Voice AI for Field Ops

Overview

Built a production voice AI system for field operations that handles real-time audio streaming, maintains context across conversations, and supports role-specific dialogue flows. The system required sub-second latency, reliable session management, and robust error handling for production use in field service environments.

High-Level Architecture

Architecture shown at high level. Internal implementation details omitted for confidentiality.

What I Owned

• •Architected real-time audio streaming pipeline with bidirectional WebSocket communication
• •Implemented session lifecycle and state management with Redis caching
• •Optimized latency and reliability metrics, reducing call drops and timeouts
• •Built monitoring and observability systems for production debugging
• •Deployed and maintained AWS infrastructure with auto-scaling capabilities

Technical Challenges

• Reliability: Implemented robust reconnection logic and session state management to handle network interruptions gracefully.
• Latency: Optimized WebSocket keepalive mechanisms and implemented timeout handling for long-running conversations.
• Session Lifecycle: Built state machines to manage session initialization, active conversation, and cleanup phases reliably.
• Context Management: Designed context-aware dialogue system with structured memory retrieval for role-specific flows.

Outcomes

Successfully deployed a production voice AI system that handles real-time conversations with role-specific context. The system demonstrates reliable performance in production, with robust error handling and optimized latency. Solved critical production issues including call drops, timeouts, and session lifecycle management, resulting in a stable platform ready for scale.