Your Knowledge,
Managed with Private AI.
Mnemne is in private beta. Get early access now!
What Mnemne is
Point Mnemne at your documents. Ask a clear question.
Get a briefing with a lead paragraph and themed sections. Each fact shows a link to a specific document. Not a list. Not a chat. You receive a ready-to-use response.
Your files stay on your machine and no data leaves your device. And there's no server to send to. The app logs every outgoing network call, every hostname, and every byte so you can see what the app is doing.
Mac download is available, please sign up below. We are adding a Windows app next. Designed for teams that need privacy or can't use cloud services.
Why we built it
Modern AI runs on an implicit arrangement.
You send your data to a company, they route your data through a data center that processes it, retains it, and sometimes trains on it. When these AI companies change their terms, which they can do, how your data is handled and protected may change too.
Your exposure to privacy risk and environmental harm follow from this arrangement, namely sending your information to a remote data center. But take away the data center, and the risks disappear. That's why we built Mnemne.
The pillars
100% Private
Your privacy matters. Most AI services ask you to trust a document, their terms of service. We designed the system so we have no way to see your data. And the app publishes a live network log, every outbound call, timestamped, so you can verify it yourself.
We're transparent about our practices.
Environmentally responsible
Cloud AI keeps GPUs running constantly, they stay warm and are always on, while Mnemne only uses hardware you already have and draws power when you're actually working. Every query shows what you saved, energy, water, CO₂e, compared to the cloud.
Our method is backed by a published paper.
100% Secure
Your data stays where you keep it. Mnemne on your Mac stays on your Mac. No Hyacinth server sits between you and your files. No queries get logged, no backups go to remote buckets, and no shared endpoint exists. The system we don't run isn't something we can breach.
Ethical
A business model that respects your data. We never train on your queries because we never see them. We don't build datasets from your documents because we cannot access those documents. No data ever leaves your control.
Yours to keep
Mnemne does not take custody of your files. Your originals stay where you keep them, unchanged, in their original formats. We don't store them. We don't claim ownership. We build a temporary index that points to your files, not a vault. Nothing moves. Delete Mnemne, and your files are all exactly where you kept them.
Who it's for
- Privacy For anyone whose work depends on privacy, such as attorneys handling privileged material, doctors and clinicians, therapists, financial advisors, accountants, researchers, journalists. Anyone who needs AI on documents that can't be pasted into a chat window.
- Cost For anyone tired of paying by the token.
- Custody For anyone who wants to know exactly where their data is.
- Footprint For anyone who has read the sustainability numbers on hyperscale AI and would rather not be part of the demand curve.
Mnemne runs on any Apple Silicon Mac (M1 or later) with 16 GB of RAM, macOS Tahoe or newer. Windows is next.
The same product, at organizational scale.
Mnemne Enterprise runs the same product at organizational scale, on Mnemne-Certified hardware that stays on your network. It's built for teams and small organizations that don't want or can't have cloud dependencies.
Your data stays within your infrastructure. No Hyacinth server sits between you and your data.
The appliance is yours from day one. The software runs indefinitely. If you stop paying for updates, the system keeps working.
For regulated organizations, like law firms, financial services, healthcare, defense contractors, Mnemne Enterprise is the AI that doesn't require you to route sensitive documents through someone else's data center.
Learn more
Request beta access at mnemne.aiRead the peer-citable methodology paper that ships with the product:
Per-Query Environmental Accounting for Local-First AI (Ernewein & Somol, 2026)