Enterprise Contact Center
Excellence with Amazon Connect
Demonstrating Agentic AI, Conversational IVR, and Cloud-Native Architecture
No touch-tone menus. Just natural conversation.
Try the Live Demo
Experience a fully conversational IVR - no touch-tone menus, just natural language
Call the Demo
Voice-powered experience
+1 (617) 648-2440
- 1.Neural voice greeting (Amazon Polly - Joanna)
- 2.Speak naturally - no menu options
- 3.AI understands context and intent
- 4.Bedrock generates natural responses
Sample Scripts
Try saying these phrases
"What's my checking account balance?"
"How much money do I have?"
"Check my savings balance"
Tip: The bot understands variations and synonyms. Try phrasing requests in your own words!
127
Calls Today
2:34
Avg Handle Time
3
In Queue
84
Chat Sessions
Architecture Overview
Click on any component to learn more about its role in the solution
Feature Showcase
Deep dive into the technical capabilities demonstrated in this solution
Conversational IVR
Customers speak naturally instead of pressing buttons. Amazon Lex V2 understands intent, context, and variations in how people phrase requests.
- Natural language understanding
- 7 custom intents with slot types
- Multi-turn conversation support
- 0.75 confidence threshold
// Lex V2 Intent Configuration
{
"Name": "CheckBalance",
"SampleUtterances": [
"What's my balance",
"How much money do I have",
"Check my {AccountType} account"
],
"Slots": [{
"Name": "AccountType",
"SlotTypeName": "AccountTypeSlot"
}]
}Agentic AI with Bedrock
Claude 3 Sonnet powers intelligent decision-making, understanding complex requests and maintaining context across conversation turns.
- Multi-turn reasoning
- Dynamic action selection
- Context preservation
- Financial guardrails
# Agentic Orchestrator Pattern
class AgenticOrchestrator:
def process_turn(self, user_input, context):
# 1. Analyze intent with context
analysis = self._analyze_intent(user_input)
# 2. Execute appropriate action
result = self._execute_action(
analysis['action'],
analysis['parameters']
)
# 3. Generate natural response
return self._generate_response(result)Python Backend Services
Serverless architecture with specialized handlers for customer lookup, balances, transactions, payments, transfers, and AI orchestration.
- Python 3.12 runtime
- Shared utility layer
- Structured JSON logging
- Full type hints
# Lambda Handler Example
def lambda_handler(event, context):
correlation_id = log_event(logger, event, context)
try:
customer = get_simulated_customer(
customer_id=event.get('customer_id')
)
return build_lex_response(
intent_name='CheckBalance',
message=format_balance_response(customer)
)
except Exception as e:
logger.error(f'Error: {e}', exc_info=True)
return handle_error(e, correlation_id)Infrastructure as Code
Complete infrastructure defined in 8 CloudFormation templates, enabling consistent, repeatable deployments across environments.
- 8 nested stacks
- IAM least-privilege
- Resource tagging
- Environment parameters
# Main Stack Orchestration
Resources:
IamRolesStack:
Type: AWS::CloudFormation::Stack
Properties:
TemplateURL: !Sub '${TemplatesBucket}/iam-roles.yaml'
ConnectInstanceStack:
Type: AWS::CloudFormation::Stack
DependsOn: [IamRolesStack, LexBotStack]
Properties:
Parameters:
LexBotId: !GetAtt LexBotStack.Outputs.BotIdTechnical Deep-Dive
Explore the implementation details across all components
Amazon Connect Contact Flows
The contact flows orchestrate the customer journey, from greeting through Lex interaction to agent escalation. All flows are fully conversational with no DTMF menus.
{
"Version": "2019-10-30",
"StartAction": "welcome-greeting",
"Actions": [
{
"Identifier": "welcome-greeting",
"Type": "MessageParticipant",
"Parameters": {
"Text": "Thank you for calling Apex Financial Services. How can I help you today?"
},
"Transitions": {
"NextAction": "invoke-lex"
}
},
{
"Identifier": "invoke-lex",
"Type": "GetParticipantInput",
"Parameters": {
"LexV2Bot": {
"AliasArn": "$.LexBot.AliasArn"
},
"InputTimeLimitSeconds": "10"
}
}
]
}Real-Time Metrics
Live dashboard showing contact center performance metricsSimulated
127
Calls Handled Today
2:34
Average Handle Time
3
Contacts in Queue
84
Chat Sessions
92%
Lex Confidence
1.2s
Bedrock Latency
4.8
Customer Satisfaction
78%
Self-Service Rate
CloudWatch Dashboard
Auto-refresh: 5sCall Volume (24h)
Service Performance
Metrics are simulated for demonstration purposes. In production, these would connect to CloudWatch.
Built by CloudEvolve
CloudEvolve empowers businesses with innovative IT solutions. As a certified AWS Partner, we specialize in cloud architecture, Amazon Connect, application development, and business intelligence.
Our team transforms legacy systems into modern, efficient solutions, ensuring seamless integration and operational excellence. We've partnered with Fortune 500 companies across industries including hospitality, travel, and financial services.
50+
Projects Delivered
100%
Client Satisfaction
24/7
Support Available
Core Expertise
AWS Certifications
Services We Offer
- 1Amazon Connect Implementation
- 2Conversational AI Development
- 3Cloud Migration & Modernization
- 4Custom Application Development
- 5DevOps & Automation