Thoughts and Practices on GEO Optimization Strategies for Large-Scale Multi-Category Official Websites and Top 500 Listed Company Websites

In recent years, GEO (Generative Engine Optimization) has been gaining increasing traction. A notable shift is that it's no longer just SMEs (Small and Medium-sized Enterprises) that are taking GEO seriously, but more and more listed companies, official websites of large multi-category group enterprises, and even Fortune 500 companies.

However, in practical communications, I've found that many large enterprises recognize "the importance of AI search" yet remain unsure about whether to implement GEO, where to start, and what tangible outcomes it can deliver. From the perspective of conventional ROI (Return on Investment) calculation, the growth in leads and enhanced brand exposure brought by GEO are indeed not immediately noticeable for large-scale enterprises in the short term; meanwhile, unavoidable compliance and brand risk issues have also caused many projects to be delayed indefinitely.

For large enterprises, GEO is not a question of "whether it's worth it", but of "how to perceive this initiative".

I. What Can GEO Bring to Listed Companies?

If viewed solely from the "customer acquisition" angle, GEO is clearly not appealing enough for listed companies. But when framed within the logic of information distribution in the AI era, it addresses far more fundamental issues.

• Correction and Positioning of Brand Information

In reality, numerous SMEs leverage big brands to boost their credibility when creating GEO content for customer acquisition. By associating themselves with well-known brands, these SMEs aim to elevate their market standing—but such content often disregards information accuracy. Product relationships, technology ownership, and collaboration backgrounds are arbitrarily spliced, and even obvious errors are common.

After being repeatedly cited by AI, this content leads to the so-called "AI hallucination". Ultimately, when users ask large models about a big enterprise, the brand introduction they receive is inherently inaccurate.

The primary value of GEO here is not exposure, but correction. Through the official website—a credible information source—enterprises can continuously provide stable and accurate information to AI, giving models a reliable anchor for "understanding who you are".

The core value of official website GEO is to shift from "others defining who you are" to "you defining who you are".

• Systematic Correction of Multi-Product and Multi-Series Information

Unlike SMEs, large enterprises typically have highly complex product lines. A single category may include multiple series and models with similar parameters and highly overlapping application scenarios. If the official website fails to clearly distinguish these in its structure and expression, AI will easily merge them ambiguously, resulting in inaccurate recommendations or comparisons.

GEO here does not require "creating more content", but rather uses clearer product hierarchies, model differentiations, and information organization methods to enable AI to accurately identify differences between products.

If you don't clarify your products, AI will "guess" for you.

• Correction of Contact Information and Conversion Paths

This is a common yet often overlooked issue. In many cases, AI can accurately describe a brand and its product capabilities, but when users follow up with questions like "Where is the official website?", "How to contact you?", or "How to consult?", the information suddenly cuts off.

For users, this creates an experience gap; for enterprises, it results in dual losses of trust and conversions. A key goal of official website GEO is to form a continuous, traceable closed loop between brand, products, and contact information on the AI side.

Being recommended by AI but unfindable by users is equivalent to a wasted recommendation—correction is therefore essential.

• Lead Acquisition and Risk Hedging

Undeniably, GEO can generate high-quality inquiry leads—even 1000%+ or 2000% growth for many SMEs—which is one reason it has surged in popularity in recent years. For listed companies, however, this is more of a secondary benefit than a core driver.

More importantly is risk hedging. If an enterprise does not proactively optimize for GEO, AI will evaluate it based on fragmented, outdated, or even negative information; meanwhile, competitors may gain advantageous positions in AI comparisons and recommendations through systematic content layout. For listed companies, this is no longer just a marketing issue, but one of brand perception and public trust.

Not doing GEO does not mean "no risk"—it means surrendering the right to interpretation to AI.

II. Core Goal of Official Website GEO: Becoming a Trusted Data Source for AI

The GEO discussed in this article is not about generic traffic, but focuses on the official website as a core position—enabling it to become a sustainable, trusted data source for AI, rather than relying solely on press releases, encyclopedia entries, or third-party content.

In practice, the first step is often not revising content, but conducting an in-depth diagnosis of the official website. I have long held the view that a website with healthy SEO (Search Engine Optimization) fundamentals is likely well-suited for GEO. Especially for listed companies and large multi-category official websites, product copy is typically written by product departments based on real business needs, inherently possessing high originality and professionalism.

This is why many SEO practitioners feel "restricted" when first working on large enterprise projects, complaining about limited modification scope and struggling to meet KPIs (Key Performance Indicators)—yet find that results are surprisingly stable after minor structural adjustments.

While large enterprise official websites have few modifiable elements, their original content makes GEO easier to implement than for SMEs—it is by no means a "mystery".

III. Rational Judgement on llms.txt and Schema.org

Regarding llms.txt, it’s important to clarify: it is not a replacement for robots.txt, but an exploratory declaration mechanism to indicate whether website content may be used by AI.

Currently, llms.txt has neither legal enforceability nor hard technical constraints. The absence of obvious crawl records in logs does not confirm its effectiveness or ineffectiveness—this is the root of the controversy surrounding it.

From an enterprise perspective, however, llms.txt at the very least clarifies the stance on data authorization, reduces potential copyright and ethical risks, and positions the official website clearly amid unclear industry regulations.

llms.txt is more of a "statement of attitude" than an "effect switch".

As for Schema.org structured data: it does not determine whether AI crawls a page, but helps machines accurately understand content structure after crawling. In the Chinese market, it is not a mandatory requirement (as it is nearly useless for most official websites), but still holds value in reducing comprehension costs for complex multi-product/multi-series scenarios.

IV. Content Enrichment, Product Naming, and Aggregation Pages

On the content front, GEO does not equate to "padding". AI prefers pages with complete structure, sufficient information, and a format close to "user manuals". Therefore, product pages need to evolve from simple display to knowledge-rich explanatory pages—supplemented with parameters, scenarios, cases, and FAQs (Frequently Asked Questions) to make them easier for AI to understand and cite.

Meanwhile, product naming is critical. If AI can only cite product capabilities but cannot stably associate them with the brand, the website content is essentially being "used for free" by AI. Setting clear, unique, distinguishable names for each product—so users immediately recognize your brand upon seeing the product name—is a foundational step in official website GEO.

Building on this, comparison pages for same-series products, model aggregation pages, and application scenario aggregation pages can amplify overall brand exposure in AI recommendations without involving competitor comparisons.

For example, when the top three products recommended by AI all come from your brand, brand awareness naturally forms.

At this point, it’s clear that the core of GEO lies not in techniques, but in truly understanding the operational nature of AI search. This is why I have been conducting GEO training—frankly, I made some money from early research and training in 2024 and the first half of 2025.

However, as technology has become transparent, GEO training is no longer profitable due to reduced fees. It is now more of a long-term personal interest and methodology output—to demystify the underlying logic of AI search, suitable for individuals seeking understanding and transformation, as well as job seekers, entrepreneurs, and enterprises looking to implement GEO independently. Just today, I completed training for an enterprise in Beijing, marking my 131st trainee. I also continue to provide GEO optimization and GEO consulting/escort services, helping enterprises clarify strategic direction and implement actionable paths while ensuring compliance.

Success in GEO often hinges on whether you truly understand AI search.