Every voice AI converts speech to text and throws away 50% of the signal. Orpheus gives your agent two ears: one to hear how the customer really feels, one to hear how the agent actually sounds. One API. Both sides of every call.
A customer says "Sure, sounds good." Your agent hears agreement. But a human rep would hear the flat tone, the long pause, the falling pitch — and know the deal is about to die.
Customers decide within 30 seconds if they're talking to a robot. Your agent doesn't know it's losing them because it can't hear the disengagement happening.
Nearly half of customers use polite language to mask their real reaction. "Let me think about it" means no. Your agent hears the words and schedules a follow-up that will never convert.
Today, zero AI agent platforms analyze vocal biomarkers in real time. Your agents optimize scripts and prompts while ignoring the richest signal in every call: the human voice.
Imentiv hears the customer. Deepfake detectors hear the agent. Only Orpheus hears both sides of every call — in one API call.
Your agent finally understands HOW the customer feels — not from their words, but from their voice. 26 physiological biomarkers, extracted in real time, interpretable by your LLM.
Your agent finally knows how it sounds. Before the customer hears it. A frame-level quality gate that catches every TTS artifact your ears would miss.
Click a scenario. See what your agent misses — and what Orpheus catches.
During a live call, stream the customer's audio to Orpheus. Works with Retell, Bland, Vapi, Twilio, or any SIP/WebRTC stack.
Orpheus returns physiological biomarkers, paralinguistic state, trend analysis, and real-time alerts. Not a black-box emotion label — interpretable signals your LLM can reason about.
Add the Orpheus context to your system prompt. Your agent now makes decisions based on how the customer sounds, not just what they say. Conversion goes up. Drop-offs go down.
# Your agent's call handler async def on_customer_speech(audio_chunk): # What you do today: words only transcript = await stt(audio_chunk) # What Orpheus adds: the other 50% sense = await orpheus.analyze(audio_chunk) # Now your LLM sees the full picture response = await llm.complete( system="""You are a sales agent. Adapt based on acoustic signals.""", user=f""" Customer said: {transcript} Voice signals: Stress: {sense.stress} ({sense.stress_trend}) Engagement: {sense.engagement} Alert: {sense.alerts} """ )
Orpheus is in private beta with select AI agent teams. Request access and we'll set up a live analysis of your agent's calls within 48 hours.