Every AI you use should know
what the others learned.
Most memory systems ask how a single model remembers. We asked a different question: what if every AI you connect shared the same knowledge? That question became the Neural Fabric.
A kernel, not a plugin
Qorbit is an operating system for AI memory. Not a plugin you add to one tool. Not a vector database you query from one model. An OS, with a kernel at its center, that sits between every AI you use and everything they need to know.
Today, when you ask Claude something, it knows what it was trained on. Close the window and everything it learned is gone. Open Cursor, ChatGPT, or any other tool and each one starts from zero. They share nothing.
The Qorbit kernel changes that at the infrastructure level. Every query from any connected agent is intercepted, enriched with relevant knowledge from the Fabric, and returned with sources, confidence, and a timestamp. Every write is validated before anything lands on the graph. Every write is recorded in a provenance chain. The kernel is the layer your AI tools were missing: one memory, one truth, shared by every agent you connect.
The kernel loop
A question enters. The kernel intercepts it, retrieves what matters, validates before writing, and returns an answer traceable to the exact facts that produced it. Every cycle makes the next one sharper.
Direct lookup F1 1.000 · Validated to 5M entities · 10-hop graph traversal · Spectral radius ρ(M) = 0.989–0.990 · $0 embedding cost
What makes it different
What we chose not to build
When memory lives in the model, it dies with the model. Swap providers and you start over. Correct a mistake and wait for retraining. Audit an answer and hit a black box. Fine-tuning offers no mathematical stability guarantee. It can degrade model quality in ways no one can predict or reverse.
When memory lives in the Fabric, every one of those problems disappears. Switch from Claude to Gemini tomorrow and lose nothing. Fix an error and every connected agent sees the correction on the next query. No fine-tuning delay, no GPU hours, no retraining. Every write is bounded by a spectral radius guarantee. Every answer is traceable to the exact facts that produced it. The model becomes what it should be: interchangeable infrastructure, not the place your knowledge is trapped.
That is the trade we made, and we stand by it.
Because memory is model-agnostic, any MCP-compliant tool connects with nothing but a config file. No SDK. No plugin. No install.
Any MCP-compliant tool connects over stdio or HTTP · Streamable HTTP endpoint on Cloud Run · Bearer token auth · 60 req/min · Gemini (Google AI Studio), ChatGPT, and others connecting as MCP support rolls out