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.