nPulse
A user is upset. Your AI agent, voice or text, keeps going in the same tone, same pace, same script. By the time your dashboard shows the disengagement metric, the user is gone.
nPulse closes that loop in real time. Continuous emotion evaluation from voice and from text. The signal goes straight into the LLM's reasoning, the TTS pace, and the agent's behavior mode. Switch to empathy. Slow down. Hand off to a human before the conversation breaks.
SIGNAL_FLOW
Architecture
Inputs
Engine
Outputs
SYSTEM_CAPABILITIES
Multi-vector emotional intelligence.
01
Voice Tone Analysis
Evaluates pitch, pace, volume, and prosodic patterns in real-time audio streams.
02
Text Sentiment
Continuous evaluation of text content for emotional signals and intent markers.
03
Real-Time Routing
Sub-second delivery of emotional context to LLM reasoning and TTS output layers.
04
TTS Modulation
Pulse-modulated speech output. Agent voice adapts pace and tone to caller emotional state.
05
Behavior Control
Switches agent behavior based on detected emotions. Drops script, opens conversation, or escalates.
06
Continuous Evaluation
Not snapshot-based. Tracks emotional trajectory across the entire conversation.
CASE_STUDIES
Proven at Scale
EdTech Mentorship
Mentor agent detects student disengagement in real-time. Triggers adaptive content delivery when attention drops. Running in production for thousands of learners.
Case Study →Teletherapy
Identifies anxiety and distress signals in therapy chat. Routes to a human clinician when emotional intensity exceeds thresholds.
Case Study →Support Agents
Customer support agents that detect rising frustration in voice or chat, adapt tone, and escalate to a human when de-escalation paths run out.
Case Study →