Speaker-accurate by default
Diarization that holds up under crosstalk, overlapping speech, and far-field audio. The conditions every real meeting actually has.
Any language, any acoustic condition
Noise, accents, code-switching, overlap, everything handled. Your users don't record in studios; your pipeline shouldn't pretend they do.
Optimized for advanced LLM-powered transcription platforms
Enhanced note-taking, including speaker separation and timestamping features for a granular overview of each meeting’s roster and agenda.
Seamlessly integrates into existing stacks
Production-ready models built for scale. Process millions of audios without re-architecting your stack.
Use cases
Meeting & interview notetakers: Per-speaker turns, accurate names across recordings, clean inputs for LLM summarization
Live captioning & broadcast: Real-time speaker labels for accessibility-grade captions
Customer care & call analytics: Agent vs. customer separation for QA scoring, talk-ratio, sentiment per speaker
Compliance & audit archives: Timestamped, speaker-attributed records that satisfy regulated retention and review
Healthcare AI scribing: Reliable separation of clinician, patient, and bystanders for correctly attributed clinical notes
Features
Speaker diarization
Who spoke, when, and for how long
Speaker identification
Match voices to known identities across files
Confidence scoring
Per-segment reliability surfaced as metadata
STT orchestration
Plug into the STT you already use; we add the layer it's missing
hours processed
faster than real-time diarization at scale
languages supported
“The real win was reliability at high load because every time attribution failed, downstream features suffered.”
Aleksandr Ogaltsov
AI Scientist @ Jamie