4 operational categories
- 01Scoring5
- 02Trust & Conversion5
- 03Technical SEO3
- 04AI Search3
A source-bound vocabulary for reading public customer paths without turning an observable signal into invented revenue, rankings, or proof.
Public pages only. The $97 report comes after the result.
4 operational categories
Words set scope.
A scan can preserve what a public surface exposes. Private outcomes stay outside the claim until their owning systems provide evidence.
Start with the business question, then inspect the definition, source, and limit before acting on the label.
A dominant leak is a documented sector hypothesis used to focus inspection on a likely point of friction. It is not a measured frequency claim or a diagnosis of an individual website before evidence is collected.
A revenue leak is an observed condition in a public customer path that may add friction before a booking, call, inquiry, or purchase. The label identifies a risk to investigate; it does not prove that revenue was lost.
A revenue-capture score is a normalized diagnostic summary of public-page observations that may affect a visitor's path to action. It is not a conversion rate, revenue estimate, or guarantee of recovered sales.
A rule taxonomy is the published registry of named checks used to interpret observable signals on a public page. Each Revvye rule carries an identifier, category, evidence pattern, severity range, and limitation.
A severity rating is Revvye's triage label for a finding: critical, high, medium, or low. It communicates fix order within the public evidence available, not predicted financial impact.
Booking friction is a visible or structural condition that adds ambiguity, effort, or interruption between interest and a public booking, call, or inquiry route. Revvye observes the path presented by the page; it does not submit forms or complete transactions during a public scan.
A conversion path is the sequence of public steps offered between a visitor's entry point and a business action such as a call, inquiry, booking, or checkout. Revvye maps observable steps and handoffs without claiming that a transaction was completed.
Follow-up readiness describes the public expectations and states presented after a visitor is asked to inquire, book, or request contact. A public scan can observe stated next steps; it cannot verify delivery or response performance without customer-owned records.
NAP consistency describes whether a business's name, address, and phone number agree wherever those details are represented. A public Revvye scan can compare visible page text with machine-readable declarations on the submitted surface, not every external directory by default.
A trust signal is a public-page element that helps a visitor evaluate credibility, identity, relevance, or risk before acting. Examples include clear business details, credentials, policies, provider context, and attributable customer evidence.
Crawlability is whether a public URL can be requested and meaningfully read under a stated set of crawler conditions. It is related to, but does not prove, indexing, ranking, citation, or recommendation.
Local visibility describes the public signals that help a person or machine understand where a business operates and which locations or service areas it serves. It is not the same as a verified map position or local-search ranking.
Structured data is machine-readable markup—often JSON-LD—that describes entities and relationships on a webpage using a shared vocabulary such as Schema.org. Revvye can observe detected markup and selected properties on the public page it receives.
AI crawler access describes what a site's public robots.txt, response headers, and page directives declare for known AI-related user agents. The declaration can permit or restrict access, but it does not prove that an agent visited, indexed, or cited the page.
Answer engine optimization is the practice of making public information easier for answer systems to access, interpret, attribute, and represent accurately. It improves the evidence surface but cannot guarantee inclusion in a particular answer.
Generative engine optimization is the practice of improving how public content and entity signals can be found, understood, and represented by generative systems. It is an optimization discipline, not a promise that a model will mention or recommend a business.
The methodology shows how named rules, evidence, severity, and limits move from a public observation into a report an operator can inspect.
Inspect the methodology →