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Methodologies

The 6 Pillars
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Methodology

The 6 Pillars of GEO

Each pillar represents a critical area where AI models form their "understanding" of a brand. Our methodology ensures comprehensive coverage across all dimensions.

Our Framework

Comprehensive AI Visibility Analysis

Each pillar addresses a specific dimension of how AI models perceive and represent your brand.

01

Clarity

We analyse site architecture, semantic HTML, and labelling. We ensure that a machine can distinguish between a product feature and a customer testimonial.

02

Consistency

We audit the brand's "Digital Footprint". If the company description on LinkedIn contradicts the website, the AI model loses confidence. We ensure brand data is uniform across all indexed platforms.

03

Trust

We look for "Authority Signals". This includes Schema.org markup for authorship, verifying award citations, and ensuring the brand is mentioned in high-authority datasets.

04

Visibility

We map where the brand appears in the wider web ecosystem. AI models rely heavily on 3rd-party contexts (e.g., "Best of" lists on news sites or Reddit discussions).

05

Freshness

Especially for RAG systems (like Perplexity), we ensure the site signals ongoing activity. We audit "Last Modified" headers, sitemap frequency, and news feed integration.

06

Technical Foundations

The "Under the Hood" audit. This includes robots.txt optimisation for AI crawlers, implementation of ai.txt (the emerging standard), and structured data (JSON-LD) specifically for LLMs.

In Detail

What We Analyse

A deeper look at the specific checks and audits within each pillar.

Clarity

Site Architecture & Semantic Structure

We analyse site architecture, semantic HTML, and labelling. We ensure that a machine can distinguish between a product feature and a customer testimonial.

Key Checks

  • Site architecture analysis
  • Semantic HTML structure
  • Content labelling and categorisation
  • Machine-readable content hierarchy

Consistency

Digital Footprint Audit

We audit the brand's "Digital Footprint". If the company description on LinkedIn contradicts the website, the AI model loses confidence. We ensure brand data is uniform across all indexed platforms.

Key Checks

  • Cross-platform brand data audit
  • NAP (Name, Address, Phone) consistency
  • Brand messaging alignment
  • Social profile synchronisation

Trust

Authority Signals

We look for "Authority Signals". This includes Schema.org markup for authorship, verifying award citations, and ensuring the brand is mentioned in high-authority datasets.

Key Checks

  • Schema.org authorship markup
  • Award and certification verification
  • High-authority dataset mentions
  • Expert attribution signals

Visibility

Web Ecosystem Mapping

We map where the brand appears in the wider web ecosystem. AI models rely heavily on 3rd-party contexts (e.g., "Best of" lists on news sites or Reddit discussions).

Key Checks

  • Third-party mention tracking
  • Industry publication presence
  • Community discussion analysis
  • "Best of" list inclusion

Freshness

Activity Signals

Especially for RAG systems (like Perplexity), we ensure the site signals ongoing activity. We audit "Last Modified" headers, sitemap frequency, and news feed integration.

Key Checks

  • Last Modified header audit
  • Sitemap update frequency
  • News feed integration
  • Content recency signals

Technical Foundations

Under the Hood

The "Under the Hood" audit. This includes robots.txt optimisation for AI crawlers, implementation of ai.txt (the emerging standard), and structured data (JSON-LD) specifically for LLMs.

Key Checks

  • robots.txt AI crawler optimisation
  • ai.txt implementation
  • JSON-LD structured data for LLMs
  • Technical SEO foundations

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Ready to Optimise Your AI Visibility?

Our assessments cover all 6 pillars to give you a complete picture of how AI models perceive your brand.