The inventory builds itself
Connector scans discover AI systems, models and datasets across the organisation. Nobody has to keep a spreadsheet of what exists, which is the artefact that goes stale first.
AI & product teams
Governance that reads your stack, not your calendar.
The fastest way to make engineers resent compliance is to ask them to describe, in a document, work that their tools already record. The evidence is read from the systems you build in.
How it fits delivery
Built into the work, not bolted on.
Evidence from the tools you use
Source control, model registries, experiment tracking and cloud ML platforms are read where they already are. Compliance stops being a parallel set of documents describing your work.
Risk scored on real attributes
Assets are scored against a rule library first and a model only for what the rules do not match. Scores never quietly downgrade, and a critical rating always goes to a person.
Obligations arrive as work
When something changes — a new model in production, a new regulatory duty — the affected controls, policies and evidence are named, with owners, rather than announced.
Nothing invented about your systems
Where a scan cannot see something, the result is recorded as undetermined rather than as a pass or a failure. You will not be asked to explain a finding the platform could not observe.
Governance that keeps pace with release
Evidence has a validity period and re-scans are scheduled, so the record reflects what is running now rather than what shipped last quarter.
Next / evaluate the platform
Bring your frameworks.
See the operating system.
We will walk through the platform against your own governance and compliance needs, and what getting to audit-ready would take.