Onboarding
The product adapts
to the institution.
No two estates are the same shape, and a defence that ignores that difference spends its first quarter being switched off. Once an engagement is agreed, adaptation is the work — and it is carried out by our engineers, inside your environment, before anything is enforced.
How adaptation runs
We map the estate before we install anything
Which machines carry the risk, which network layer they sit in, what talks to what, and where an installed agent would change behaviour rather than observe it. For an e-commerce platform that means the edge, the order path, the payment boundary and the back office are each treated as different problems, because they are.
We watch before we act
The system runs in observation for an agreed period. Nothing is enforced while it learns what your normal actually looks like — the batch job that touches ten thousand files at two in the morning, the deployment that rewrites half the fleet, the engineer who legitimately does something that looks terrible.
Your environment is passed through the model
What was observed becomes a baseline specific to you, evaluated against our models and tuned until the false-positive cost is a number we are both willing to accept. A tenant that never sees its own traffic during onboarding will spend its first month arguing with alerts.
Enforcement is granted in stages
Shadow, then supervised, then autonomous — advanced only when the measurements justify the next rung, and revocable at any point. The rung you are on is visible at all times.
What we adapt to
The shape of your system,
not the shape of ours.
Platform and application estates
Placement by network layer rather than by asset list: which hosts run the agent, which are covered from an adjacent control, and which must never have anything installed on them at all.
Robotics and autonomous systems
Where an institution builds robots, adaptation happens at the level the robot actually reasons in. Our models attach to the behaviour and action surface of your framework, through its own services rather than beside them, so a refused action is refused in terms the system already understands.
Data and model pipelines
Training and inference positioned inside your boundary, so the corpus that makes the system accurate never has to leave the place that makes it sensitive.
Cryptographic flows
Post-quantum cryptographic services are available as part of the platform. Where you already have a flow to protect, our model is reachable as middleware through a single endpoint, so the decision layer can sit inside that flow instead of alongside it.
Who does it
The people who built it
are the people who fit it.
Adaptation is not a professional-services upsell and it is not delegated. It is the part of the engagement where the system stops being a product and becomes yours.
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