No, not for the complete workflow, because Intel Macs lack the project’s live speaker-identification and voice-fingerprinting support, and semantic search is degraded.

• The README’s Download note says Intel Macs cannot use live speaker identification or voice fingerprinting.
• The stated reason is that ONNX Runtime stopped shipping macOS x86_64 binaries in version 1.24.
• Meetings still record and transcribe normally, so basic meeting transcription remains available.
• Notes semantic search falls back to keyword matching on this platform rather than providing equivalent semantic retrieval.

If participant-level labeling and semantic search are hard requirements, the platform limitation prevents full coverage. If you only need recording and transcription for Zoom, Teams, or FaceTime, part of the workflow remains supported.

Yes, because it combines a global hotkey, voice commands, and in-place editing of selected text in a workflow explicitly supported by the README.

• Voice dictation supports a global hotkey and automatically pastes text into any application.
• The AI agent supports GPT-5, Claude, Gemini, Groq, Tinfoil, OpenRouter, and local models.
• The Voice Assistant hotkey sends speech directly as an AI command without a wake word or cleanup pass.
• The README explicitly says highlighted text is edited in place; with a writable cursor, output is pasted automatically, otherwise it appears in a floating panel and is copied to the clipboard.

This matches cross-application rewriting, formatting, and drafting. Whether a specific application supports in-place replacement still depends on its writable cursor and OS automation permissions.

Yes, provided you accept the hardware usage and speed differences of local models; the README explicitly supports a path where audio stays on the device.

• The project description states that Whisper or NVIDIA Parakeet can provide fully private offline transcription, with audio never leaving the device.
• “Local or cloud — your choice” says transcription, AI reasoning, speaker diarization, and semantic search can use local models or cloud providers.
• Local Whisper supports Metal, CUDA, and Vulkan acceleration for AMD/Intel GPUs.
• The project states “no data collection, no telemetry” and uses the MIT License.

This makes local dictation and meeting transcription compatible with a local privacy boundary. However, enabling cloud AI, synchronization, or external models means the workflow is no longer fully offline.

Yes, because its desktop architecture closely matches your stack, although native modules and OS permissions add build complexity.

• The README lists React 19, TypeScript, Electron 41, better-sqlite3, whisper.cpp, and sherpa-onnx.
• The repository includes C and Objective-C code, indicating that speech inference and platform integration are not purely JavaScript concerns.
• The README provides packages for macOS, Windows, and Linux, plus API, MCP, and platform guides.
• The project uses the MIT License, allowing commercial use and modification.

The UI, data layer, and Electron integration are therefore good starting points. Native inference, automatic pasting, microphone access, and keyboard-monitoring permissions still require platform-specific work.

Yes, because the README covers call detection, live transcription, speaker features, note organization, and historical-media import in one workflow.

• Meeting transcription detects Zoom, Teams, and FaceTime calls and provides live speaker diarization, voice fingerprinting, and Google, Microsoft, or Apple Calendar integration.
• Local speaker diarization labels speakers on-device and recognizes voice fingerprints across meetings without cloud processing.
• Audio import supports drag-and-drop audio/video, batch uploads, and YouTube/audio URL transcription, with optional speaker detection.
• Notes provide folders, search, semantic search, cloud sync, and AI actions for organizing transcription results.

It therefore fits a unified workflow for live meetings and historical recordings. However, the README does not guarantee speaker-label accuracy under overlapping speech, noise, or poor recordings.

It depends: the interfaces and enterprise controls are explicitly present, but the README does not prove that these capabilities are fully self-hostable or satisfy your audit requirements.

• Features lists Public API & MCP for managing notes and transcriptions programmatically and connecting an AI assistant through the MCP server.
• Enterprise controls support organization policy, company SSO, SCIM, and centrally managed Amazon Bedrock or Azure OpenAI access without distributing cloud keys.
• Team spaces provide roles, invitations, and server-enforced membership for organizational collaboration.
• Although the project uses the MIT License, the README does not describe cloud sync, team spaces, and enterprise identity features as a complete offline self-hosted stack.

The project fits integration and centralized access control. If your requirement is enterprise-owned deployment of every backend, audit log, and data-residency component, the available documentation is insufficient to confirm fit.