Open test

AI is already answering questions about your page

Can machines trust the facts
on your page?

Measure whether crawlers and models can retrieve, parse, extract, verify, and relate what you published — without inventing agreement where the evidence conflicts.

No account. Evidence preserved for every finding.

How it works

  1. 01

    Fetch like a machine

    Raw HTML, crawler identities, robots policy, and rendered DOM — kept separate.

  2. 02

    Extract atomic facts

    Only propositions the source supports. Questions are not claims. Qualifiers survive.

  3. 03

    Compare representations

    Visible HTML vs JSON-LD vs other machine layers. Contradictions stay visible.

  4. 04

    Preserve evidence

    Every finding links back to source text, selector, and measurement confidence.

For operators

See whether AI-facing answers can misstate your offer, pricing, or policy from conflicting page layers.

For practitioners

Audit crawler access, structured data integrity, fact parity, and entity identity with permanent fixtures.

Deep enough for experts

The instrument behind this page is the same harness used for crawler matrices, structured-data integrity, fact parity, entity graphs, and measurement-validity CI. UNKNOWN stays UNKNOWN.

Read the methodology →Open the instrument →

  • 13 public crawler identities
  • Raw HTML vs rendered DOM
  • JSON-LD parse validity ≠ schema truth
  • Atomic facts with assertion mode
  • Permanent regression fixtures