Private, self-hosted meeting AI that runs on your hardware

Most meeting AI is someone else's cloud with a nice front end: a bot joins your call, the audio streams to a vendor, and the transcript, summary, and "who said what" all happen on machines you don't control. Daisy is a private meeting AI built the other way around. The processing happens on your hardware — botless capture, on-device transcription and speaker ID, and AI features through a model you choose. The meeting record is born on the computer in front of you and stays there.

That makes Daisy a self-hosted meeting AI in the only sense that matters for privacy: nothing is processed on a vendor's servers as a precondition of using it. The core loop is local and free, and the single step that can reach the cloud is opt-in and spelled out below.

What "private meeting AI" should mean

The phrase gets used loosely. A tool can call itself private while still uploading every word to a SaaS bucket under its own terms, behind an account it controls. So here's a sharper test: where does the audio get processed, where does the data live, and what has to phone home for the thing to work?

By that test, Daisy keeps the whole core on your machine. Recording, transcription, speaker identification (diarization), and full-text search all run locally, for free, with no account and no sign-up. There's no bot dialing into your call and no telemetry beacon. You install a desktop app and it works on the audio in the room.

Self-hosted, the desktop-app way

Be clear about what "self-hosted" means here, because the word usually implies a Docker image and a box you maintain. Daisy isn't that. It's a local desktop application — self-hosted in the truest sense, in that it runs on the computer you're already sitting at. There's no server to deploy, no container to patch, no reverse proxy to terminate TLS on. You don't operate infrastructure; you run a program.

For the r/selfhosted instinct, that's usually the right trade. The privacy win of self-hosting is "the data never leaves a machine I control." A desktop app delivers that without asking you to babysit a service. The data plane and the control plane are the same laptop.

Where your data lives

Every artifact a meeting produces stays on your disk. By type:

• Raw audio — saved as .wav, plus a compact stereo .opus for keeping. Open formats, your filesystem.
• Transcript — plain markdown. Grep it, back it up, feed it to any tool, or delete it.
• Speaker voiceprints — biometric data, so it's held in an encrypted vault, and it's deletable. This is what lets diarization recognize recurring speakers, and it never leaves the machine.
• API keys — same encrypted vault. No recovery path; they're yours to hold.
• License — a signed token on disk, verified offline. It never phones home. One license covers three devices.

Nothing in that list is locked behind a proprietary blob or a server you don't control. The files are the product, and they're on your hardware.

The one cloud-optional step

Honesty matters here, so: there is exactly one part of Daisy that can reach the internet, and only if you let it. Summaries, Q&A, and coaching need a language model. You pick how that runs:

• A local LLM via Ollama or LM Studio — fully local, nothing leaves the machine. This keeps even the AI features on your own hardware.
• A cloud model you bring (your own key, over HTTPS, no proxy in the middle). The transcript text goes straight to the provider you chose — OpenAI, Anthropic, Groq — under your account, not Daisy's.
• Neither — Daisy hands you the transcript plus a ready-made prompt to paste wherever you like.

That's the honest framing: private by default, your choice to extend. Not "fully private out of the box" — if you opt into a hosted model, transcript text leaves on that one path, because you told it to.

Live captions sit on a similar opt-in ladder. Desktops and Apple Silicon Macs run them on-device. Lighter laptops choose: on-device captions (heavier on the machine), a bring-your-own Deepgram realtime key for a lighter footprint, or skip live captions and take the full transcript when you stop. That Deepgram key is the only place the cloud touches transcription, and only in that narrow live-caption role on lighter laptops — the finalized transcript is always produced locally. Diarization is always on-device and free, in every configuration.

How it compares to cloud meeting tools

Cloud meeting AI — the Otter / Fireflies / Fathom / Granola category — works as a class roughly like this: a bot joins the call, the audio goes to the vendor's cloud, transcription and speaker labels happen there, and the record lives under the vendor's terms in an account you sign into, usually priced per seat.

Daisy inverts each of those choices:

• Processing location — your machine, not a vendor's servers.
• Capture — botless; nothing auto-dials into your meeting.
• Data residence — files on your disk in open formats, no hosted workspace.
• Account — none; no sign-up, no email to start.
• AI model — yours, whether a local LLM or your own cloud key (BYOK, no proxy).
• License — verified offline, never phones home.

This isn't a claim that hosted tools mishandle your data — it's a claim about architecture. If your standard is "the recording shouldn't be processed on someone else's servers," the difference is structural, not a setting.

Daisy runs on Linux (AppImage and .deb), Windows (.zip or NSIS installer), and macOS (Apple Silicon .dmg). There's a 30-day trial with no account, so you can confirm the local loop on your own hardware before deciding anything.