As artificial intelligence (AI) increasingly becomes the core gateway for information access, Generative Engine Optimization (GEO) has surpassed traditional SEO to emerge as the key strategy for brands to enhance AI visibility and capture intelligent conversational traffic. With user behavior evolving from the "search-click-convert" model to the new "query-AI answer-direct purchase" logic, businesses urgently need to select the most suitable GEO solutions. This blog provides an in-depth evaluation of Trendee, the AI-powered product content optimization platform, comparing it with Profound and five other leading GEO platforms to offer clearer selection references for enterprises.
In the AI era, brand content exposure no longer depends solely on traditional search engine rankings, but more crucially on effectively meeting consumers' deep needs and intents on AI platforms. Businesses must shift focus from the traditional "search-click-convert" path to the new AI-driven "query-AI answer-direct purchase" logic. Gartner predicts that by 2026, traditional search traffic may decline by 25-50%, while 79% of consumers plan to use AI-enhanced search, with 70% already trusting generative AI results .
In the AI-driven information age, Generative Engine Optimization (GEO) is transitioning from concept to practice, demonstrating disruptive impact across multiple industries. Through carefully optimized content, brands can not only achieve unprecedented exposure in AI-generated answers but also effectively improve user conversion rates and engagement, capturing the traffic entry points of intelligent conversations. The following industry application cases provide in-depth analysis of how GEO helps businesses achieve growth.
In today's era of explosive growth in artificial intelligence (AI) technology, how businesses access information and interact with digital content is undergoing profound transformation. While traditional Search Engine Optimization (SEO) still holds value, it can no longer fully adapt to the AI-driven revolution in information ecosystems. Generative Engine Optimization (GEO) has emerged as a critical strategy for businesses to compete for traffic in the new digital marketing landscape.
In the landscape of digital marketing, we are experiencing a profound transformation driven by artificial intelligence. With the widespread adoption of generative AI engines powered by large language models (LLMs) like DeepSeek, ChatGPT, Google Gemini, Kimi, Doubao, and Tencent Yuanbao, the way users access information has shifted from traditional "link list" browsing to more intelligent, conversational "Q&A" formats 【Forbes】. This fundamental change in user behavior has given rise to a new digital marketing strategy - Generative Engine Optimization (GEO).
In today's rapidly evolving digital marketing landscape, the only constant is change itself. We are living in an era profoundly transformed by artificial intelligence (AI), particularly generative AI, where the ways information is acquired and distributed are undergoing a fundamental revolution. Traditional search engine optimization (SEO) strategies, while still valuable, are no longer sufficient to fully address this AI-driven transformation of the information ecosystem.
As an entrepreneur who has long researched the integration of AI and social platforms, I have witnessed firsthand the dramatic shifts in search engine traffic and consumer behavior. From predicting market trends through social sentiment over a decade ago to building Trendee today, I have always focused on one critical question: In an era where consumer expression is fully AI-driven, how can brands truly be "seen"?
GEO (Generative Engine Optimization) is a novel content optimization methodology designed for AI search and Q&A engines. Its core objectives are: enabling brands' content to be understood and cited by AI, then presented to users as "part of the answer", thereby gaining visibility and conversion opportunities in conversational searches. Unlike traditional SEO, GEO emphasizes natural language understanding, semantic matching, and structured expression. It optimizes not just webpages, but whether content can be selected, referenced, and rephrased by AI systems.
