This article breaks down the key differences between SEO and AEO, why both matter for product discoverability, and how verified data gives brands a competitive edge in AI-driven commerce.
Generative Engine Optimization
Pranav Aggarwal et al., Princeton University (2024)
This paper introduces the concept of Generative Engine Optimization (GEO), a framework for improving how online content appears in AI-generated answers from models like ChatGPT and Gemini. Through large-scale experiments, the authors show that verified, well-structured information increases visibility by up to 40%, proving that credibility now drives discoverability in AI systems.
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Role-Augmented Intent-Driven Generative Search Engine Optimization (G-SEO)
Xiaolu Chen et al., Shanghai Jiao Tong University (2025)
Building on GEO, this research explores how AI models interpret user intent and role-based context when generating responses. The paper presents a system called G-SEO that adapts content for generative search by analyzing how large language models prioritize structured and trustworthy data.
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Beyond Keywords: Driving Generative Search Engine Optimization With Content-Centric Agents
Qiyuan Chen et al., Tsinghua University (2025)
Qiyuan Chen et al., Tsinghua University (2025)
This paper expands on the field of Generative Search Engine Optimization (GSEO) by introducing a multi-agent system that helps content creators adapt information for generative models. The authors present CC-GSEO-Bench, a large-scale benchmark for evaluating how content quality, structure, and credibility affect AI-generated visibility. Their findings reinforce that AI systems consistently favor verified, well-structured information.
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In this session, Kimberly Shenk, Co-founder and CEO at Novi, will bring a data science perspective to the rapid rise of AEO and its implications for brands.
Stop optimizing for static moments and start influencing contextual conversations.
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