Whoever answers for the AI should control it.
Verillian is the AI governance layer that runs on your own devices, with the record kept on your own servers under keys only you hold.
The night shift that explains shadow AI
John Smith works in the emergency room of a hospital. He's always been someone who cares about people, the kind of person that goes above and beyond for his patients. Today has been an extraordinarily difficult day, even by emergency room standards. As he hovers over his patient, a sweet 6-year-old girl with a rare disease, he stumbles. He knows the medicine has to be administered immediately, but in that moment he catches himself. He stares at the two medication bottles in front of him, both correctly assigned to his patient, so nothing wrong there. But he's got this nagging feeling that there's something off about mixing these two medications together. He just can't remember exactly what it was, he's too tired to jog his memory that far back. A girl's life is at stake here.
So, without hesitation he reaches for his phone and whispers under his breath: “Sorry IT...” He snaps a photo of the bottles and asks FrontierAI Model V for some help. Model V scolds him for sending this PII over, but also catches the mistake that he knew was there all along. The explanation is clear, and he's able to make the necessary adjustments to save his patient's life. The next morning the little girl wakes up with a smile on her face. Her data may have been leaked, but she's alive.
Who can blame John for doing what he needed to do? No one can. But it can still be a HIPAA violation, with fines for the hospital and a career at stake for John. Nobody wrote a policy for that moment, and nobody can show afterwards what was sent. Verillian is for the next morning: so the hospital can show what was asked, under which rule, and what the record says.
Why we built it
Why now
AI crossed from drafting text to taking actions inside regulated work, and it did not wait for an approval process.
The gap
Security spent a decade perfecting detection, and AI that acts is where detection runs out: the report arrives after the action it describes. Nothing writes down what the AI was asked, what it did, or what came back. The industry's answer, visibility from another vendor's cloud, trades one loss of control for another.
What we built
A layer that runs on the device, where the work happens. You write the policy once and it reaches every machine, scoped to the role of the person at it. It enforces what you declared before an agent can act, with no model in the decision path. It seals what happened under a key only you hold. Nothing of yours reaches us, by architecture.
The stance
We'd rather under-claim and show you. What the product can't do is written down in one place, plainly, where your reviewer will look. And the record exists so you never have to take anyone's word for what your AI did, ours included.
See your policy decide on a real device
Book thirty minutes, bring one of your own requests, and watch it ruled live.