AI Readiness Audit

How to analyze any URL for AI citation readiness, use keyword context to focus your scan, and interpret every section of your results.

What the Audit does

The AI Readiness Audit fetches any public URL, parses its HTML, and scores it against 28 research-backed signals that predict citation probability in AI answer engines like ChatGPT, Perplexity, Gemini, and Claude.

Critically, the Audit does not query an LLM to check whether you are cited. That would be expensive, unreliable, and non-deterministic. Instead, it analyzes the structural, semantic, and metadata signals on your page that AI models use to decide whether to cite a source.

Signal checks per scan

Weighted scoring pillars

15–30s

Average scan time

The "Target vs. Soldier" concept

Every URL you analyze is a soldier — a specific page fighting for citation in AI answers. But a soldier performs best when they know exactly which battle they are in. That battle is your target keyword.

When you supply a target keyword alongside a URL, the Audit shifts from a generic structural analysis into a focused, intent-aware analysis. The scoring engine weights signals differently based on the query type (informational vs. instructional vs. comparative), and the generated Fix Roadmap lists only the gaps that matter for that specific keyword's intent.

Without a target keyword

The Audit runs a comprehensive structural analysis and returns a general AI Readiness Score. Useful for a baseline health check of any page.

With a target keyword

The Audit analyzes the page through the lens of that query. It checks whether your content actually answers that specific question, whether your Schema covers the relevant entity types, and whether your H2 structure matches the query intent. The Fix Roadmap becomes laser-focused on closing the gaps that would cause an AI to pick a competitor over you for this exact keyword.

The best way to use keyword context

Use the Magic Wand (Optimize) button on the Entities & Topics page. It pre-fills both the URL and the keyword into the Analyzer in one click, creating a perfectly focused audit session without any copy-pasting.

Target Keyword context banner

When a target keyword is supplied, a persistent banner appears at the top of every section in your results report. The banner shows:

The target keyword you provided
The detected query intent (Informational, Instructional, Comparative, or Brand)
A reminder that all scores and recommendations are contextualized for this query

This banner persists as you scroll through the full report — Score, Platform Breakdown, Pillar Analysis, and Fix Roadmap — so you always have the optimization context in view. If you re-analyze the same URL without a keyword, the banner disappears and the analysis reverts to the general mode.

Keyword context is stored with each scan

The keyword used during a scan is saved to your Scan History. When you revisit a past result, the same keyword context is restored automatically.

Running an audit

Enter your URL

Navigate to the Dashboard or AI Readiness Audit page. Paste a full URL including the https:// prefix. The page must be publicly accessible — password-protected or login-gated pages cannot be fetched.

(Optional) Enter a target keyword

Type the specific keyword or question you want this page to rank for in AI answers. Example: "best project management software for remote teams" or "how to reduce customer churn". Leave blank for a general structural audit.

Click Analyze

The analysis begins immediately. A progress state tracks each stage: Fetching page → Parsing HTML → Running 28 signal checks → Generating Fix Roadmap.

Review your full report

The results page loads automatically. Scroll through your AI Readiness Score, platform-specific scores, pillar breakdown, and prioritized Fix Roadmap.

Analysis stages

The Audit processes your page through four sequential stages:

1. Fetching

RankAsAnswer uses Jina.ai to fetch the raw HTML of your page, including rendered JavaScript content where possible. This is a read-only operation — nothing is written to your site.

2. Parsing

The HTML is parsed to extract heading structure (H1–H6), Schema markup (JSON-LD), meta tags (title, description), word count, readability metrics, external links, list structures, and image alt texts.

3. Scoring

Each of the 28 signals is evaluated and scored. Scores are combined using the 4-pillar weighted model (Structure 30%, Metadata 25%, Content 25%, Citation Patterns 20%) to produce your overall AI Readiness Score and per-platform scores.

4. Generating Insights

The scoring gaps are ranked by citation lift impact to produce your Fix Roadmap. If a target keyword was supplied, the roadmap filters and re-prioritizes items based on that query's intent.

The 4 scoring pillars

Your AI Readiness Score is a weighted composite of four pillars. Each pillar addresses a different dimension of citation readiness:

Pillar	Weight	What it measures
Structure	30%	H1/H2 hierarchy, list usage (bullets, numbered steps), question-phrased headings, definition patterns
Metadata	25%	Title tag optimization, meta description intent-alignment, Open Graph tags, canonical URLs
Content	25%	Readability (Flesch-Kincaid), word count, content freshness (date signals), passive voice ratio, sentence length
Citation Patterns	20%	Presence of FAQ/HowTo/Article Schema, external citation links, author markup, ImageObject schema

Why no LLM queries during scoring?

Querying a live LLM to check citation status is unreliable — the same query produces different results each time, and it does not explain why you were or were not cited. Structural signal analysis is deterministic and actionable: it tells you exactly what to fix.

Fix Roadmap

The Fix Roadmap lists every gap identified during the analysis, sorted by impact. Each roadmap item shows:

Fix type — the specific schema, tag, or content change needed
Estimated citation lift — the projected improvement in citation probability for this signal
Priority level — Critical, High, Medium, or Low based on impact and effort
Generate button — click to generate the exact code or rewrite using Gemini (1 credit)

Generated fixes are stored in your scan history and can be revisited at any time without spending additional credits.

Export the roadmap

Use the Export button in the roadmap to download the full prioritized fix list as a Markdown file. This is useful for sharing with a developer or content team implementing the changes.

Scan history

Every audit you run is saved to your History page. From there you can:

Revisit the full results of any past scan
See how a page's score has changed over multiple scans
Access previously generated fixes without re-running the analysis
Compare scores across different pages

The History page is accessible from the sidebar under the main navigation. Scans are retained for 90 days on the Free tier and indefinitely on paid plans.

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