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What the platform does

v1 ships three intelligence modules on one shared substrate: one crawler, one normalized data layer, one evidence engine, one rule engine, and one findings model. Deterministic analysis covers 100% of crawled pages; AI is used only to explain findings and draft fixes.

E-E-A-T Signal Assessment

Audit observable experience, expertise, authoritativeness, and trust signals consistently across every crawled page.

  • Authorship: author attribution, author pages, credentials, and JSON-LD author properties.
  • Organisational trust: About, contact, editorial, and policy pages; Organization schema.
  • Sourcing: outbound citations for factual claims, broken references, self-referential linking.
  • Maintenance & disclosure: publication and modification dates, affiliate and sponsorship disclosure.

Boundary: An Observable Signal Assessment — it reports what is present and absent on the page, and never claims to compute Google’s internal quality assessment.

FAQ & Schema Intelligence

Detect, validate, and remediate structured data site-wide, and generate JSON-LD constrained to values your page supports.

  • Site-wide detection of JSON-LD, Microdata, and RDFa, with parse status recorded as evidence.
  • Validation against a versioned schema.org profile — required and recommended properties.
  • Template grouping: one defect across 8,000 pages becomes one prioritised item, not 8,000.
  • JSON-LD generation that fills only sourceable values and marks the rest INCOMPLETE — never invented.

Boundary: Ratings, prices, review counts, authors, and dates are never generated. Unsourceable properties are emitted as explicit placeholders.

AI Search Readiness

Assess observable content characteristics that relate to usefulness as source material for AI-mediated search.

  • Answer directness, heading structure, enumerable-content formatting, and sentence complexity.
  • Deterministic rules run on 100% of pages; bounded deep analysis runs on a plan-sized page set.
  • Every element cites a page excerpt or states an absence; excerpts are verified verbatim.
  • Coverage is disclosed — you always see how many pages received deep analysis.

Boundary: An Assessment, never a prediction. It reports no probability, likelihood, or percentage chance of appearing in any AI-mediated result.

AI Content Decay Detector

Identify content that is becoming stale on observable evidence — not because it is simply old.

  • Multiple signals: outdated years in titles, unmaintained articles, stale “last updated” dates, dead references.
  • Content-hash drift across scans (history) surfaces pages that never change while comparable pages do.
  • Every finding carries its evidence, severity, and confidence — and an explicit evaluation date.
  • Age alone is never a finding; undated or evergreen pages are excluded with the reason shown.

Boundary: It will not claim a statistic is wrong unless verification supports it, nor claim a traffic drop it has not measured.

AI Internal Linking Engine

Find meaningful internal-linking opportunities using the pages that actually exist in your crawl.

  • Orphan pages, excessive depth, broken internal targets, and non-descriptive anchor text.
  • Topically related page pairs with no link between them (model-assisted similarity).
  • The link graph, anchor text, and depth are captured from day one.
  • Suggested anchors are labelled as suggestions; the graph facts are deterministic.

Boundary: Never invents a URL — every recommended target is a page verified in your crawl data.

One prioritised list, not three data dumps

Findings from all three modules share one model, one evidence format, and one deterministic priority ordering — so you work a single ranked action list.

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