Useful meeting memory usually asks for too much trust
Most meeting tools make transcription and summaries convenient by sending sensitive conversations to someone else's infrastructure. That tradeoff does not work for every team, especially when the discussion is confidential, regulated, or simply private.
Hush began with a different premise: the computer in the room should be able to capture and organize the meeting without turning the meeting itself into cloud data.
One local workflow from recording to recall
The product brings several jobs into one private workflow:
- Capture microphone and system audio
- Transcribe and separate speakers on the Mac
- Produce summaries, decisions, and action items grounded in the transcript
- Search past meetings and return cited answers
- Keep the core experience usable without a network connection
The interface has to make sophisticated local processing feel calm and understandable. Privacy is part of the product behavior, not a settings-page promise.
Product decisions shaped by the trust model
Local processing affects onboarding, model downloads, performance feedback, storage, search, and failure recovery. The product must explain what is happening without making the user learn the underlying machine-learning stack.
That constraint shaped both the application and its beta path: clear system status, traceable outputs, controlled distribution, and claims that can be demonstrated rather than assumed.
Hush has a working product, a private-beta path, and a standalone public site. These screens now show the production interface through the product's own synthetic sample meeting. Test findings and beta evidence can be added as they become ready to share.