AI Foundations Self-Location →

AI Foundations Self-Location provides a structured way to make a person, project, organization, system, paper, AI agent, dataset, hypothesis, or other entity accurately locatable within future information systems.

The Self-Location Standard defines the complete source-supported record: what the entity is, where it came from, who has authority to maintain it, what it claims, what it does not claim, how it relates to other entities, and where its canonical source can be found.

The Self-Location Registry provides the operational intake and indexing layer. It receives an Index Manifest, checks the canonical source and stated authority, records a registry decision, and creates a searchable entry pointing back to the full record.

The registry does not become the author, owner, source, or authority of the records it indexes.