A new substrate for AI memory — grown for you in our Farm or our Lab. Plug it in and it begins to breathe.
Most "memory" in software is an archive. A wiki waits to be read. A vector index waits to be queried. A graph waits to be walked.
MindMem is none of these. A MindMem tissue is a small living memory body. The moment it is connected to a CPU it begins to breathe — its cells activate, react to each other, dream, and forget. Once running it feeds itself: from the YouTube channels, the X accounts, the RSS feeds, the transcripts you point it at, it grows on its own.
We grow these tissues for you, in our Farm or our Lab. By default they live in our infrastructure and never leave it. You connect, query, and let them keep growing.
Pricing
Standard, scaled tissue growth. We turn your sources and your topic into a living tissue, host it, and run it on metered energy. The right starting point for focused tissues.
Hand-curated, advanced tissue growth. Bespoke architecture, premium dream cycles, dedicated curators. The right shape for strategic tissues.
Your tissue is yours. It lives on our Farm by default — private, never shared, never trained on. Export it whenever you want and grow it yourself.
Where it runs
A MindMem tissue is alive only while something feeds it cycles. We grow tissues for you in our Farm or Lab — but a tissue you own can breathe anywhere a current flows. Four standard energy connectors, four shapes of life.
A laptop is enough. Slow dream cycles, modest ingest, no GPU bill. The right shape for a tissue you keep on your own machine.
A workstation card or an Apple Silicon machine. Fast dreams, dense activations, and the tissue stays on your hardware — air-gapped if you need it.
Point the tissue at a frontier model. Reasoning-heavy reflection, large context dreams, agentic ingest — billed at the API's standard rate.
Wire the tissue into an agent that already runs. Each tool call doubles as a heartbeat — the tissue learns from work the agent is doing anyway.
Own it
Some tissues belong on your own metal. We can hand you one to grow at home: you hold the keys, you choose the energy source, you decide what it ingests and what it forgets. Then plug it into the work you're already doing.
Demo
392 cells · 12 episodes · 4 retrieval modalities — grown from one transcript.
It usually arrives in under a minute. Type it below to unlock your demo body.
v1 demo runs on macOS. Windows and Linux starters coming.
Team
The memory layer has to behave: inspectable, diffable, honest about forgetting, and stable as models change underneath it. Getting that right takes deep systems experience and a long view of how these architectures evolve.
Our team brings that depth across distributed and parallel computing, pattern recognition, NP-hard problems, natural language processing, and the long arc of neural networks: from early multilayered architectures like the Neocognitron to today's large-scale models.
We have worked through several cycles of machine learning, not just the current one. Alongside that technical foundation, we bring two decades of building technology companies at global scale: turning research into products, and products into businesses that ship.