Site entity tag = with Organization The main Schema JSON-LD code is placed on the front page of the website and declares to the AI/search engine that your company is a unique and independent "entity", define who, where, what, and official identity this foreign trade factory is, and establish a unified brand identity for the entire network.

The format is generally JSON-LD, embedded in the web page<head>,It will not be displayed on the front-end page, but will be specially read by Gemini, Perplexity, and Google Knowledge Graph..

Distinctions:

• llms.txt: AI readableSite Content Catalog(Text list)
• Organization Schema:Brand ID card, define the business entity itself
• Product Schema: Product page tags describe the combination of a single product entity and are the two basic technical components of GEO.

What core fields are included in the site entity mark (foreign trade B2B focus)

<script type="application/ld+json">
{
"@context": "[https://schema.org](https://schema.org)",
"@type": "Organization",
"@id": "shturl.cc/GcmouK72EUpthJ8XndqIuqjIGc",
"name": "ABC Machinery Co., Ltd",
"legalName": "ABC Machinery (Xiamen) Co., Ltd",
"url": "shturl.cc/ATIZJZduXv09",
"logo": "shturl.cc/ATIZJZduXv09/logo.png",
"description": "China manufacturer of industrial pumps, OEM & ODM, ISO9001, CE certified, export worldwide",
"foundingDate": "2010",
"address": {"@type":"PostalAddress","addressCountry":"CN","addressLocality":"Xiamen"},
"telephone": "+86-xxx",
"sameAs": [
    "[https://linkedin.com/company/xxx](https://linkedin.com/company/xxx)",
    "[https://youtube.com/@xxx](https://youtube.com/@xxx)"
],
"areaServed": ["Europe","Southeast Asia","USA"],
"knowsAbout": ["industrial pump","water treatment equipment"]
}
</script>

Key field descriptions:

1. @id (Entity Unique ID): The most important! The entire station has a unified fixed ID, and the AI identification section represents your company.Don't change it casually, used to associate all site products, cases, and articles back to this corporate entity.
2. sameAs: Point to LinkedIn, Youtube, overseas social media, and overseas B2B platform homepages, and tell AI: These external materials,All from the same company, used to eliminate confusion among brand names (entity disambiguation, which is most needed by foreign trade factories).
3. knowsAbout: Tell AI that the product/industry the company is good at directly establishes the connection between entities and business themes.
4. legalName: The full legal company name in English to avoid confusion caused by inconsistency between brand name and registered name.

🚀Core values for GEO (Generative Engine Optimization)

GeographyThe goal: When overseas buyers ask AI to find suppliers, the AI can accurately recognize your brand and include you in the AI answer. Site Entity Tag ResolutionEntity recognition and disambiguationThis isGEO's most basic capabilities:

1. Resolve entity ambiguity (the biggest pain point in foreign trade) Many factories have the same English names, which is easy to confuse by AI. The production capacity/certificate of factory A is installed in factory B. The entity tag gives you a unique identity that AI can distinguish:This entity = your factory, not others.
2. Establish a basic brand identity file for AI and improve EEAT credibility When AI evaluates whether a supplier is trustworthy, it will grab physical information and compare official website, social media, and overseas media information. Entity tags use the official website asFirst-party authoritative sourceStrengthen the physical evidence of "professional and trusted manufacturers" and increase the probability that AI will accept your content.
3. Connect all entities at the station to build a simple knowledge map The home page Organization entity is the root entity, and this can be referenced on the product page, case page, and FAQ page.@id。AI understands relationships:These product/project cases all belong to this China factory, rather than a bunch of isolated pages.
4. Help Google Knowledge Panel and SGE/AI Overview identify your brand entities It is not directly guaranteed to produce a knowledge panel, butIt is a necessary basic condition for entering the knowledge map candidate pool, it is easier to call your brand information when retrieving answers in Gemini and Perplexity.

⚠️外贸落地注意事项

1. The information must be exactly consistent with the body of the web page, cannot write false establishment time or false certification, otherwise EEAT will be directly destroyed and AI will refuse to accept it.
2. The entire station only uses the same fixed @id, the home page and About Us page reuse the same entity ID. Do not generate different IDs for each page.
3. It's not just done in one go: after the company's address, main category, and official website are renamed, the entity tags must be updated simultaneously,Ensure that information on all external platforms across the network remains consistent(Name, address, brand).
4. Entity tags ≠ direct KPI: It will not directly bring inquiries, but will solve the problem of "can AI recognize who you are"; if you want to be quoted by AI, you also need llms.txt, product Schema, real cases, and external authority endorsements.

