This methodology explains what the audit checks, how scores are interpreted, and why lab data, field data, contextual page type, and unavailable checks are handled separately.
The audit combines automated technical checks for performance, SEO, accessibility, best practices, technical health, and AI-search readiness. It is designed to turn raw diagnostic data into a practical improvement snapshot for a public webpage.
Lighthouse laboratory results are controlled test runs. Field data comes from public real-user telemetry when available. Rohini keeps those sources separate because lab performance and real-user conditions are not the same thing.
A search tool, marketing site, ecommerce page, article, dashboard, or application should not all be judged against identical assumptions. The audit applies contextual page-type logic so non-applicable checks do not create artificial penalties.
When a third-party provider, anti-bot system, field dataset, or rendering condition prevents a reliable automated conclusion, the audit can mark a check as unavailable or manual review. Those states are intentionally separate from confirmed failures.
The audit does not guarantee Google rankings, AI citations, accessibility compliance, legal compliance, security certification, or business outcomes. It is a technical and informational diagnostic layer designed to support better decisions.