The challenge.
Voice interactions involve several asynchronous systems: transcription, language understanding, action execution, and audio generation. A useful assistant needs clear state transitions and safe boundaries around the actions it can take.
The proposed solution.
An event-driven voice pipeline that converts speech to text, uses an LLM to interpret intent within a defined tool schema, and turns the response back into speech. Signed webhooks connect approved actions to external systems.
Engineering scope.
- Define a voice session lifecycle and typed event contracts.
- Integrate speech-to-text, language-model, and text-to-speech providers.
- Validate tool arguments and require confirmation for consequential actions.
- Implement webhook verification, retries, and privacy-conscious logging.
The stack.
One connected system.
Voice input
Session-aware audio capture
Speech-to-text
Transcription with explicit error handling
LLM orchestration
Constrained tools and validated arguments
Approved webhooks
Verified requests and idempotent actions
Text-to-speech
Streaming response audio
Key engineering decisions.
Unpredictable model output
Validate structured responses and tool arguments against a strict schema; do not treat generated text as trusted commands.
Conversational latency
Stream intermediate stages where possible and clearly represent listening, thinking, and speaking states.
Sensitive voice data
Minimize transcript retention, redact sensitive logs, and define explicit recording consent.
Intended outcomes.
These are design goals for this concept, not measured or delivered project results.
- A clear, observable conversation lifecycle.
- Controlled action execution with human confirmation where needed.
- Provider boundaries that support future integrations.