Beacon analyzes 8 AEO dimensions and 6 SEO dimensions in every full audit — returning specific, scored findings for each. Below is exactly what each mode examines and what it returns.
Beacon analyzes pages against both traditional Google ranking factors and AI answer engine citation signals simultaneously. No major incumbent SEO tool — Semrush, Ahrefs, Moz — has natively built AEO scoring into their core audit product. Beacon is built specifically for the reality that the content Google ranks and the content AI engines cite are often different, and that winning in 2025 requires optimizing for both.
The complete dual-channel analysis
The full audit is Beacon's flagship mode. It evaluates every significant SEO and AEO signal on your page and returns a comprehensive scored report. This is the starting point for any optimization workflow — it tells you exactly where you stand and what to fix first.
Current tag content, specific issues, character count assessment, and a recommended replacement optimized for the target keyword and click-through rate.
Detection of missing or weak meta descriptions, specific issues, and a keyword-containing replacement with a clear call to action under 155 characters.
Full H1–H3 hierarchy audit — semantic keyword signal assessment, navigation clarity, and specific restructuring recommendations.
Target keyword density, semantic coverage score, and the specific entities and LSI terms that should be added for topical authority.
Link graph observations and specific anchor-text recommendations to distribute authority and guide crawlers.
Robots.txt risks, sitemap status, canonical tag strategy, Open Graph tags, mobile responsiveness indicators, and duplicate content signals.
How directly your content leads with answers in the first sentence — the pattern AI engines use to extract and cite content.
How likely this page is to be cited by ChatGPT, Perplexity, Google AI Overviews, or Gemini — and the specific reasons why or why not.
Estimated count of discrete, quotable factual claims — the raw material LLMs extract and synthesize. Includes specific examples to add.
Main entity identification, supporting entities, and the missing entities that would establish clearer context for AI engine disambiguation.
The specific schema.org types recommended based on content: FAQPage, HowTo, Article, SoftwareApplication, Organization.
The related entities, concepts, and topical terms AI engines expect to find — with a list of missing ones to add.
Present and missing recency indicators — dates, version numbers, publication timestamps — that AI engines weight for content currency.
Topics that would increase citability, with search volume estimates and the reason each gap matters for AI engine authority.
When a URL is provided, Beacon automatically fetches live Google Lighthouse data via the PageSpeed Insights API — both mobile and desktop. Performance, Accessibility, Best Practices, and SEO scores are displayed alongside LCP (Largest Contentful Paint), FCP (First Contentful Paint), TBT (Total Blocking Time), and CLS (Cumulative Layout Shift). This runs in parallel with the AI analysis, adding no extra wait time.
Writer-ready briefs for both channels
Generates a complete brief for creating new content optimized for both Google and AI engines. Returns target keyword, secondary keywords, semantic entities to mention, the questions AI engines will answer for this topic (8–12), a full outline with H2s and key points, atomic facts to include per section, FAQ section structure, schema recommendations, and the differentiation angle that would make this content more citable than competitors.
Production-ready JSON-LD, no placeholders
Generates complete, valid schema.org JSON-LD markup ready to paste into a <script type="application/ld+json"> tag. Uses real values inferred from the content — not placeholder text. Covers the most appropriate schema type for the content, explains which rich results it enables, and provides implementation notes and a validation checklist.
8–12 question-answer pairs engineered for AI citation
Generates FAQ content built specifically to be retrieved and cited by AI answer engines. Each answer leads with the direct response in the first sentence (the pattern LLMs extract), includes specific facts, numbers, and named entities, and is 40–100 words — long enough to be substantive, short enough to be fully quoted. Also generates the FAQPage JSON-LD schema for direct implementation.
Rewrite existing content for maximum AI citability
Analyzes existing content for citation readiness, then rewrites it to maximize the likelihood of being selected by AI answer engines — while preserving the author's voice and staying within ±15% of the original word count. Converts vague claims into atomic facts, adds named entities and dates, restructures paragraphs for LLM extraction, and uses definitional sentences ("X is Y that does Z") that LLMs prefer to extract.
Who ranks on Google vs. who gets cited by AI — and why they differ
For a given target keyword and business context, analyzes who likely dominates Google SERPs versus who AI engines most likely cite — and the gap between them. This gap is often significant: the top-ranking page for a query is frequently not the page Perplexity or ChatGPT would cite in an answer. Understanding this gap is the foundation of a dual-channel content strategy.
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