Canonical Integrity

Canonical Integrity measures how clearly an institution declares the “official” version of each page. AI systems rely heavily on canonical tags to determine which URL is authoritative, which duplicates should be ignored, and how the website’s structure fits together.

When canonicals are missing, inconsistent, or conflicting, AI systems fragment the entity graph and cannot reliably determine which version of a page to treat as primary. This reduces interpretive trust, creates “ghost surfaces,” and weakens overall visibility across GPT, Perplexity, Copilot, Gemini, and ERNIE.

A clean canonical structure signals that your institution is deliberate, stable, and self-consistent — traits AI systems interpret as reliability.

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  • Declare one canonical per page, matching the exact live URL
  • Ensure canonicals are permanent, lowercase, and stable
  • Remove or fix inherited template canonicals
  • Prevent parameters (?ref=, ?source=, etc.) from becoming indexable duplicates
  • Audit staging or dev URLs and ensure they are never canonicalized
  • Use absolute URLs (e.g., https://exmxc.ai/...) rather than relative paths
  • Re-run a canonical audit whenever creating new templates or duplicating pages

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    Missing canonical tags on key pages

    Multiple pages declaring the same canonical URL

    Canonical pointing to outdated or incorrect URLs

    Pages canonicalizing to staging or development environments

    Pagination canonicalizing incorrectly

    Auto-generated templates inheriting wrong canonicals

    Mismatches between uppercase/lowercase versions of URLs

    HTTP/HTTPS or www/non-www conflicting canonicals

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