Zero-egress inference
No inference data leaves the workstation. There are no cloud data processing agreements, no third-party inference audits, and no data-residency reviews to block adoption.
Security configuration scans become one-click templates. Because every AI model runs locally, scan content, credentials, and hostnames never leave the estate.
No inference data leaves the workstation. There are no cloud data processing agreements, no third-party inference audits, and no data-residency reviews to block adoption.
The orchestration engine is the sole credential custodian. No AI model ever sees a password, token, or connection string. This is enforced by the architecture, not by policy.
Credentials, hostnames, dataset names, IPs, and job-card identifiers become typed placeholders before any text reaches a model.
Shell steps run with pinned working directories, environment allow-lists, output caps, and timeouts. An opt-in OS sandbox profile is also available on macOS.
Every run is recorded. Every shell invocation writes a JSON audit line. Every AI suggestion and user decision lands in the execution history.
Runs pause before any node that deletes files, force-pushes, or removes infrastructure. This is on by default, and each step is confirmed on its own.
Michal, systems programmer at a Tier-1 global bank
Turn an hour-long, four-system relay into one click - and make 'your toolkit was out of date' impossible to hit by accident.
Four tools and ~12 manual steps become one Play button
A batch operator responsible for many LPARs
Run the same vetted scan across the whole estate on a schedule - and never let one back-level LPAR rot silently.
Manual relay times N LPARs becomes a schedule that runs itself
Domain segmentation, credential custody, the PII sanitizer, and the compliance implications are documented in full.