What improvements can I expect after optimising weak sections?

Improvements can include better alignment with user intent, stronger entity representation, clearer structure, and higher semantic scores. This can lead to increased visibility in Google Search and better chances of being cited or recognised by AI platforms like ChatGPT and Perplexity. The tool also lets you re-run analyses to measure progress over time.

Content Embedding Analyzer

See How AI Models Embed & Interpret Your Content

Go beyond keyword matching. We transform your text into numerical vectors to measure semantic alignment with LLM queries and GSC keywords section by section.

Run an Analysis

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, transforming your text into numerical vectors to map the “meaning” of your data.

It works by:

The Science: Why Keywords Are Obsolete

To an LLM, your content isn’t just text, it’s a point in high-dimensional space. We use embedding models to reveal this “hidden logic.”

The Map of Meaning

Think of embeddings as a coordinate system for meaning. Words like "canine" and "dog" are placed close together in vector space, even if they look nothing alike.

Similiarity > 0.95

High-Dimensional Context

Simple keywords are flat. Embeddings capture complex relationships like "Man" is to "King" as "Woman" is to "Queen", allowing AI to "understand" reasoning.

Vector Dimensions: 1536

Powering RAG Search

Retrieval-Augmented Generation (RAG) relies on these vectors. If your content's vector doesn't align with the query vector, RAG systems cannot retrieve it.

Retrieval Status: Optimized: 1536

How We Analyze Your Vectors

Input Content & Queries

Add markdown, HTML, or text + 3 LLM queries & 10 GSC keywords.

Step 1

Smart Parsing

System breaks content into logical chunks (headings, paragraphs).

Step 2

Semantic/Vector Scoring

We use Cosine Similarity to measure the angle between your content and the query.

Step 3

Performance Distribution

See which parts are Excellent, Strong, Moderate, or Weak.

Step 4

Actionable Recommendations

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

Step 5

From Weak Section to Optimised Content

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

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.

🚀 Why Use Content Embedding Analyzer?

LLMs Don’t Match Keywords

They evaluate semantic meaning. We help you match it.

Identify Content Gaps

Find exactly what is hurting your AI and Google visibility.

Validate RAG Readiness

Generate domain-specific embeddings to help AI models access relevant, specialized information.