Deploying conversational voice agents to production is vastly different from building text-based LLM agents. Voice systems operate under a tight latency budget of under 800ms. If your agent takes longer than a second to respond, the conversation feels unnatural and breaks down.
1. The Audio Pipeline Architecture
Our speech agent stack relies on a three-stage real-time stream pipeline:
- Speech-to-Text (STT): High-accuracy, low-latency streaming transcription using Whisper-based model networks.
- LLM Engine: Optimized open-source or commercial models running with token-streaming enabled over dynamic prompt structures.
- Text-to-Speech (TTS): Expressive, conversational synthesis with sub-200ms time-to-first-byte (TTFB).
Explore the full range of systems we support on our dedicated AI & LLM Systems Integration page.
2. Crucial Polish: Interruption Handling
Humans do not wait for the other person to finish speaking if they want to correct them. Handling interruption requires building active interruption listeners that immediately kill the TTS audio buffer playout the millisecond user speech is detected in the input socket channel. Failing to implement this results in agents talking over users, creating a frustrating experience.
3. State & Context Synchronization
Voice agents need to perform actions—like querying databases, updating CRM pipelines, or booking meetings. We use state machines (e.g. XState) coupled with tool-calling capabilities to guarantee that database writes and UI updates occur synchronously as the agent speaks.
To integrate conversational bots or smart automation into your product line, feel free to get in touch with our team.