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AI Trust Signals

Map the signals that help AI systems decide whether a source is coherent, current, and reusable.

Trust and Provenance Raw markdown

Trust is public evidence

AI systems rarely have a private conversation with your business before deciding whether your content is worth using. They judge based on public evidence.

That evidence includes timestamps, source consistency, author clarity, cross-page agreement, topical focus, and the way structured files line up with visible pages.

What trust signals look like

  • Freshness that shows up in update history
  • Stable page types with clear purpose
  • Glossary terms that keep language consistent
  • FAQ pages that answer predictable questions without contradiction
  • Directory listings that describe an asset the same way everywhere
  • Schema and machine-readable files that agree with human-facing copy

What breaks trust

Trust falls apart when public assets disagree with each other. A service page says one thing, a directory listing says another, and the data file says something else. To a machine, that looks like noise.

It also breaks when content is stale, undocumented, or hidden behind formats that are difficult to parse.

What a trust layer should do

A good trust layer does not try to perform authority. It simply makes the site coherent, current, and legible. Over time, coherence becomes a citation asset.