Voice-native AI sounds impressive—until you learn most vendors transcribe audio to text first, then analyze. That method throws away emotional data buried in tone and delivery.
Modulate took a different path. Velma analyzes audio directly. They call it an Ensemble Listening Model. No text conversion happens first. Over 20 million minutes of conversations get processed daily, catching what text-based systems completely miss.
Real-time monitoring spots emotion shifts. Policy violations. Fraud attempts. Even deepfakes. Customer service managers can set up alerts—triggers fire when callers show distress or aggression. Modulate claims 51% better accuracy than competing models. Runs at 18 times lower cost. That matters when you're analyzing millions of calls.
Transcription? Handled. Actually beats specialized transcription services on accuracy. Beyond that—topic extraction, summary generation, AI voice agent evaluation, harassment flagging. Workflow triggers let you automatically escalate calls. Generate compliance reports. Queue recordings for coaching sessions.
Training data came from hundreds of millions of hours of real conversations. That's why it understands context better than models trained on general internet text. Fraud detection teams use Velma to catch social engineering attempts. Community moderators deploy it to identify toxic behavior before things escalate.
Modulate integrates with CCaaS platforms and telephony providers. Voice-native architecture means you're not losing information in translation steps. For enterprises drowning in voice data, that architectural choice makes a real difference.
