Metric 1
Schema Markup
What we measure: Presence and quality of machine-readable structured data relevant to page intent.
Why it matters: Improves interpretability and signal confidence for automated extraction pipelines.
Seven Core Metrics
Think of these as warning surfaces. Each one points to a different family of fixable weaknesses.
Risk
Weak pages usually become visible too late.
Response
Find the blocker, fix it, then rescan with intent.
Control
Use the output to replace guesswork with a decision path.
These are not vanity subscores
Each metric is a warning surface for a different kind of page weakness. Teams use them to decide where the real risk is, what to fix first, and what should be rescanned after changes.
Metric 2
On-page Trust Signals
What we measure: Visible indicators such as author, organization, and contact trust context on the page.
Why it matters: Helps systems evaluate source context without relying on hidden assumptions.
Metric 3
Semantic Structure
What we measure: Heading hierarchy, semantic organization, and layout patterns used for content chunking.
Why it matters: Reduces extraction ambiguity and improves consistency across rescans.
Metric 4
Entity Recognition
What we measure: Clarity and consistency of key entities (organizations, people, products, places).
Why it matters: Strengthens machine mapping of core concepts to your brand and subject domain.
Metric 5
Content Clarity
What we measure: Readability and directness of statements used in machine extraction contexts.
Why it matters: Supports cleaner quote extraction and lowers interpretation drift.
Metric 6
Freshness Signals
What we measure: Detectable publish/update timestamps and related recency cues.
Why it matters: Enables systems to reason about content recency instead of guessing.
Metric 7
Crawler Accessibility
What we measure: Crawler accessibility and rendering compatibility across JS and no-JS views.
Why it matters: Prevents hidden content and rendering gaps from breaking diagnostics reliability.
Before/After markup example
This example shows the style of fix that improves extractability clarity. It is illustrative and does not reveal internal scoring methods.
Before
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "",
"datePublished": ""
}After
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "AI readiness implementation checklist",
"author": { "@type": "Person", "name": "Team Lead" },
"datePublished": "2026-04-01",
"dateModified": "2026-04-10"
}