was costly and slow.
• Fragmented Knowledge Access: FAQs and support documents were scattered across internal systems, making it difficult for agents (and customers) to get quick, accurate answers.
• Operational Inefficiency: Manual workflows required repeated data entry and escalations, increasing costs and lowering overall support efficiency.

Objective

• Automate Customer Interactions: Reduce agent dependency by automating common queries (billing, payments, outages) via chatbot and voicebot.
• Seamless Omnichannel Support: Provide customers with consistent experiences across web chat and voice calls through Lex and Connect.
• Centralize Knowledge Access: Build a unified knowledge base connected to Bedrock with OpenSearch Serverless to ensure fast and accurate responses.
• Enhance Scalability and Reliability: Deploy a serverless, AI driven solution that automatically scales during peak periods without impacting response times.
• Improve Customer Satisfaction: Deliver instant, reliable answers while enabling smooth escalation to human agents when needed.
• Reduce Operational Costs: Lower call center costs by offloading repetitive queries to AI while improving efficiency for human agents.

Rationale

• AWS Native & Serverless: Leveraging Lex, Bedrock Agents, OpenSearch Serverless, and Connect provided a fully managed, scalable architecture with minimal operational overhead.
• AI Powered Intelligence: Amazon Titan Nova Pro, integrated with Bedrock Agents, enabled contextual, human-like responses, improving accuracy and customer trust.
• Unified Knowledge Base: Bedrock Knowledge Base with OpenSearch ensured quick retrieval of information from structured datasets (S3), centralizing FAQs and service info.
• Security & Compliance: With AWS Cognito for authentication and AWS’s enterprise grade encryption and IAM controls, the system ensured secure handling of customer data.
• Monitoring & Observability: CloudWatch provided end to end visibility into chatbot and voicebot performance, ensuring proactive troubleshooting and reliability.
• Future Proof & Scalable: Modular design with S3 data sources and serverless workflows ensured easy updates, integration with new channels, and the ability to scale with their growing customer base.

Key Performance Indicators (KPIs)

Solution Overview

Objective

To develop a scalable, intelligent, and multi channel customer support solution for utility services, enabling users to interact via both text and voice. The system should answer FAQs, escalate queries to human agents when necessary, and ensure secure, efficient, and high performance operations.

Key Achievements

• Multi channel support: Integrated chatbot (web) and voicebot (phone call).
• AI powered responses: Leveraged Amazon Titan Nova Pro via Bedrock Agents.
• Searchable knowledge base: Integrated Bedrock Knowledge Base with OpenSearch Serverless.
• Secure authentication: Managed via AWS Cognito.
• Serverless & scalable: Architecture leverages managed services for high availability.
• Low latency performance: Optimized vector search and caching.
• Call escalation: Voicebot integrated with Connect for seamless agent handover.

System Architecture

Key Components:

1. Frontend:
   

   • S3 Static website hosting, storage of templates, brand profiles, and generated assets
   • CloudFront CDN for static content and secure user access

2. Authentication:
   

   • Cognito User Pool for secure login and session management.

3. Chatbot Processing:
   

   • Amazon Lex receives text/voice input.
   • Lex invokes Bedrock Agent for AI powered responses.

4. Knowledge Base:
   

   • Bedrock Knowledge Base connected to OpenSearch Serverless for vector search.
   • Data stored in S3 as CSV files for ingestion and sync.

5. Model:
   

   • Amazon Titan Nova Pro as the base model for reasoning and answering.

6. Voicebot / Call Handling:
   

   • Connect routes voice calls to Lex.
   • Supports escalation to human agents.

7. Logging & Monitoring:
   

   • CloudWatch for logs, metrics, and error monitoring.
   • S3 for call records from Amazon Connect.

High Level Workflow

Chatbot

1. The user accesses the application . If unauthenticated, they are redirected to Cognito for login.
2. The user sends a text/voice query via the web app.
3. Query sent to Lex.
4. Lex forwards requests to Bedrock Agent.
5. Agent retrieves relevant data from Knowledge Base (OpenSearch).
6. Titan Nova Pro processes and formulates the final response.
7. Response is sent back to the user.

Voicebot

1. The user calls the customer support number.
2. Connect answers and routes the call to Lex.
3. Lex processes the audio input, invokes Bedrock Agent.
4. The agent retrieves relevant data from the knowledge base and sends a response back.
5. Connect plays the response to the caller.
6. If unresolved, escalates to a real agent.

Detailed Workflow

Chatbot Journey

Authentication & Session

• Users authenticate via Amazon Cognito User Pool, which validates credentials and issues JWT tokens.
• These tokens are exchanged through a Cognito Identity Pool, which provides temporary AWS credentials.
• Using these credentials, the frontend securely calls Amazon Lex without exposing static AWS access keys.

Message Processing

• For text input, the frontend uses RecognizeTextCommand (AWS SDK).

• For voice input, the frontend uses RecognizeUtteranceCommand with PCM audio streams.

Lex identifies the intent and forwards the query to the Amazon Bedrock Agent.

