As the AIGC wave sweeps the globe, the way users obtain information is undergoing a fundamental transformation. Large language models (LLMs) represented by ChatGPT, Gemini, and Kimi are gradually replacing traditional search engines and becoming the main entrance for users to acquire knowledge and solve problems. Against this backdrop, the battlefield of brand marketing has shifted from traditional SEO (search engine optimization) to GEO (generative engine optimization).

JE Labs closely monitors industry trends and cutting-edge dynamics, continually researching emerging market sectors. Based on systematic analysis, we have prepared this report to provide guidance for this structural change.

1. Key Points

1.1 GEO is Digital Identity Authentication

The core of GEO is to establish brand identity rights in the future information ecosystem. Through systematic content feeding, brands evolve from mere search results to authoritative sources in AI cognition. In an AI-driven search environment, visibility depends on whether the AI system recognizes the brand as a trustworthy source.

This systematic content feeding not only involves publishing information but also ensuring that information appears across multiple trusted sources. AI models are inherently skeptical of single sources and require cross-validation; a fact must appear simultaneously on websites, news reports, and community discussions to be fully trusted and cited.

1.2 GEO is a Superstructure Built on SEO

GEO does not replace SEO but rather builds upon it at a higher level. A strong SEO foundation is critical for AI systems to adopt and reference information. SEO determines whether a brand can be found, while GEO determines whether AI chooses to cite it. If the SEO foundation is solid, half the battle for GEO has already been won.

Specifically, a solid SEO foundation includes not only a good data structure and high-quality backlinks but also semantically rich and clearly optimized content, ensuring that AI systems can easily interpret and integrate information into their knowledge graphs.

1.3 User Structure Determines Strategic Value

While important, brands should not blindly invest in GEO. Whether GEO is worth systematic investment largely depends on the "AI density" of the brand’s user base—i.e., the frequency with which users rely on AI in their decision-making processes. GEO can become a key growth lever that directly impacts conversion efficiency; however, for traditional audiences with low AI adoption rates, the return on investment for GEO needs to be assessed with caution.

2. How to Determine the Necessity of Doing GEO

2.1 Suitable Industries

Not all industries are equally suitable for large-scale GEO investment. Before investing in GEO, businesses should first assess a fundamental question: Has AI become a part of their users' decision-making process?

If target users increasingly rely on AI tools to understand product information, compare options, or seek advice, then the strategic value of GEO will significantly increase. On the other hand, if purchase decisions are still mainly driven by offline channels, social media influence, or brand loyalty, GEO may not yet be a top priority.

Based on user decision behaviors and information structures, industries can generally be divided into three categories:

Image source: JE Labs

This categorization aligns with observed AI search behaviors. Research from Semrush indicates that the most common AI search queries fall into three categories: explanatory queries, comparative queries, and decision-support queries. These query types are concentrated in industries with a large amount of information and high complexity.

2.2 ROI Consideration

First, the initial investment in GEO is typically higher, requiring companies to develop high-quality, knowledge-based content, construct a structured data framework, and design an information architecture that is easy for AI systems to understand and reference. According to Brightedge Media data, this usually exceeds traditional SEO by 15-25%. However, this higher upfront cost often brings higher quality traffic and stronger conversion potential. AI-generated answers come with inherent "trust signals." Users often regard AI recommendations as expert guidance, which means traffic driven by AI recommendations typically has stronger intent and higher conversion rates than traditional search traffic.

Second, GEO offers significant long-term value. When a brand's content is frequently cited by large language models, AI search engines, or RAG systems, the brand can gradually establish itself as a trusted knowledge source within the AI ecosystem. Meanwhile, ignoring GEO carries implicit risks. As more users turn to AI interfaces for information, brands lacking presence in AI knowledge systems may face three major challenges:

• AI completely avoids mentioning the brand when answering related questions;
• AI may generate incorrect or incomplete information about the brand;
• AI may recommend competitors who have optimized GEO.

In short, the decision-making framework can be summarized as follows: if users are using AI to make decisions, brands need to appear in AI-generated answers. In this context, GEO is no longer just a marketing optimization tool; it has become a new level of brand infrastructure in the AI-driven information economy.

