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GEO

GEO vs Traditional SEO: Why You Need Both

Why dual-axis scoring is the only approach that works across Google rankings and AI retrieval simultaneously.

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AI SEO

Entity SEO: The Foundation of AI Citability

LLMs ignore keyword-stuffed content and cite entity-rich passages. Here is how to structure yours.

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Content

Building a Content Workflow for Generative Engines

How to restructure your editorial pipeline without doubling workload or fragmenting tooling.

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Core concept

Understanding GEO (Generative Engine Optimization)

AI search engines prioritize citability, entity authority, and factual density over raw keyword presence. ChatGPT, Perplexity, and Claude construct answers from passages — not from ranked documents. To be cited, your content must be entity-dense, answer-first in structure, and verifiably factual. GEO is the practice of building these properties in at draft time.

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Core concept

The Dual-Axis Scoring Loop

Stop choosing between traditional Google ranking and AI visibility. The canonical scoring model evaluates content on both axes simultaneously: Content Score measures seven traditional SEO signals, and GEO Score measures five AI retrieval signals. Both scores share the same entity vocabulary, so a fix that improves one never contradicts the other. The Review Gate enforces minimum thresholds on both before any Article reaches the CMS.

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Core concept

Pillar-and-Cluster Architecture

Organize your content by building authoritative pillar pages supported by specific, long-tail cluster topics. Proper interlinking via Internal Link Suggestions prevents orphan pages, consolidates link equity where it matters most, and establishes topical authority across the Keyword Cluster. The scoring engine tracks internal links as a Content Score signal, surfacing weak cluster connections before they cost you rankings.

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