Summary in one sentence

llms.txt = For AI to seesite directory; Site entity tag = company ID card shown to AI, tell the big model who you are and what your only identity is, solve brand confusion, and build the physical foundation of GEO.

What is llms.txt

llms.txt is put on the websiteroot directory(https://你的域名/llms.txt). Plain text files in Markdown format, proposed by the communityIndustry practice specifications (non-W3C/Google official mandatory standards), specially for large model crawlers (ChatGPT, Gemini, Perplexity Search Agent, etc.)Website simplified manual + content index.

There are also optional packages llms-full.txt: Package the core page body directly into a single file to make it easier for AI to read all content at once. Foreign trade B2B generally gives priority to deploying llms.txt, and only large knowledge bases require a full version.

Distinguish three documents in one sentence:

• robots.txt: Tell the crawlerDon't grab anything
• sitemap.xml: Give it to Google search engine and listall URLs
• llms.txt: Tell AI big modelWhich pages to read first and what does the pages say?Remove noise interference such as navigation bars, pop-ups, and advertisements on the web page

Core help for GEO (Generative Engine Optimization)

GEO's goals:Let overseas buyers ask AI to find suppliers, the AI can understand and accept your official website information, and put your brand in the AI answer.。 llms.txt is a basic component of the GEO technology layer, andSchema structured markupParallel.

1. Reduce the noise of AI reading web pages and reduce understanding costs Ordinary independent station HTML mixed navigation, sidebar, pop-up windows, JS code, AI extraction of product, factory, and qualification information is prone to errors, resulting inAI hallucination(Putting competitive product information on you and incorrectly describing your production capacity/MOQ/certification). llms.txt directly provides a clean text summary and guides AI to read it directlyProduct page, factory introduction, case, certificate, FAQOn these B2B high-value pages, skip irrelevant content.

2. Proactively declare your brand entity to AI (the most critical for foreign trade B2B) The company profile should be clearly written at the beginning of the document: main products, factory location, market, OEM/ODM capabilities, and core certification. It's equivalent to giving a large modelOfficial statement of the first party, help AI build your brand entity, solve brand disambiguation (confusion of duplicate factories) issues, and improve EEAT credibility.

3. Control the priority of AI grabbing, and key content is retrieved first Don't write all pages in the station, just select the core pages, and you can also set themOptionalPartition: Secondary content can be skipped when there is not enough AI context. Foreign trade independent stations are generally displayed: home page, product classification, factory introduction, customer case, certificate, procurement FAQ, contact page. When AI searches RAG, it gives priority to reading the high-value pages you mark to increase the probability of being cited.

4. Improve the accuracy of AI citations and reduce error information Pain points encountered by many foreign trade factories: When AI introduces your company, parameters and production capacity are incorrectly written. llms.txt standardizes key business descriptions, and AI references have a greater probability of using original information on the official website, rather than fragmented second-hand information on the entire network.

   Note:llms.txt itself is not a direct ranking weighting signal, will not let AI quote you out of thin air; what it solves isCan AI accurately read your information?; Whether it can be accepted or not still depends on the true content of the page, cases, qualifications, and endorsement of external authority (EEAT).

5. Cooperate with Schema to form an iron triangle of GEO technology GEO technology infrastructureCombination:llms.txt + JSON-LD(Schema) + Clean and structured page content

• Schema: Within a single page, tell AI what this page is (product, company, case)
• llms.txt: The whole station dimension tells AI the core content map of the entire website

Foreign trade B2B independent station llms.txt simple template

# ABC Machinery Co., Ltd
> China manufacturer of industrial pumps, OEM & ODM available, ISO9001, CE certified, export to Europe, Southeast Asia.

