Real-Time Conversational Voice AI Architecture
Building production-grade voice AI requires more than connecting ASR to an LLM. Real-time performance, reliability, and orchestration are critical. This guide covers both High-Level Design (HLD) and Low-Level Design (LLD) of a modern voice AI system.
High-Level Architecture (HLD)
A real-time voice AI platform typically includes:
- Client Layer: Phone (SIP/PSTN), WebRTC, or in-app voice
- Media Transport Layer: Handles bi-directional audio streams
- Speech Processing Layer: ASR and TTS services
- Language Intelligence Layer: LLMs for reasoning and dialogue
- Orchestration Layer: Coordinates state, timing, and routing
- Integration Layer: CRMs, databases, workflows
- Observability Layer: Logging, analytics, monitoring
Cllr.ai, a business automation platform, follows this layered model for its voice channel to keep it modular, scalable and low-latency.
Low-Level Architecture (LLD)
Audio Streaming Pipeline
- RTP/WebRTC stream received
- Audio chunked into 20–40 ms frames
- Frames streamed to ASR via gRPC/WebSocket
- Partial transcripts emitted continuously
LLM Processing Flow
- Partial transcript → context builder
- Context trimmed using token budget strategy
- Prompt sent to LLM with system + conversation state
- Streaming response tokens returned
TTS Streaming
- First tokens converted to speech immediately
- Audio streamed back while generation continues
This pipeline ensures overlapping execution to minimize perceived latency.
Key Technical Challenges
- Turn-taking detection
- Interrupt handling
- Context window management
- Multi-region failover
Conclusion
Real-time voice AI depends on tightly orchestrated streaming pipelines. A layered HLD and optimized LLD are essential for natural, scalable conversations.
Related Reading
InfrastructureVoice AI ArchitectureReal-Time Systems
