For production LLM inference

Securing the foundation of the inference stack.

  • KV cache first
  • We never see the prompt

Why we exist

The list of things that go wrong keeps growing.

A new detector for every attack, every surface, does not scale.

Every surface. One computation. They differ upstream. They meet here.

So we watch KV pages.

Those failures already share one place: the model's computation. Text, voice, tools, and agents all land there.

And we learn their shape.

From your own traffic we learn how your workload normally computes. What doesn't fit surfaces, attack or ordinary breakage alike. One baseline, not a detector per failure.

A baseline learned from your traffic, with one page that doesn't fit A band traces the normal shape of a workload's KV pages. The pages sit inside it. One page sits outside the band and is marked as not fitting. doesn't fit your baseline
Learned from your traffic. Not from a list of attacks.

Design partners

A small number of design partners.

You bring a workload; we sit with your team until it proves itself, or doesn't.

Become a design partner