Reduce speaker attribution errors
Agent vs. customer turns, correctly tagged; even on overlap, hold music, IVR carryover, and three-way calls.
Make voice agents production-resilient
Real calls are messy. Our models are built for the actual conditions of contact center audio, not benchmark recordings.
Power QA scoring and analytics that actually work
Talk-ratio, sentiment-per-speaker, agent coaching: all of it depends on knowing who said what. Get that right, and everything downstream works.
Plug in beneath your existing stack
Works alongside any STT, any LLM, any voice agent framework. We don't replace your stack, we make it functional.
Use cases
Voice Agent input layer: Real-time speaker attribution so your agent acts on the right turn
Conversation analytics: Agent vs. customer separation for QA, coaching, and CSAT analysis
Compliance & Call recording: Speaker-attributed, timestamped records for regulated review
Agent assist & coaching: Per-speaker insights surfaced live for supervisors
Voice agent evaluation: Measure turn-taking, latency, and consistency in production agents
Features
Real-time diarization
Sub-second speaker attribution for live agent assist and voice agents.
Overlap & interruption handling
Tag who's talking when conversation overlaps.
Speaker identification
Tuned your models to recognize your contact center agents.
Confidence scoring
Flag uncertain turns for human review or fallback logic.
Enterprise-grade reliability
Built for the SLAs your customers expect.
hours processed
languages supported
latency for real-time workflows