AI GTM workflows need approval gates, pacing, and visible controls
Zevari is designed for controlled LinkedIn and GTM workflows through Claude, Codex, and MCP-capable clients. Sensitive actions are staged, account-level controls stay visible, and operators can review what the AI prepared.
Control model
- Review gates for sensitive actions
Messages, comments, connection requests, publishing, and campaign activation are designed to be staged for operator review before execution.
- Pacing and account controls
Zevari uses warm-up state, daily and weekly limits, burst caps, duplicate checks, and stale-state checks to keep outbound work deliberate.
- Workspace-scoped context
AI clients work through Zevari workspace context, selected LinkedIn accounts, team membership, Library assets, campaigns, and approvals.
- Blocked action visibility
Blocked work, safety events, pending approvals, and recent activity are surfaced in the app so operators can inspect what happened and why.
Core boundaries
- AI client access
Claude, Codex, and MCP-capable clients call Zevari tools through authenticated workspace access.
- Write actions
Sensitive GTM actions are prepared and staged through Zevari's review boundary before execution.
- LinkedIn activity
Zevari is designed around controlled account access, account state, pacing, and safety checks rather than blind browser automation.
- Team operations
Workspaces separate members, sender accounts, billing context, Library assets, campaigns, approvals, and dashboard state.