From SEO to GEO: How Brands Can Occupy Large Model Mindshare in the AI Era

As the wave of AIGC sweeps across the globe, the way users acquire information is undergoing a fundamental transformation. LLMs represented by ChatGPT, Gemini, and Kimi are gradually replacing traditional search engines as the primary entry point for users to gain knowledge and solve problems. Against this backdrop, the battlefield of brand marketing has shifted, officially moving from traditional SEO (Search Engine Optimization) into the era of GEO (Generative Engine Optimization).

JE Labs keeps a close watch on industry trends and cutting-edge developments, continuously researching emerging market areas. Based on systematic analysis, we have produced this report to navigate this structural metamorphosis.

1. Key takeaways

1.1 GEO is Digital Identity Verification

GEO is about establishing brand identity rights within the future information ecosystem. Through systematic content feeding, brands evolve from simple search results into authoritative sources within AI cognition. In an AI-driven search environment, visibility is determined by whether AI systems recognize your brand as a trusted source.

This systematic content feeding involves not just publishing information, but ensuring its presence across multiple credible sources. AI models are inherently skeptical of single sources and require cross-validation; a fact must appear on your website, in a news report, and in a community discussion simultaneously to be fully trusted and cited.

1.2 GEO is the superstructure built on SEO

GEO does not replace SEO, it is an advanced layer built on top of it. A strong SEO foundation (structured data, high-authority citations, and credible content) is essential for AI systems to adopt and reference your information. SEO determines whether you can be found, and GEO determines whether AI chooses to cite you. If your SEO foundation is strong, you have already won half the GEO battle.

Specifically, a robust SEO foundation includes not only well-structured data and high-authority backlinks but also content that is semantically rich and optimized for clarity, ensuring AI systems can easily interpret and integrate your information into their knowledge graphs.

1.3 User structure determines the strategic value

Brands should not rush blindly into GEO investments. Whether GEO deserves systematic investment depends largely on the “AI density” of the brand’s users — how frequently users rely on AI in their decision-making process. GEO can become a critical growth lever directly impacting conversion efficiency. For more traditional audiences with lower AI adoption, however, the ROI of GEO requires more careful evaluation.

To illustrate, industries can generally be categorized based on user decision behavior and information structure, which directly impacts their suitability for GEO investment.

2. Determine if GEO is necessary

2.1 Industries suited for GEO Not all industries are equally suited for large-scale GEO investment. Before investing in GEO, companies should first evaluate a fundamental question: Is AI already part of their users’ decision-making process?

If target users increasingly rely on AI tools to research information, compare products, or seek recommendations, the strategic value of GEO rises significantly. On the other hand, if purchasing decisions are still primarily driven by offline channels, social media influence, or brand loyalty, GEO may not yet be a top priority.

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

This categorization aligns with observed AI search behavior. Research from @semrush shows that the most common AI search queries fall into three categories: explanatory queries, comparison queries and decision-support queries. These query types are concentrated in high-information and high-complexity industries.

2.2 ROI consider

From an ROI perspective, GEO differs from traditional SEO. First, the initial investment is often higher. GEO requires companies to develop high-quality knowledge-based content, build structured data frameworks, and design information architectures that AI systems can easily interpret and cite. According to @BrightedgeMedia, content investment for AI search optimization is typically 15–25% higher than traditional SEO.

However, this higher upfront cost often leads to higher-quality traffic and stronger conversion potential. AI-generated answers carry an inherent “trust signal.” Users frequently perceive AI recommendations as expert-level guidance, which means that traffic arriving through AI-driven recommendations often demonstrates stronger intent and higher conversion rates than conventional search traffic.

Second, GEO delivers significant long-term value. When a brand’s content becomes frequently referenced by large language models, AI search engines, or RAG systems, the brand can gradually establish itself as a trusted knowledge source within AI ecosystems.

At the same time, ignoring GEO carries hidden risks. As more users turn to AI interfaces for information, brands that lack presence in AI knowledge systems may face three major challenges:

• AI completely avoids mentioning the brand when answering related questions;

• AI may generate inaccurate or incomplete information about the brand;

• AI may recommend competitors that have optimized GEO.

In practical terms, the decision framework can be summarized simply: If your users are making decisions with AI, your brand needs to appear in AI-generated answers. In that context, GEO is no longer just a marketing optimization tactic, it becomes a new layer of brand infrastructure in the AI-driven information economy.

