Enterprise AI deserved better than a firewall and a prayer
Your security stack was built for software that plays by the rules. Your AI doesn't. That's the gap we exist to close.
VISION
What we stand for
Most vendors treat AI security as a filter: check the inputs, check the outputs, call it defence. That work matters, and we do it. But it's a fraction of the problem. Four beliefs shape everything we build.
Checking inputs and outputs is necessary, but risk doesn't start at the prompt or end at the reply. It runs through the instruction that shapes how the model behaves, the data it reads, the tools it calls, and the actions it takes in production. We secure all of that, not just the two ends.
Security is a lifecycle problem, not just a filter
Large organisations don't run one model. They run several, swap them as better ones arrive, and add RAG, agents, and their own fine-tuned versions. A security layer tied to one model is obsolete the day the stack changes. Working with any model isn't a checkbox. It's the only way to stay useful.
Model-agnostic is not a feature. It's a condition of being useful
A bank or an insurer often can't send sensitive data to someone else's cloud, and shouldn't have to in order to secure it. So we deploy where your data already is, in your own environment, on-prem or in your cloud. The security comes to the data, not the other way around.
Deploy where the data lives
A regulator or a board doesn't want assurances. They want evidence. So everything we do leaves a record: what the AI was asked, what it did, what we allowed or blocked, and why. When someone asks you to prove your AI is under control, you can.
Security you can prove, not security you have to trust
Adem Caglayan
Co-Founder & CPO
The only way to secure something that reasons is to treat its whole lifecycle as the attack surface, from the prompt that shapes its behaviour to the tools it calls in production. That conviction is the product.
