Discover. Understand. Trust. Cite.
The audit follows the sequence a public retrieval system needs before it can use a website as evidence: reach the page, understand the entity, evaluate trust and extract a useful answer.
METHODOLOGY / SIGNAL 2026.1
A score you can inspect, not just accept.
AI SIGNAL° uses deterministic checks against public source HTML and machine-readable resources. Every recommendation is tied to an observed signal.
Readiness is not observed visibility.
The current scan does not run prompts inside ChatGPT, Gemini, Claude or Perplexity. It measures the public conditions that influence retrieval and citation readiness. Live platform testing belongs to the paid analysis layer.
0125%
AI access
Checks retrieval, user-fetch and training-control agents separately, plus machine guidance and discovery signals.
0220%
Content citability
Looks for self-contained answer passages, useful hierarchy, question coverage and measurable facts.
0315%
Technical indexability
Evaluates public fetchability, metadata, canonicals, language, sitemap health and page coverage.
0415%
Entity and trust
Checks ownership, authorship, legal and contact paths, external evidence, dates and entity connections.
0510%
Structured data
Parses JSON-LD types, primary entities, stable identity signals and answer-oriented markup.
0615%
Answer readiness
Measures whether page structure supports direct, extractable answers without claiming an actual citation.
Up to six public HTML pages plus robots.txt, sitemap.xml and llms.txt, with strict request, redirect, time, response-size and abuse limits.
Source HTML only. Content that appears exclusively after client-side JavaScript runs may not be fully represented in this release.
No audit can guarantee rankings, traffic or AI citations. The report identifies observable barriers, strengths and next actions.
READY TO SEE THE SIGNAL?