Trust Signals
Last updated: February 3, 2026
Trust signals reduce uncertainty. The more your claims are verifiable and consistent, the easier it is for an engine to include you.
What counts as a trust signal
- Attribution: who made the claim, and why they’re qualified.
- Verification: links to stable sources, methods, and artifacts.
- Consistency: the same definition across pages and over time.
- Boundaries: clear limits (“works for X, not for Y”).
Trust ≠ schema
Structured data helps parsing, but it doesn’t create meaning. If the text is ambiguous, markup will mirror the ambiguity.
Proof blocks: the simplest high-leverage upgrade
Add proof blocks near high-stakes claims (pricing, performance, compliance, accuracy, comparisons).
- Claim: one sentence, specific.
- Method: how it was measured.
- Boundary: where it does / doesn’t hold.
- Evidence: link to a stable artifact (docs, policy, benchmark, paper).
Semantic tie-in (why this also boosts semantics)
- Proof blocks force precise entity definitions (WHO) and constraints (WHAT).
- They improve intent alignment by answering “why should I believe this?”
- They reduce disagreement across engines by standardizing meaning.
Related pages
Continue through the AI Visibility ontology with these related nodes.
Next steps
Use trust signals to reduce uncertainty, then verify with a re-run.