Voice AI Platform

Challenge

The client needed to scale their customer service operations without proportionally increasing headcount. Traditional IVR systems were frustrating customers with rigid menu trees, while human agents were overwhelmed with repetitive inquiries.

Key requirements included:

• Natural conversational flow that could handle complex multi-turn dialogs
• Real-time voice processing with minimal latency
• Seamless handoff to human agents when needed
• Comprehensive logging and analytics for conversation quality

Solution

Built a modular voice AI architecture with three core components:

1. Voice Processing Pipeline

Real-time speech-to-text using Azure Cognitive Services, optimized for Japanese language with custom vocabulary for domain-specific terms. Implemented streaming transcription to minimize perceived latency.

2. Conversation Engine

LLM-powered dialog management using carefully engineered prompts. The system maintains conversation context, handles interruptions gracefully, and knows when to escalate to human agents.

3. Voice Synthesis Backend

Text-to-speech with natural prosody and appropriate pacing for phone conversations. Implemented caching for common responses to reduce latency and costs.

Infrastructure

All infrastructure managed as code using Terraform across 5 Azure projects:

• Core API: FastAPI services on Azure Container Apps
• Voice Services: Azure Communication Services integration
• Management UI: Next.js dashboard for monitoring and configuration
• CI/CD: GitHub Actions with automated testing and deployment
• Observability: Structured logging, metrics, and alerting

Results

The platform successfully handles over 1000 calls daily with high customer satisfaction:

• 70% of inquiries resolved without human intervention
• Average handle time reduced by 40%
• Customer satisfaction scores maintained above baseline
• Engineering team scaled from 2 to 6 members during the project