01
Modeling
Define the canonical entities, attributes, and relationships that describe a brand and its category.
Laboratory · 02
Knowledge Graph SEO Laboratory.
The Knowledge Graph SEO Laboratory is our research practice for entity-first information architecture: schema.org graphs, internal entity linking, disambiguation, and semantic relationships that let search engines and language models resolve a brand, product, or category as a connected concept rather than a string of keywords. It tests how knowledge graph depth and entity coverage influence retrieval, recommendation, and brand interpretation across Google, Bing, ChatGPT, and Perplexity.
Focus areas
What this laboratory investigates
Entity disambiguation and canonical entity modeling.
Topic and subtopic graph design for content hubs.
Schema graph layering across pages and sections.
Internal linking patterns that reinforce entity relationships.
SaaS feature, integration, and comparison architecture.
Enterprise knowledge structure design.
Methodology
How the laboratory operates
02
Instrumentation
Implement schema graphs, internal linking, and content structure that express the model.
03
Validation
Test whether AI systems and search engines correctly interpret the brand within its category and adjacent categories.
Active experiments
What we are currently testing
Entity coverage vs answer engine clarity
Measuring how completeness of entity coverage on a site influences answer engines' ability to describe the brand accurately.
Schema graph layering
Comparing the effect of layered schema across Organization, Service, Product, and FAQ types on retrieval.
Comparison page architecture
Studying which patterns of SaaS comparison content most reliably appear in AI category comparisons.
Applied to
SaaS CompaniesAI StartupsEnterprise SoftwareProfessional Services
FAQ
Frequently asked questions
Follow this laboratory's research.
Findings from this laboratory are published as the research programme develops.