Compatibility & Requirements
- Platforms: Desktop v1.10.0+ (macOS/Windows). Linux support pending.
- Hardware: GPU recommended for advanced features (e.g., Full Diarization).
Transcription & Media Sources
- Sources: Live recording (Zoom, Meet, Teams, local microphone) or file upload.
- Real-Time Transcription: Disabled by default. Toggle in settings (requires high-end hardware).
- Interactive Transcript:
- Double-click text to edit transcription.
- Click transcript segments or speaker names to jump to that timestamp in the audio player.
Speaker Diarization (Identification)
Configure under Preferences (Sliders icon, top-right):
| Diarization Mode | Performance Cost | Description |
|---|---|---|
| No Diarization | None | Raw transcript without speaker labels. |
| Basic Diarization | Minimal (Default) | Differentiates βUserβ (you) from βOtherβ (all other audio combined). |
| Full Diarization | High (+20-40% time) | Identifies individual speakers by unique voice characteristics. |
Management: Click a speaker label to rename across the entire transcript.
Meeting Summaries
Summaries use the active system model configured in Settings > AI Providers > LLM.
Templates
Access via the 3-dot menu next to the recording player.
- Pre-built Templates: General Meeting, Sales Call, Engineering Meeting.
- Custom Templates: Create per-meeting or load existing. Click Apply Summary Template to send to the LLM.
- Regenerate Summary: Select
Regenerate Summaryfrom the 3-dot menu to update after editing transcripts or templates.
Action Items & Agentic Follow-ups
Proposes tasks based on summary/transcript using active Agent Skills (MCPs, Agent Flows).
- Execution: Select Run to execute. No actions run automatically.
- Inspection: Click View Arguments to inspect parameters before execution.
- Regenerate: Select
Regenerate Action Itemsfrom the 3-dot menu to update.
QA & Search
Ask Questions Tab
- Opens a dedicated workspace containing the embedded transcript for interactive QA.
Semantic Search
- Accessed from the right sidebar.
- Searches transcripts and summaries semantically using a local, on-device vector database and embedding model.