Enterprise
AI automation your security team will sign off on
Agents with permission tiers, workflows with audit trails, models behind data boundaries — deployed in your VPC or on-premise, and operated 24/7. Governance is the architecture, not an add-on.
Identity & access
SSO/SAML, role-based access across teams and agents, per-organization isolation. Builders build; viewers view.
Your infrastructure
On-premise or your cloud account — data residency and the exit door stay yours. Deployment options.
Audit everything
Every agent action logged with input and outcome; execution history streamed to your SIEM. Compliance becomes a sponsor, not a blocker.
Permission-tiered agents
Read-only → draft-for-approval → autonomous, per action type. Irreversible actions start behind human approval and graduate on evidence.
Data boundaries for AI
Decide which data classes may reach which model providers; multi-provider routing enforces it. Sandboxed tool execution contains the blast radius.
n8n at enterprise grade
Queue mode, HA workers, Git-backed environments, external secrets. The n8n Enterprise setup.
From pilot purgatory to production
Most enterprise AI initiatives die between the demo and the rollout. Our playbook goes the other way: a real process in production for one team within six weeks, measured weekly, then scaled by replication — each new process reusing the plumbing of the last. We wrote the whole approach up in the Enterprise AI Automation Guide.