Content Embedding Analyzer

See where and how your content is embedded online

Content Embedding Analyzer

Go beyond keyword matching. Use AI-powered semantic analysis to check how well your content aligns with LLM queries and Google Search Console keywords — section by section.

Run an Analysis

Tagline Loop

Parse

Break content into logical sections using headings and structure.

Score

Calculate semantic similarity with LLM queries + GSC keywords.

Recommend

Auto-generate actionable content improvements.

Optimise

Fix weak areas and validate improvements with re-analysis.

What is Content Embedding Analyzer?

🔎 Semantic Content Analysis for LLM SEO

Search is moving from keywords to semantic relevance. The Content Embedding Analyzer uses AI embeddings to measure how well your content matches the meaning behind real queries — not just the words.

It works by:

Comparing your content against LLM-style queries.
Cross-referencing with Google Search Console keywords.
Pinpointing which sections of your page perform well, and which need improvement.

How It Works

Input Content & Queries

Add your content (markdown, HTML, or text), up to 3 LLM queries, and 10 GSC keywords.

Step 1

Smart Parsing

System automatically breaks content into sections via headings or chunking.

Step 2

Semantic Scoring

Each section is scored against queries + keywords using embeddings.

Step 3

Performance Distribution

See which parts of your content are Excellent, Strong, Moderate, or Weak.

Step 4

Actionable Recommendations

Auto-tasks suggest fixes: add missing entities, improve schema, or expand coverage.

Step 5

👉 Every section gets a score, so you know exactly what to fix.

🚀 Why Use Content Embedding Analyzer?

LLMs don’t match keywords — they evaluate semantic meaning.
Identify content gaps hurting AI and Google visibility.
Cross-check GSC data with AI queries to focus on what matters.
Prioritise fixes with section-level scores & recommendations.
Validate improvements with before/after tracking.

From Weak Section to Optimised Content

Example: A content section on “manual citation services” scored only 42% similarity with the query “affordable local citation services”.

The analyzer recommended:

Add entity mentions (pricing, packages).
Expand FAQ with cost-related queries.
Insert schema markup for services.

After optimisation → score improved to 85%, boosting both search relevance and AI citation potential.

See where and how your content is embedded online