Knowledge Retrieval & Response Generation

• The Bedrock Agent is linked to a Knowledge Base (OpenSearch Serverless as a vector store, data synced from S3).
• Relevant documents are retrieved, and Titan Nova Pro generates a response using RAG.

Response Delivery

• Lex returns the response, which is displayed in the chat UI.

Voicebot Journey

Call Handling

Customer calls are received by Amazon Connect and routed through a predefined Contact Flow.

Speech to Intent

Connect streams caller audio to Amazon Lex for speech recognition and intent matching.
If the intent requires AI support, Lex triggers the Bedrock Agent.

Response Generation

• Bedrock Agent queries the Knowledge Base in OpenSearch.
• Titan Nova Pro generates the final response.

Response Playback

• Lex passes the text response back to Connect.
• Connect plays the answer using its built in TTS for enhanced natural voice.

Escalation Path

 After each interaction, Connect asks if the user has any further queries. The user can either continue asking questions, request to speak with an agent, or choose to end the call.

Monitoring and Operations

• CloudWatch:
  

  • Monitors Lex and Bedrock Agent latency.
  • Tracks Connect call durations and abandonment rates.
  • Logs errors from KB lookups and API calls.
  • Logs records in S3

• Error Handling:
  

  • CloudWatch Alarms for failures in Lex → Bedrock or Bedrock → OpenSearch flows.
  • Automatic retries for transient failures.

• Operational Dashboards:
  

  • Lex conversation metrics.
  • Connect call statistics.

Security and Compliance

• Authentication & Authorization:
  

  • AWS Cognito for user auth.
  • IAM roles with least privilege for Lex, Bedrock, Connect.

• Data Security:
  

  • All data encrypted at rest (S3 SSE) and in transit (TLS).

• Compliance:
  

  • Architecture aligns with AWS security best practices.
  • Meets GDPR data privacy requirements for handling customer data.

Performance and Cost Optimization

• Performance:
  

  • Enabled vector search in OpenSearch for fast KB retrieval.
  • Optimized Lex session attributes to reduce repeated queries.

• Cost Optimization:
  

  • Amazon Connect configured for optimal agent utilization.
  • CloudWatch log retention tuned to business needs.

Sustainability

• Serverless Design: Serverless architecture reduces idle compute usage.
• Minimal Infrastructure: Minimal persistent infrastructure, leveraging AWS managed services.
• Efficient AI Model Usage: Efficient model usage via Bedrock reduces carbon impact.

Future Scope

1. Multi language Support :

Enable the chatbot and voicebot to handle queries in multiple languages (e.g., Spanish, French, Hindi, etc.) by integrating Amazon Translate and multilingual models in Bedrock.

Business Impact:

• Expands the customer base to non English speaking users.
  Improves customer satisfaction by allowing customers to interact in their preferred language.
• Strengthens the company’s brand in diverse geographical markets.

2. Integration with CRM :

Connect the chatbot and voicebot with CRM systems (e.g., Salesforce, HubSpot) to access customer history, preferences, and past interactions.

Business Impact:

• Enables personalized responses, increasing customer satisfaction and retention.
  Reduces resolution time by automatically fetching relevant customer data.
• Supports upselling and cross selling opportunities based on customer history.

3. Proactive Customer Notifications :

Implement outbound communication capabilities to send proactive messages (via SMS, Email, or push notifications) for service updates, payment reminders, outage alerts, etc.

Business Impact:

• Enhances transparency and trust by keeping customers informed.
• Reduces inbound call volumes since customers receive answers before asking.
• Improves operational efficiency by lowering repetitive support queries.

4. Advanced Analytics & Insights Dashboard :

Build a BI dashboard integrating data from Lex, Bedrock, Connect, and CloudWatch for deep analytics on conversation trends, sentiment, resolution time, and escalation rates.

Business Impact :

• Allows management to make data driven decisions for improving customer service.
• Identifies frequent pain points, enabling proactive issue resolution.
• Tracks agent and bot performance for continuous optimization.

5. Integration with Payment Systems :

Allow customers to make bill payments directly through the chatbot/voicebot by integrating with secure payment gateways (e.g., Stripe, PayPal).

Business Impact :

• Creates a self service revenue channel, improving cash flow.
• Reduces dependency on human agents for transactional tasks.
• Enhances convenience, improving customer loyalty.

6. Sentiment Analysis for Escalation :

Use sentiment analysis to detect frustration or dissatisfaction in user messages and trigger real time escalation to a human agent.

Business Impact :

• Prevents customer churn by addressing dissatisfaction immediately.
• Improves first contact resolution rates.
• Builds a reputation for empathetic, responsive support.

7. AI powered FAQ Expansion :

Continuously train and expand the knowledge base using AI to detect gaps and auto generate new Q&A pairs from customer conversations.

Business Impact :

• Keeps the bot up to date without heavy manual input
• Increases query resolution rates over time.
• Reduces operational costs by minimizing manual KB updates.

Compliance with AWS Well-Architected Framework

Conclusion

The Utility Customer Support Chatbot and Voicebot solution provides a robust, scalable, and AI powered customer engagement platform. By leveraging AWS’s managed services, the system delivers secure, low latency, and cost effective operations while remaining adaptable for future enhancements.