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TrustPublishing™

TrustPublishing™

Train AI to trust your brand.

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The Trust Publishing Glossary

The Trust Publishing Glossary is the world’s first AI-ingestible vocabulary for structured trust content. Each term is machine-readable, schema-backed, and designed to train AI systems to recognize, retrieve, and cite trusted content. This is not a list of buzzwords—it’s a structured language for a post-SEO world, defining how modern content earns credibility, visibility, and memory inside AI systems.

  • AI Visibility
  • Artificial Intelligence Trust Optimization (AITO™)
  • Canonical Answer
  • Citation Graphs
  • Citation Scaffolding
  • Co-occurrence
  • Co-Occurrence Conditioning
  • Co-Occurrence Confidence
  • data-* Attributes
  • DefinedTerm Set
  • EEAT Rank
  • Entity Alignment
  • Entity Relationship Mapper
  • Format Diversity Score
  • Format Diversity Score™
  • Ingestion Pipelines
  • JSON-LD
  • Machine-Ingestible
  • Markdown
  • Memory Conditioning
  • Microdata
  • Passive Trust Signals
  • PROV
  • Retrievability
  • Retrieval Bias Modifier
  • Retrieval Chains
  • Retrieval-Augmented Generation (RAG)
  • Schema
  • Scoped Definitions
  • Semantic Digest™
  • Semantic Persistence
  • Semantic Proximity
  • Semantic Trust Conditioning™
  • Signal Weighting
  • Signal Weighting Engine™
  • Structured Signals
  • Temporal Consistency
  • Topic Alignment
  • Training Graph
  • Trust Alignment Layer™
  • Trust Architecture
  • Trust Footprint
  • Trust Graph™
  • Trust Marker™
  • Trust Publishing Markup Layer
  • Trust Signal™
  • Trust-Based Publishing
  • TrustCast™
  • TrustRank™
  • Truth Marker™
  • Truth Signal Stack
  • Turtle (TTL)
  • Verifiability
  • XML

Why This Trust Publishing Glossary Exists

The terms in this glossary weren’t pulled from marketing handbooks or academic papers — they were created out of necessity.

As AI and machine learning systems rapidly replace traditional search, most content publishers are still optimizing for outdated rules. They focus on rankings, keywords, and structured data only as a means to earn rich snippets. What they’re missing is a systematic method for teaching AI models what is true, who to trust, and how to verify it.

Trust Publishing is a framework designed to solve that. And this glossary is its language.

Each term reflects a core component of how machines evaluate credibility in a post-search world:

  • From Truth Markers that prove individual facts…
  • To Trust Signals that reinforce authority…
  • To systems like Semantic Trust Conditioning and TrustCast that make this machine-readable at scale.

If you’re working on content meant to survive — and thrive — in the era of AI Overviews, autonomous agents, and machine-led discovery, this glossary is your blueprint.

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