Customer support bots
Let support agents handle voice calls by transcribing customer speech into structured intents your bot framework can act on instantly.
Your AI agents need structured, clean voice input — not raw noisy transcripts bloated with filler words. Privocio's Agent output mode delivers token-optimized JSON your framework consumes directly, cutting LLM costs by up to 60%.
Privocio's Agent mode is a purpose-built output format for LLM agent pipelines. Instead of handing your framework a wall of text, the API returns structured JSON with speaker labels, timestamps, and cleaned segments — ready for your chain, tool, or function call.
{
"mode": "agent",
"segments": [
{
"speaker": "user",
"text": "Schedule a standup for tomorrow at 9am",
"start": 0.0,
"end": 2.4
}
],
"token_count": 12,
"raw_token_count": 31
}12 tokens instead of 31 — a 61% reduction on this example.
User speaks a command or query
Audio transcribed and structured
Clean, token-optimized output
LangChain, CrewAI, or custom
Works with any framework — LangChain, CrewAI, AutoGen, custom pipelines, or plain HTTP.
Let support agents handle voice calls by transcribing customer speech into structured intents your bot framework can act on instantly.
Voice-driven coding workflows where developers speak requirements and the agent receives clean, parsed instructions — no filler words, no wasted tokens.
Feed meeting audio through Privocio's agent mode to get structured speaker-attributed segments that your summarizer can process without hallucination noise.
Trigger multi-step workflows from natural language voice commands. Privocio delivers structured JSON your orchestrator consumes directly.
Agent mode strips noise before tokens hit your LLM, reducing costs and improving response quality.
Your audio is never used for model training. Self-hosted deployment keeps data entirely in your infrastructure.
Predictable flat-rate billing every 4 weeks — no per-minute surprises as your agent traffic scales.
Start with the free transcription tool or explore plans that scale with your agent workloads.