3. Decoding the GEO Mechanism

The core of GEO lies in understanding the "thinking style" and "preferences" of AI large models. Through systematic content feeding and channel layout, brand information becomes the preferred and authoritative source when AI generates answers. This marks a shift from traffic competition to identity verification.

Optimizing generative engines requires breaking the misconception of anthropomorphism: AI models do not "understand" things like humans do; they are based on vector mathematics and probability calculations.

3.1 Dual Memory Architecture

AI does not "remember" brands; it reconstructs them based on probabilities. AI models process information through two different paths:

• Long-term memory (pre-trained data): The "crystallized intelligence" the model acquires during training (e.g., Wikipedia, Books3). Influencing this requires a long-term "brand placement" strategy to ensure the brand becomes native content for future models (e.g., GPT-5).
• Short-term memory (RAG with real-time retrieval): The model's "fluid intelligence." When a user inquires about current rates or features, the AI performs real-time fetching. The goal is to achieve technical structuring to appear in the “top 10-20” retrieval windows.

3.2 Trust Pyramid

Generative engines prioritize the credibility of sources over popularity.

• First layer (truth layer): .gov, .edu, Wikipedia, Bloomberg. Data here is considered factual.
• Second layer (authority layer): Industry-specific media (e.g., CoinDesk), verified expert blogs.
• Third layer (noise layer): Regular corporate websites and social media.

AI models are skeptical of single sources. They need cross-validation—facts must appear simultaneously on websites, news reports, and community discussions (e.g., Reddit) to gain trust.

3.3 Preferred Content Structure

AI "reads" tokens rather than pages. To maximize citation rates:

• Use dense sentences that contain statistics and clear attribution (e.g., "According to data from 2025...").
• AI prefers lists, JSON-LD schemas, and comparison tables. Tables are the most effective way to force AI to recognize relationships between brands and competitors.
• Importantly, avoid keyword stuffing; research from Princeton University indicates that keyword stuffing can actually reduce citation rates by 10%.

4. Strategic Differentiation: China vs. the West

GEO strategies must be differentiated based on the target ecosystem.

4.1 Chinese Market: Authority and Officialness

• Core Concept: Ecological Binding
• Key Platforms: Baidu (Wenxin Yiyan), ByteDance (Doubao), Tencent (Hunyuan), etc.
• Strategy: Rely on "official" sources. Brands must have Baidu Baike entries and WeChat public accounts. Chinese models have higher "risk aversion" parameters; they prefer content that clearly indicates risks and emphasizes compliance.

4.2 Western Market: Consensus and Open Networks

• Core Concept: Relevance Engineering
• Key Platforms: Google (Gemini), Perplexity, ChatGPT, etc.
• Strategy: Rely on “collective intelligence.” High trust signals come from Wikipedia, Reddit discussions, YouTube comments, and tech blogs. The emphasis is on semantic proximity and mathematical relevance.

5. GEO Service Provider Map

The recommendation logic of LLMs is opaque, a "black box." Accordingly, a new ecosystem of GEO service providers has emerged. The global GEO market can be divided into three strategic paths: technological infrastructure providers, authority-driven content agencies, and growth-focused marketing companies.

5.1 Technological Infrastructure Providers

The first type views GEO primarily as a computational linguistics and information retrieval problem. The goal is to enhance how easily AI systems discover and interpret brand content. Their approach utilizes techniques such as vector embeddings, semantic similarity modeling, and RAG optimization to ensure that brand information is structured in a manner that is efficiently retrievable and citable by AI models. In China, platforms like GenOptima provide similar capabilities by monitoring and optimizing AI visibility across multiple models.

5.2 Authority-Driven Content Agencies

The second type focuses on trust signals and authoritative content. Agencies like First Page Sage believe that AI recommendations ultimately reflect a trust allocation mechanism. Their strategy emphasizes:

• Occupying spaces in authoritative databases and media
• Thought leadership content development
• Strengthening E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). By consistently appearing in trusted information sources, brands increase their likelihood of being cited by large language models. This model represents an evolution from the traditional SEO trust framework to the AI era, particularly suited for industries with high credibility requirements, such as finance, healthcare, and B2B services.