3. Decoding the AI Mind (GEO Mechanics)

The core of GEO lies in understanding the "mindset" 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 suggests a shift from traffic competition to identity validation.

To optimize for generative engines, one must dismantle the anthropomorphic fallacy: AI models do not "know" things in the human sense, they compute probabilities based on vector mathematics.

3.1 Dual-Memory Architecture

AI doesn’t remember brands. It reconstructs them probabilistically. AI models process information through two distinct pathways:

• Long-Term Memory (Pre-training Data): The model's "crystallized intelligence" acquired during training (e.g., Wikipedia, Books3). Influencing this requires a long-term "Brand Inception" strategy to ensure the brand is native to future models (e.g., GPT-5).

• Short-Term Memory (RAG & Real-Time Retrieval): The model's "fluid intelligence." When a user asks about current rates or features, the AI performs a real-time crawl. The goal is to be technically structured to appear in the "Top 10-20" retrieval window.

3.2 The Trust Pyramid & Consensus

• Generative engines prioritize Source Credibility over popularity. Tier 1 (The Truth Layer): .gov, .edu. Wikipedia, Bloomberg. Data here is treated as fact. Tier 2 (The Authority Layer): Industry-specific media (CoinDesk), validated expert blogs. Tier 3 (The Noise Layer): General corporate sites and social media.

• AI models are skeptical of single sources. They require Cross-Validation— fact must appear on your website, in a news report, and in a community discussion (e.g., Reddit) simultaneously to be trusted.

3.3 Preferred Content Structures

AI "reads" tokens, not pages. To maximize citation rates:

• Use dense sentences with statistics and explicit attribution (e.g., "According to 2025 data...").

• AI favors lists, JSON-LD schema, and Comparison Tables. Tables are the most effective way to force an AI to recognize the relationship between your company and its competitors.

• Crucially, avoid keyword stuffing; research from Princeton University (KDD 2024) indicates that keyword stuffing can actually decrease citation rates by 10%.

4. Strategic Divergence: East vs. West

A critical finding of JE Labs is that GEO strategies must be bifurcated based on the target ecosystem.

4.1 China Market: Authority & Officialism

• Core Philosophy: Ecosystem Binding.

• Key Platforms: Baidu (Ernie Bot), ByteDance (Doubao), Tencent (Hunyuan).

• Strategy: Reliance on "Official" sources. A brand must have a Baidu Baike entry and Official Account presence. Chinese models have a high "Risk Aversion" parameter; they favor content that explicitly warns of risks and highlights compliance.

4.2 Global Market: Consensus & The Open Web

• Core Philosophy: Relevance Engineering.

• Key Platforms: Google (Gemini), Perplexity, ChatGPT.

• Strategy: Reliance on "Collective Intelligence." High-trust signals come from Wikipedia, Reddit discussions, YouTube reviews, and technical blogs. The focus is on semantic proximity and mathematical relevance.

5. GEO Service Providers Mapping

The recommendation logic of LLM is opaque, creating a “black box”. In response, a new ecosystem of GEO service providers has emerged. The global GEO market can be divided into three strategic approaches: technical infrastructure providers, authority-driven content agencies, and growth-focused marketing firms.

5.1 Technical Infrastructure Providers

The first category treats GEO primarily as a computational linguistics and information retrieval problem. The goal is to improve how easily AI systems can discover and interpret branded content.

A representative example is @iPullRankAgency, which focuses on “Relevance Engineering.” Its approach leverages techniques such as vector embeddings, semantic similarity modeling, and RAG optimization to ensure that brand information is structured in ways that AI models can efficiently retrieve and cite. In China, platforms such as GenOptima provide similar capabilities through systems designed to monitor and optimize AI visibility across multiple models.

5.2 Authority-Driven Content Agencies

A second group focuses on trust signals and authoritative content. Agencies such as First Page Sage operate under the assumption that AI recommendations ultimately reflect a trust allocation mechanism. Their strategy emphasizes:

• Placement in authoritative databases and media

• Thought leadership content development

• Strengthening E-E-A-T (Experience, Expertise, Authority, Trust)

By consistently appearing in trusted information sources, brands increase their likelihood of being cited by large language models. This model represents an evolution of traditional SEO trust frameworks into the AI era, particularly relevant for industries where credibility is critical, such as finance, healthcare, and B2B services.

