The GEO Research Center
The GEO Research Center is GOSH’s central body of research on Generative Engine Optimization (GEO) and large language model (LLM) citation behavior.
It documents how generative AI systems interpret, retrieve, and present information about brands, businesses, and content, based on real prompts, real outputs, and real-world consequences across major AI models.
Research published in the GEO Research Center is observational, not predictive. Its purpose is to surface recurring patterns in AI visibility, citation behavior, and failure modes so organizations can adapt with clarity rather than assumption.
Research in the GEO Research Center is organized into active tracks representing distinct lines of inquiry.
Each track focuses on a specific aspect of generative AI behavior, allowing findings to be documented, compared, and expanded over time as patterns emerge across models and use cases.
WHAT IS GENERATIVE ENGINE OPTIMIZATION?
A reference overview defining Generative Engine Optimization (GEO) and its function in AI discovery and citation.
AI Visibility Studies
Research examining how and why brands appear — or fail to appear — in AI-generated responses.
GEO vs SEO
Gap Analysis
Analysis of where traditional search rankings diverge from AI answers and what causes the disconnect.
Model Behavior Observations
Documented observations of how large language models interpret prompts, entities, and sources.
Real-World AI Failures
Case studies where AI responses are inaccurate, misleading, or contextually incorrect.
Research Methodology
All research in the GEO Research Center follows a documented methodology covering prompt controls, model selection, and interpretation limits.
