GEO & Machine Readability — Help AI Understand, Not Merely Crawl
How to structure Work Identity data so AI can understand entities, relations, context, sources, and meaning instead of merely crawling text.
Crawlable does not mean understandable
AI may read every word and still fail to distinguish claims, proof, projects, results, and the relations among them.
Step 1 — Define entities clearly
Person, Organization, Profile, Project, Result, Evidence, Tip, Letter, and Event should exist as entities with distinct meaning.
Framework: Discoverability → Structure → Semantics → Relations → Understanding
Discoverability — can machines find it?
Structure — is the data organized clearly?
Semantics — what does each field actually mean?
Relations — how do entities connect?
Understanding — can AI correctly interpret the full context?
Step 2 — Semantics matters more than keyword stuffing
Repeating keywords does not help AI understand contribution, ownership, or proof.
Semantic structure should clarify roles and meaning.
Step 3 — Relations create context
Project → Result → Evidence is fundamentally different from three disconnected text blocks.
Relations help AI understand causality, attribution, and provenance.
Step 4 — Use source-aware structure
A Result should include source, timeframe, attribution, and verification context rather than only a number.
Step 5 — Create a Direct Answer and summary layer
AI benefits from a short summary for fast understanding, but it should link down to deeper sources.
Step 6 — Internal linking is machine navigation
Profile should link to Project; Project to Evidence; Tip to Related Project; Letter to relevant Decision or Principle.
Applying this on Forwork
A useful model: Entity → Attributes → Relations → Sources → Summary → Retrieval. GEO does not replace proof; it makes proof easier for machines to interpret correctly.
Conclusion
Do not only ask, “Can AI crawl this page?”
Ask: “Can AI correctly understand who did what, in which context, which result belongs to which work, and which source proves it?”
Discoverability → Structure → Semantics → Relations → Understanding.