Voice AI for mental health
Self-hosted voice-to-voice pipeline replacing premium commercial APIs — ~90% cost reduction with privacy-first architecture.
// OUTCOME
- 01~90% cost reduction vs comparable commercial voice AI stacks.
- 02Conversations stay on infrastructure the client controls.
- 03Unit economics that allow scaling without raising prices on users.
// PROBLEM
A mental health conversation platform was hitting unit economics walls. Commercial voice AI APIs cost so much per conversation that scaling meant either raising prices on vulnerable users or shutting the door on them. They also had real privacy obligations — mental health conversations can't sit on third-party vendor servers indefinitely.
// SOLUTION
- 01Open-source transcription (Whisper variants) tuned for emotional speech.
- 02LLM reasoning layer with mental-health-aware prompting and safety guardrails.
- 03Affordable voice synthesis replacing premium commercial TTS.
- 04Real-time latency optimization so conversations feel natural.
- 05Privacy-first architecture with full audit trails and client-controlled data.
// STACK
PythonOpenAI WhisperLLM APIs (Anthropic / OpenAI)Custom voice synthesisReal-time transcriptionVector DB
VOICE AIMENTAL HEALTHINFRASTRUCTURELLM