Voice AI Architecture: Building Sub-200ms Conversational Systems

The Latency Imperative

In voice conversations, humans perceive delays above 300ms as unnatural. For AI voice agents to feel conversational rather than robotic, the entire pipeline — from speech recognition to LLM processing to speech synthesis — must complete in under 200ms. This is not a model problem; it is a systems architecture problem that requires careful optimization at every layer.

• Turn detection: 50–80ms (VAD + endpointing)
• Speech-to-text: 40–60ms (streaming ASR)
• LLM inference: 30–50ms (first token)
• Text-to-speech: 20–40ms (streaming synthesis)
• Network overhead: 10–30ms (WebRTC/SIP)

Streaming Architecture

Traditional request-response architectures are fundamentally incompatible with real-time voice. We use a fully streaming pipeline where ASR begins transcribing while the user is still speaking, the LLM starts generating before the full transcript is complete, and TTS begins synthesizing audio from partial LLM output. This pipelining approach reduces perceived latency by 60–70% compared to sequential processing.

Turn-Taking and Interruption Handling

Natural conversation involves complex turn-taking dynamics — backchanneling ("uh-huh"), interruptions, overlapping speech. Our voice AI handles these using a combination of Voice Activity Detection (VAD), energy-based endpointing, and semantic completion detection. When a user interrupts the agent mid-sentence, the system stops synthesis within 50ms, processes the interruption, and responds naturally.

Multilingual Voice Pipelines

Enterprise deployments often require handling multiple languages within a single call. Our voice architecture supports seamless language detection and switching, with dedicated ASR and TTS models per language. Arabic-English code-switching — where speakers mix languages mid-sentence — is handled using a unified multilingual ASR model trained on code-switched corpora.

• Language detection: real-time, per-utterance
• ASR models: Deepgram Nova, Whisper fine-tunes
• TTS models: ElevenLabs, custom Arabic voices
• Code-switching: unified multilingual decoder

Scaling to Thousands of Concurrent Calls

Production voice AI deployments for large enterprises must handle thousands of simultaneous calls. We architect for this using stateless agent instances behind a load balancer, with session state stored in Redis. Each call is an independent WebRTC session routed through a TURN server, enabling horizontal scaling without shared state bottlenecks.