01
The operating problem
Most paid media stacks automate platform actions but leave portfolio decisions fragmented. Teams still reconcile budget, creative, attribution, finance, and risk in separate workflows.
Enterprise ad automation should be treated as a decision system: what data is trusted, which actions are permitted, how uncertainty changes a recommendation, who approves material changes, and how the system returns to a safe state.
02
Reference architecture
A robust architecture separates ingestion, modeling, policy, activation, and evaluation. This prevents model output from becoming an uncontrolled platform action.
- Canonical event and spend model
- Identity and conversion-quality layer
- Marginal-return models with uncertainty
- Constraint engine for budget, risk, and approvals
- Activation adapters with idempotency and rollback
- Incrementality measurement and decision logs
03
What to measure
Optimize for contribution margin and incremental outcomes, not dashboard efficiency alone. Track recommendation acceptance, override reasons, confidence calibration, anomaly recovery time, and the share of spend covered by reliable measurement.