5.3 Growth-Focused Agencies

The third type approaches GEO from a performance marketing perspective. For example, NoGood integrates GEO into a broader growth strategy by tracking brand visibility, sentiment, and share of voice across multiple LLM platforms. These companies focus not only on citations but directly link GEO performance to revenue, lead generation, and user acquisition metrics. This approach redefines GEO as a new customer acquisition channel, rather than merely a visibility optimization technique.

5.4 Emerging Chinese GEO Market

The GEO service market in China exhibits two distinct directions. One category of providers emphasizes technology platforms and model compatibility, such as GenOptima, which focuses on multi-model monitoring and optimization; GNA, which concentrates on large-scale AI query simulations to test how different prompts and information structures influence AI responses. The other category integrates GEO with traditional marketing strategies, such as PureBlue, which combines AI visibility optimization with traditional brand promotion activities.

6. GEO Practical Guide

Step One: Competitive Analysis and Visibility Clarification

• Objective: Clarify the brand's initial visibility in AI large models and understand how AI describes and recommends competitors.
• Method:
• Simulate user inquiries: Simulate user questions on mainstream AI platforms and gather AI answers. Pay close attention to how brands and competitors are mentioned.
• Analyze brand visibility: Count the frequency at which the brand name and related concepts are mentioned by AI. Record the context and sentiment of these mentions.
• Analyze competitors: Record how AI describes and recommends competitors, extracting the advantages labels or unique selling points perceived by AI.

Step Two: Uncover High-Frequency AI Questions

• Objective: Identify the most common questions users ask AI, laying a foundation for precise customer acquisition.
• Method:
• Analyze user intent chains: Outline the complete question chain from user cognition to decision-making. Understand typical user journeys and the information needs at each stage.
• Check trends: Use tools like Google Trends, Semrush, or Ahrefs to search for trending industry keywords, grasping the trending patterns of related topics and questions. Identify emerging trends and evergreen queries.
• Gather questions: Utilize professional tools or manual research to extract "most asked questions in XX industry" from forums, Q&A platforms, and AI assistant logs, precisely targeting user needs.

Step Three: Content Creation: Creating AI "Preferred" Content

GEO does not directly modify model parameters. Instead, it establishes semantic connections between brands and core concepts by publishing a large volume of high-quality, structured content that aligns with large model preferences, thereby occupying AI's cognitive share.

Image source: JE Labs

Content Taboos: Avoid using exaggerated or inaccurate expressions, such as "the strongest XX platform," "guaranteed profits/high returns," or "radical speculative narratives."

Step Four: Multi-Platform Distribution: Leveraging High-Authority AI Channels

• Objective: Utilize platforms that are considered high authority by AI to allow faster and more frequent scraping of brand content by AI.
• Core Principle: All content should become a long-term learning source for the model, rather than a short-term marketing channel. By embedding consistent brand information in multiple high-authority sources, cross-validation is formed, compelling AI adoption.

🌟 Main Model Preference Analysis and Channel Placement Strategy

Image source: JE Labs

Step Five: Effect Monitoring and Maintenance (Long-Term)

• Objective: Validate effects and adjust content based on AI feedback.
• Method:
• Continuous monitoring: Closely monitor algorithm fluctuations of AI large models and changes in brand rankings in AI searches.
• Check indexing: Continuously check which content has been scraped and indexed by AI.
• Directly ask AI: Feed published articles to AI and directly inquire, "Can my article 'XX' serve as material for answering 'XX question'?" Analyze AI’s responses to understand its perception of content relevance and authority.
• Fill in gaps: Adjust content strategies based on AI feedback. For example, if AI rarely cites content about "costs," then specifically supplement a "cost comparison table for businesses of different sizes" and republish it, helping to drive the iterative process of continuous optimization.

7. Conclusion

The shift from SEO to GEO represents a transition from "renting visibility" to "owning authority." In the traditional search era, brands competed for rankings on results pages; in the generative AI era, brands compete for their positions in model cognition.

This means that GEO is no longer merely a marketing optimization tactic; it has become a new level of brand infrastructure in the AI-driven information economy, transforming content from purely human-reader-oriented marketing materials into essential training data for machines. The future of a brand lies not in being searched, but in being generated.