5.3 Growth-Focused Agencies

The third category approaches GEO from a performance marketing perspective.

For example, NoGood integrates GEO into broader growth strategies by tracking brand visibility, sentiment, and share of voice across multiple LLM platforms. Instead of focusing solely on citations, these firms link GEO performance directly to revenue, lead generation, and user acquisition metrics. This approach reframes GEO as a new acquisition channel, rather than merely a visibility optimization technique.

5.4 The Emerging Chinese GEO Market

China’s GEO service market shows two clear directions. Some providers emphasize technical platforms and model compatibility, such as GenOptima, which focuses on multi-model monitoring and optimization. GNA focuses on large-scale AI query simulations to test how different prompts and information structures influence AI responses.

Others combine GEO with traditional marketing strategies, such as PureBlue, integrating AI visibility optimization with traditional branding campaigns.

6. GEO Practical Guide

Step 1: Competitor Analysis & Visibility Clarification

• Objective: Clarify the brand's initial visibility in AI large models and understand how competitors are described and recommended by AI.

• Method: Simulate User Questions: Simulate user questions on mainstream AI platforms (e.g., ChatGPT, Gemini, Perplexity) and collect AI answers. Pay close attention to how your brand and competitors are mentioned. Analyze Brand Visibility: Count the frequency of brand names and related concepts being mentioned by AI. Note the context and sentiment of these mentions. Analyze Competitors: Record how competitors are described and recommended by AI, and extract their advantage tags or unique selling propositions as perceived by AI.

Step 2: Mining High-Frequency AI Questions

• Objective: Find the questions users most frequently ask AI to establish a foundation for precise customer acquisition.

• Method: Analyze User Intent Chain: Map out the complete chain of questions from user cognition to decision-making. Understand the typical user journey and information needs at each stage. Check Popularity: Use tools like Google Trends, Semrush, or Ahrefs to search for industry buzzwords and grasp the popularity trends of related topics and questions. Identify emerging trends and evergreen queries. Crawl Questions: Utilize specialized tools or manual research to crawl "most asked questions in the XX industry" from forums, Q&A platforms, and AI assistant logs to precisely lock onto user needs.

Step 3: Content Creation: Creating Content AI "Love"

GEO does not directly modify model parameters but builds semantic associations between brands and core concepts by publishing a large volume of high-quality, structured content that large models prefer, thereby occupying AI mindshare.

Content Taboos: Avoid using exaggerated or imprecise expressions such as "Best XX Platform," "Guaranteed Profit/High Yield," or "Aggressive Speculative Narratives."

Step 4: Multi-Platform Distribution: Leveraging High-Weight AI Channels

• Objective: Leverage high-weight platforms for AI to let AI crawl brand content faster and more frequently.

• Core Principle: All content needs to be long-term learning sources for models, not short-term marketing channels. By pre-embedding consistent brand information in multiple high-weight sources, cross-verification is formed, forcing AI adoption.

🌟 Mainstream Model Preference Analysis & Channel Placement Strategy

Step 5: Effect Monitoring & Maintenance (Long-term)

• Objective: Verify effects and adjust content based on AI feedback to make recommendations more precise.

• Method: Continuous Monitoring: Closely watch algorithm fluctuations of AI large models and changes in brand ranking in AI search. Check Inclusion: Continuously check which content has been crawled and indexed by AI. Ask AI Directly: Feed published articles to AI and ask directly: "Can my article 'XX' serve as material for you to answer 'XX question'?" Analyze the AI's response to understand its perception of your content's relevance and authority. Fill Gaps: Adjust content strategy based on AI feedback. For example, if AI rarely cites content about "fees," specifically supplement a "Fee Comparison Table for Enterprises of Different Scales" and republish. This iterative process ensures continuous optimization.

Conclusion

The shift from SEO to GEO represents, is 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, they compete for a position within the model’s cognitive map.

This means GEO is no longer just a marketing optimization tactic, but a new layer of brand infrastructure in the AI-driven information economy, transforming content from mere marketing material for human readers into essential training data for machines. Those who successfully translate their brand identity into structured, machine-understandable, and verifiable language will ultimately define the answers that the next generation of users receives.

The future of branding is not about being searched, but about being generated.

Disclaimer:

1. This article is reprinted from [JELabs2024]. All copyrights belong to the original author [JELabs2024]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.

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