AI-native business operations platform
Northstar
A sanitized demonstration of production architecture for turning fragmented commercial workflows into one accountable operating layer.
This is a sanitized demonstration based on production architecture. Confidential client-specific workflows have been removed.Problem
A business was operating across disconnected tools, spreadsheets, inboxes, and handoffs. No one could reliably see the current state, the next owner, or the cost of an automated action.
Constraints
- Confidential client-specific processes
- Multiple external providers
- AI outputs need accountable human approval
- Strict separation between tenants and roles
Approach
The platform treats the operational workflow as first-class software: authoritative state, explicit transitions, provider adapters, scoped access, audit records, and human review at consequential moments.
Architecture
Key decisions
Human review is a state, not an afterthought
Drafts and recommendations wait in a clear review queue with a responsible role and audit trail.
Tenant boundaries at the core
Data access and event handling are scoped deliberately instead of relying on UI convention.
Cost is operational data
Provider use is measured beside workflow state so quality and spend can be managed together.
Failure handling
Provider errors are retained as workflow events, retried where safe, and surfaced to the operator. A failed AI call cannot silently advance a client-facing process.
Result
A coherent operating surface in place of a loose collection of SaaS tabs. Client workflows are removed from this public demonstration.
What I’d change next
Extend policy controls for more granular approval and provider-routing rules.