## Core Products
- [Industrial Centrifugal Pump]([https://xxx.com/centrifugal-pump/](https://xxx.com/centrifugal-pump/)): Heavy duty pump for water treatment, custom material available
- [Self-priming Pump]([https://xxx.com/self-priming-pump/](https://xxx.com/self-priming-pump/)): Low noise, large flow rate, MOQ 1 set

## Factory & Trust Content
- [About Us]([https://xxx.com/about-us/](https://xxx.com/about-us/)): Factory scale, production line, QC system
- [Case Studies]([https://xxx.com/cases/](https://xxx.com/cases/)): Overseas project references & site photos
- [Certificates]([https://xxx.com/certificates/](https://xxx.com/certificates/)): CE, ISO, test reports

## FAQ for Buyers
- [Buyer FAQ]([https://xxx.com/faq/](https://xxx.com/faq/)): MOQ, lead time, payment terms, shipping

## Optional
- [Blog Articles]([https://xxx.com/blog/](https://xxx.com/blog/)): Industry technical guides

ˇForeign trade is implemented to avoid pits

1. Don't pile up a lot of invalid URLs, only retain 10 - 15 core pages; putting 404 invalid links will damage AI trust
2. Requires regular updates: Change llms.txt synchronously after new products, new certificates, and cases are launched
3. llms.txt alone cannot do GEO:Must be matched with authentic and trustworthy content, schemas, and external authoritative sourcesSimply putting this file has almost no effect
4. Google officially made it clear:llms.txt is not a ranking factor for Google Search/AI Overview; It is more targeted at Perplexity, ChatGPT Agents, and various AI retrieval tools

in a word

llms.txt = For AI to seeForeign Trade Factory Information Catalog, belongs to GEO's underlying technical infrastructure. The role is not to directly get rankings, butReduce the difficulty for AI to understand your official website, reduce AI illusions, and improve the accuracy of AI reading core products and factory qualifications, increase the chance of being quoted by overseas procurement AI Q & A.

JSON-LD structured dataisGenerative AI Engine Optimization (GEO)The core infrastructure and "first principles" of the company, which directly determine the brand's ability to respond to AI-generated answers by transforming web content into a standardized format that is machine-readable.visibilityWithcitation probability.

1. What is JSON-LD structured data

JSON-LDJavaScript Object Notation for Linked Data is a method based onJSON syntaxis standardized by W3C. The core goal is to map the RDF data model to JSON syntax, so that structured data not only maintains the ease of use of JSON, but also has the machine readability and interoperability of the Semantic Web.

Its core structure contains several key elements:

• @context: Defines the mapping relationship between terms and IRI, provides semantic context, and is a required field
• @type: Specify the entity type and associate concepts in the vocabulary. Highly recommended
• @id: Provide a globally unique identifier for entities to establish reference relationships between entities

comparedMicrodataandRDFaother structured data formats,JSON-LDis currentlyGoogle,MicrosoftThe most recommended format for mainstream platforms, because it uses<script>Tags are embedded in the page, do not affect the visual presentation of the page, decoupled from HTML, low deployment and maintenance costs, and all mainstreamsearch engineandAI search platformAll support it.

2. Relationship between JSON-LD and GEO

Geography(Generative Engine Optimization,Generative engine optimization) is a technology system for content creators that aims to improve the probability that content will be recognized, understood and cited by generative AI through structured, semantic and authoritative means. inGeographyAmong the technical architecture ofJSON-LD structured dataBeing at the bottom is the cornerstone of the entire system.

The relationship between the two can be understood from the following dimensions:

1. Underlying infrastructure relationships GEO adopts a three-layer decoupling architecture, the first layer isstructured data(Schema Markup + JSON-LD), the second level is vectorization +RAG adaptation, and the third level is cross-model reference monitoring. JSON-LD is GEO's "first principles" because the AI modeltraining corpusandRAG searchThe parsing ability of unstructured HTML is far less than the extraction accuracy of structured JSON-LD. Official Google data shows thatPages using Schema Markup have increased their citation rate in AI Overviews by more than 40%.

2. weight-to-proportion relationship According to an empirical research report released by Princeton University in 2024, JSON-LD's optimization weight accounts for 32.7% in GEO's comprehensive scoring system. This weight can be further broken down into:

• Entity Anchoring: approximately 15% of the overall GEO score, passed@typeand@idAttributes help AI instantly lock the domain and identity of the content
• Semantic Association Acceleration: Contribute approximately 10% of the weight, throughsameAsOther attributes actively declare associations with external authoritative entities
• Machine Proof Weight: Make up the remaining 7.7%, structured expressions are regarded as official statements of content, and have higher priority index weights when AI generates answers

