Trust center

Control is part of the workflow, not a promise beside it.

Steadframe is designed so each AI employee has a company owner, a limited job, scoped connections, explicit handoff rules, and a visible history of work.

Read the privacy policy Data deletion

Your company workspace

Private knowledgeAI employeesApproved connections

Company-scoped data

Knowledge, employees, connections, and work records are scoped to the organization that owns them.

Provider authorization

Google and Microsoft connections use their official OAuth flows and can be disconnected by the customer.

Approval boundaries

Customer-facing and sensitive actions follow the approval settings and topic rules defined for the employee.

Visible activity

Checks, drafts, escalations, approvals, and failures remain available for operational review.

Operating model

How work moves through Steadframe

1
Observe only allowed work
The employee receives work from its assigned source and checks organization, employee, and connection scope before acting.
2
Retrieve relevant knowledge
Only organization-owned sources that match the request are added to the working context.
3
Apply controls
Sensitive topics, confidence settings, suppression, opt-outs, and approval rules determine the next path.
4
Record the outcome
The result, escalation, error, or approval remains visible to the company team.

Knowledge

Companies decide what to upload, which employee may use it, and when it should be replaced. Missing knowledge should create a gap or escalation rather than an invented company answer.

Human authority

Draft-only review is available for customer-facing work. Sensitive topics can remain mandatory handoffs even where a company enables more autonomy for routine actions.

Connections

OAuth connections remain tied to the company and assigned employee. Credentials are not displayed back through the product, and customers can revoke access through their provider.

Transparency

Reliability views, action queues, notifications, and activity records help teams understand what needs attention instead of hiding automation behind a single success message.

Policies and support