Your Schema Says One Thing. Google's NLP Sees Another. That Gap Is Your Content Strategy

Your schema markup declares what your brand is. Google's NLP models decide what machines actually see. The distance between those two is the most precise content gap analysis you can run, and you don't need to be a developer to build it. Using the agentic coding tool of your choice (Antigravity, Claude Code, or Codex), you can stand up the whole pipeline in an afternoon.

This session walks through that pipeline step by step. Extract an entity corpus from your schema.org markup. Score it with the Google Cloud Natural Language API to learn which terms Google already recognizes and which exist only in your own vocabulary. Serialize the results into a queryable knowledge graph, then compare it against competitor crawls to see where competitors publish against your positioning and where nobody covers a topic at all.

The payoff: combination articles that stack several under-recognized entities per piece, weighted with internal data that makes each page the citable source AI engines want.

At this SMX Now presentation, you will learn how to:

• Measure your baseline discoverability: score which of your brand's entities Google's NLP grounds in the Knowledge Graph and which it reads as anonymous strings, so you know exactly where machines can't find you.
  
• Close the gaps that block discovery: use combination articles, DefinedTermSet and sameAs markup, and internal data published as PropertyValue facts to make your positioning vocabulary retrievable and citable by search and AI engines.
  
• Make discoverability a managed KPI: re-run the entity audit quarterly and track coverage scores, entity-query impressions, and AI citations instead of treating discoverability as a one-time project.