How should Local AI Discovery scale attribution for a multi-location brand while avoiding content duplication and preserving hub integrity?

Local AI Discovery should keep locale signals anchored to hub facts instead of duplicating content. Each locale links to hub_fact_id with a locale_fact_id and its own proofs. Implement automated drift detection that flags diverging locale mappings; route updates through Change Control; version changes in a Change Log; publish locale changes to locale surfaces while preserving hub integrity. The provenance panel should attribute which locale contributed which proofs, without duplicating central content. Operational steps include: 1) define locale_fact_id per locale that references hub_fact_id; 2) attach locale-specific proofs to hub facts; 3) run nightly drift checks; 4) push changes via Change Center with a staged rollout; 5) provide an auditable history for executives.