Introduction
Customer support is evolving. In 2025, users don’t want to wait in queues or repeat themselves; they expect instant, intelligent responses.
With Amazon Lex handling conversations and Amazon Bedrock generating contextual replies, you can automate 60–80% of common support interactions, without sacrificing personalization.
This post walks through how to build an AI-powered customer support bot using AWS-native tools.
Key Services Used
| Service | Role |
|---|---|
| Amazon Lex | Handles dialogue, captures intents and slots |
| AWS Lambda | Connects Lex with Bedrock, parses input/output |
| Amazon Bedrock | Uses Claude or Titan to generate helpful, human-like answers |
| CloudWatch | Tracks usage and errors |
| S3 / DynamoDB | Logs conversations or stores session context |
Use Case: Support Chatbot for a SaaS Platform
Goal: Automate common support queries such as:
- Password resets
- Pricing questions
- Feature availability
- Onboarding guidance
Workflow Overview
- User starts chat via website or app (Lex embedded)
- Lex identifies intent via NLP
- If intent is known → return canned response
- If intent is unclear → call Lambda → Bedrock → generate response
- Lex delivers reply or escalates to human agent
Example Lambda → Bedrock Prompt Flow
Python
prompt = f"""
You are a customer support assistant for a SaaS product.
Respond concisely and helpfully.
Customer said: "{user_input}"
"""
- Lambda sends prompt to Bedrock (Claude/Titan)
- Processes structured output
- Sends reply back to Lex for delivery
Pro Tips
- Use Lex’s Fallback Intent to trigger AI responses when no match is found
- Store chat history temporarily in DynamoDB to maintain session memory
- Use confidence score to decide when to escalate to human
- Implement rate limiting and retry logic for Bedrock API calls
Benefits
| Metric | Result |
|---|---|
| Avg. resolution time | ⬇ 55% |
| First response time | < 2 seconds |
| CSAT score | ↑ by 18% |
| Agent workload | ⬇ by 40% |
| SLA violations | Near-zero for Tier 1 queries |
Advanced Use Cases
- Multilingual support using Bedrock’s language models
- Voice-based support with Lex + Polly + Bedrock
- Account-aware answers using contextual metadata from CRM
Conclusion
You don’t need a separate AI stack to automate support.
With Lex, Lambda, and Bedrock, AWS gives you a seamless, secure, and scalable way to create smart support agents that resolve, route, and learn—on their own.
