AI-Driven Voice Automation: Amazon Connect Customer Support

Explore how D3Clarity helped Didaxis successfully integrated Nova 2 Sonic AI into its Amazon Connect contact center to automate high-volume, low-complexity customer inquiries through bilingual voice automation. By embedding conversational AI directly into existing contact flows and grounding responses in an approved internal knowledge base, the organization reduced repetitive calls, improved average handle times, enhanced agent productivity, and delivered faster, more consistent self-service experiences in both English and Spanish. The solution provided seamless escalation to live agents when needed while creating a scalable AI foundation for ongoing contact center modernization and operational efficiency.

CASE STUDY

4-MINUTE READ

Nova 2 Sonic AI voice automation integrated with Amazon Connect contact center

The Challenge at Hand

Reducing Live Agent Load with Conversational AI Support

Didaxis operates a high-volume customer support contact center on Amazon Connect, handling inbound calls related to program access, account inquiries, scheduling, and operational FAQs. A significant percentage of these were repetitive, low-complexity requests that did not require a live agent. Yet without a scalable self-service option, these interactions consumed valuable agent time. This led to higher average handle times, reduced agent availability for complex issues, and rising operational costs as call volumes increased.

Although Didaxis maintained a structured internal knowledge base for agents, that content was not accessible to callers. The organization wanted to leverage conversational AI to:

Automate responses to common questions,
Ensure accuracy and compliance with approved knowledge,
Support English and Spanish interactions,
Verify the caller identification,
Provide account-specific information to the application,
Seamlessly escalate to live agents when needed.

The goal was to improve contact center efficiency without compromising service quality.

Innovative Solutions Unleashed

Integrating Bilingual Voice AI into Amazon Connect

To address Didaxis’s needs, any solution had to:

Integrate with the existing Amazon Connect environment,
Support natural, voice-based interactions,
Maintain strict controls around knowledge retrieval and response accuracy,
Provide seamless escalation to live agents for complex scenarios.

D3Clarity designed and deployed an AI-driven voice automation solution using Nova 2 Sonic Voice AI, fully embedded within the Amazon Connect contact center. This solution combined speech-to-speech processing, natural language understanding, and intent classification to deliver real-time automation for common customer support inquiries.

Key Features of the AI Automation Solution

Conversational AI Routing: Incoming calls are routed through AI-enabled contact flows that understand caller intent and deliver automated support.
Bilingual Support: Callers receive voice interactions in both English and Spanish without changing contact flows.
Knowledge Base Integration: AI retrieves answers directly from the Didaxis internal knowledge base, including program FAQs, policies, and procedures, ensuring accurate and compliant responses.
Identity-Aware Personalization: After secure verification, the system provides personalized account details where applicable.
Intent Classification & Confidence Scoring: Determines whether inquiries can be resolved through self-service or require escalation to live agents.
Seamless Escalation Workflow: Low-confidence or complex cases are smoothly transferred to human agents.
Performance Monitoring: Logging and analytics track AI resolution rates, caller outcomes, and automation performance.

This voice AI automation strategy increased self-service coverage without disrupting existing agent workflows and created a foundation for continued AI expansion.

Transformative Results Achieved

Increased Efficiency, Accuracy, and Self-Service Adoption

Integrating Nova 2 Sonic Voice AI into Amazon Connect significantly reduced the volume of repetitive customer inquiries that previously required live agent support. Routine questions are now resolved directly through automated, AI-powered conversations.

Operational Impact

Improved Agent Utilization: Agents were able to focus on complex, high-value support cases rather than repetitive tasks.
Reduced Average Handle Time: Automated responses lowered the time spent on routine interactions.
Increased Contact Center Capacity: Overall agent efficiency improved without adding headcount.
Faster Caller Resolutions: Callers experienced quicker outcomes via self-service, elevating customer satisfaction.

Quality and Compliance Improvements

Consistent, Accurate Responses: Because AI responses are grounded in the approved internal knowledge base, accuracy and alignment with alignment increased.
Reliable Self-Service: Reducing dependence on live agents improved governance and decreased operational risk.
Actionable Insights: Interaction logs provided visibility into automation performance and caller trends, enabling ongoing optimization.

Scalable AI Foundation

Most importantly, the project established a scalable AI automation framework within Didaxis’s Amazon Connect environment. This positions Didaxis to:

Expand automation coverage into new customer support domains.
Open future channels for customer interactions, such as webchat or SMS.
Enhance bilingual self-service capabilities.
Continue modernizing the contact center with Amazon Connect AI integrations.
Drive sustainable cost-savings and efficiency over time.

35%

AI Containment Rate represents interactions fully resolved by the AI solution without requiring human intervention.

22%

Reduction in AHT through the use of AI-driven voice interactions, intent-based routing, and accelerated resolution paths. The expected decrease in average handle time (AHT) for low-complexity calls was also 22%.

30%

increase in Agent Occupancy. This improvement was enabled through AI-powered self-service containment, automated intent handling, and confidence-based escalation guardrails that ensured only complex or low-confidence interactions were transferred to live agents.