3. Functional support relationship JSON-LD provides GEO with three core capabilities:

• deterministic analysis: Structured key-value pairs prevent LLM from being ambiguous due to HTML tag nesting, transforming the fuzziness of natural language into machine-computable certainty
• semantic annotation: Adopted@type+sameAsHelp models establish entity associations and build a semantic entity network
• Cross-platform common: Mainstream AI models such as Google, Bing, Apple, Meta, DeepSeek, and Doubao all support itSchema.org Standards

4. Effect improvement relationship Industry evaluation data shows that the citation rate of web pages deployed with perfect structured data in AI search is about 200%-350% higher than that of web pages without structured optimization, and the optimization cases of head organization can reach more than 400%. A controlled experiment conducted by the Mi-Chain Data Institute also showed that companies that additionally deployed JSON-LD structured data were 4.2 times more likely to answer relevant questions on DeepSeek than the control group.

III. JSON-LD template types commonly used in GEO

In GEO practice, different types of pages require deployment of different JSON-LD templates:

4. Practical suggestions

1. Priority deployment of FAQPage: Directly aligning user long-tail inquiries is the most cost-effective starting point
2. Ensure consistency with the body: Structured data must be consistent with the visible body, and content that is not available in the body must not be written, otherwise it will be judged as misleading
3. usesameAsEstablish cross-platform connections: Associate the official website with authoritative sources such as Zhihu and GitHub. When AI sees multiple sources pointing to the same entity, it will greatly increase confidence
4. Post-deployment verification: Use tools such as Google's Rich Results Test to check syntax to avoid syntax errors that cause entire paragraphs to become invalid
5. Large-scale dynamic injection: Dynamically generate differentiated JSON-LD content based on the semantic themes of different pages, making each page an input node for the AI knowledge graph

In short, JSON-LD structured data is the "foundation" of GEO-without it, it will be difficult for AI to accurately understand, index and quote your content; with it, your brand is more likely to become the "answer itself" of AI-generated answers.

SGE (Search Generation Experience): Google AI Search Features Details

SGEYesSearch Generative Experience (search build experience)The abbreviation for Google's launch in 2023-2024Generated AI search capability.

Simply put:

• FunctionWhen users search, the AI is generated directly at the top of the results pageA comprehensive summary answer., and attach the source link, no need to click into the web page one by one.

• Timeline:

  • May 2023: Google Search LabsSGEName test.

  • August 2024: officially renamedAI Overviews(AI Overview) is fully online, but the industry is still used to callingSGE.

• Positioning: Google's version of "AI Search", similar to Bing's Copilot and Baidu's Wenxin Yenyan Search version. If you are doing cross-border/independent station/SEO: it is often said nowSGE Optimization/AEO, is to optimize the content for this new search ecology of "AI summary first display.

LLM (Large Language Model) Overview

LLM = Large Language ModelLiteral translation:Large language model

1. Full Name:Large Language ModelThe industry standard abbreviation is LLM.

2. Popular explanation: relying on massive text data training, with strong language understanding/generation ability of artificial intelligence model, our daily dialogue, copywriting, question and answer AI, basically belong to the LLM.

3. Commonly used abbreviations: directly in spoken EnglishLarge model,Large language model.

Translation & Analysis

Answer-readyStandard translation:Can answer directly,ready-made answer type

1. Word Meaning Disassembly

1. Word formation:answer(Answer/Answer)ready(Ready, ready-made)

2. Literal:Ready to give a direct answer

2. Large Language Model (LLM) scenario-specific understanding

This is a term commonly used in the LLM field and has two meanings:

1. Content attributerefers toNo model re-reasoning, retrieval, processing, it can be directly output as the text/knowledge base content of the answer. Corresponding antonym:reasoning-needed(need reasoning),retrieval-needed(Retrieval required).

2. Common Use Cases

• Answer-ready data / corpus:ready-made answer corpus(Pre-stored standard answer text in training/knowledge base)

• Answer-ready content:Direct answer type content(Suitable for question and answer, FAQ, standard answer, fixed speech)

• Contrast distinction:

  • Answer-ready: simple question and answer, fixed answer, speech, FAQ (low reasoning cost)

  • Generative/Reasoning content: Open authoring, logical reasoning, complex analysis (requires model generation thinking)

Practical translation of 3. (selected by scene)

4. example sentences

1. This is typical answer-ready content for customer service. This is typical customer serviceDirect answer type content.

2. We separate answer-ready texts from reasoning tasks.out-of-the-box answer textA distinction is made between reasoning tasks.