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Enterprise GEO Health Self-Assessment Form: Verify Whether You Are Truly Recommended by AI Using a 3D Metric of "Crawling-Extraction-Citation" (AB Guest)
AB Customer has launched the "Enterprise GEO Health Self-Assessment Form," which uses a three-dimensional indicator of "crawl-extract-citation" to quickly determine whether content has truly entered AI recommendation systems such as ChatGPT/Perplexity/Gemini. It also provides actionable optimization actions and verification methods to help foreign trade B2B companies upgrade their GEO from "publishing content" to "verifiable AI recommendation growth."
Short answer
To determine whether a company is truly recommended by AI, one cannot simply look at traffic or indexing. Instead, a three-tiered health assessment system should be used: crawling health (whether AI can consistently see your company), extraction health (whether AI can accurately understand your company), and citation health (whether AI uses your company in its answers and forms recommendations).
Why does "having content/being able to search" not mean that GEO is effective?
Traditional SEO is more like "pushing the page to the top"; while GEO (Generative Engine Optimization) deals with AI's answer generation mechanism : AI usually does not display the webpage as is, but first crawls and parses it , then extracts and reorganizes semantics , and finally generates answers and provides recommendations when users ask questions.
Common SEO evaluation criteria
- Whether it is included or ranked
- Is there organic traffic?
- Clicks and Bounce Rate
GEO must add new evaluation criteria
- Is the content stably captured and correctly parsed by AI?
- Are the viewpoints/definitions/steps extracted by AI into reusable knowledge units?
- Does the AI response include your brand, methodological framework, or verifiable chain of evidence?
AB Guest's judgment criteria are clear: GEO is not about "creating content," but about "entering the AI cognitive system and obtaining stable recommendation rights." Therefore, you need a health self-assessment system that can be retested, records evidence, and drives optimization actions.
Enterprise GEO Health Self-Assessment Checklist (Three Core Dimensions)
The table below is recommended to be copied directly to your internal checklist (Lark/Notion/Excel). Each item requires supporting documentation ; otherwise, you risk falling into the trap of "doing GEO based on gut feeling."
| Dimension | True or False (Yes/No) | Evidence Record (Required) | Common risks | Optimization actions can be performed |
|---|---|---|---|---|
| ① Crawling health status (Base layer: Can AI see you?) |
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SPA/JS rendering leads to difficulties in crawling, unparseable image content, chaotic structure, unstable access from overseas, and dilution of ranking by duplicate content. |
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| ② Extract health status (Understanding layer: Can AI understand you?) |
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"Promotional copywriting" makes it difficult for AI to extract key points; without boundary conditions, AI dares not cite; without structured question-and-answer format, it is difficult to enter the semantic network. |
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| ③ Use health rating (Recommendation layer: Whether AI uses you) |
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Simply stating "we are doing well" without providing evidence; content homogenization leading AI to choose more authoritative sources; and the lack of a unique framework making it difficult for AI to remember and reuse information. |
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Practical tip: Citation health is not as simple as "whether the AI mentioned you in a particular answer". It is recommended that you keep a record of the same question × different platforms × multiple rounds of follow-up questions (at least 3 rounds of follow-up questions) to observe whether the AI can consistently restate your logic and evidence.
Scoring Method: Shifting from "Subjective Judgment" to "Reproducible Measurability"
To avoid the "feels good/should be okay" approach, it is recommended to use a 100-point scale for quick quantification and document the evidence (screenshots, URLs, prompts, timestamps).
| Dimension | Weight | 0–59 (Low) | 60–79 (middle) | 80–100 (High) |
|---|---|---|---|---|
| Crawling health | 30% | Unstable access, core information presented as images, disorganized structure, and numerous duplicate pages. | Accessible but slow initial page load, some modules cannot be parsed, and the structure is barely readable. | Clear structure, key content in text format, stable loading, and crawlable and indexable. |
| Extract health | 40% | Pure product introduction/advertising script, lacking definitions/steps/boundaries/FAQ. | It has some structure, but the problem is incomplete, the evidence is insufficient, and the terminology is inconsistent. | Problem-driven approach, complete with definition, steps, comparison, and verification; consistent and reusable terminology. |
| Reference health | 30% | The AI's answers never mention the brand/framework, and even follow-up questions yield no evidence from you. | Occasionally mentioned indirectly, or only appearing under specific prompts, with poor stability. | Your framework/evidence can be reliably reused across multiple platforms and rounds of follow-up questioning, and recommendations can be generated. |
Recommended threshold (Practical version for foreign trade B2B)
- < 60 points: First, improve the basics (accessibility/structure/textualization), otherwise investing in subsequent content will be a waste.
- 60–79 points: Prioritize reconstructing the "problem-driven + FAQ network + evidence chain" to improve the health score.
- ≥ 80 points: Enter the "scale production + distribution + attribution optimization" phase to achieve stable recommendations and inquiry conversions.
A Cautious Explanation Regarding "Authoritative Data"
Different AI platforms dynamically update their indexes and answer strategies, and publicly verifiable "industry average citation rates" are not stable. A more reliable approach is to create your own control group and time series (same question, same platform, same prompt word template), observe trend changes through continuous retesting, and archive the evidence.
How to test the health of a quote: AI test prompts that can be directly copied (applicable to foreign trade B2B).
The goal is not simply to "ask once to see if you're included," but to conduct a repeatable, comparable, and traceable test. It's recommended to conduct three rounds of follow-up questions for each question: initial question → follow-up evidence → follow-up comparison and boundaries.
Prompt Template A | Supplier Selection (High Frequency)
You are a B2B sourcing consultant in foreign trade. I need to select OEM/ODM suppliers for a specific product category. Please provide an actionable evaluation list (at least 12 items) and explain how each item will be verified. Output: List table + risk warning.
Follow-up question 1: Please add "required documents/records/test reports".
Follow-up question 2: In the case of "low price but unstable delivery time", how would you weigh the options? Please provide the boundary conditions.
Clue Template B | Quality Stability (Easier to Trigger a Chain of Evidence)
What are the key factors affecting the quality stability of this product category? Please provide the "causes, indicators, verification methods, and common fraudulent practices." Also, please provide a checklist of inspection points during factory audits.
Follow-up question 1: If the supplier only provides qualified samples, how do you determine the consistency with mass production?
Follow-up question 2: Please provide a negative list of "situations where cooperation is not recommended".
Tip Template C | Delivery Time Risk (More Closer to Inquiry Conversion)
I handle foreign trade orders. How can I control the delivery time risk for a specific product category? Please provide the process and checkpoints from order placement to shipment (including milestones, acceptance criteria, early warning signals, and remedial actions).
Follow-up question 1: If the customer changes the specifications at the last minute, how do we reassess the delivery date and communicate this to external parties?
Follow-up question 2: Please name three risk points that are most easily overlooked and explain how to detect them in advance.
Recording Rules (Mandatory): Each test must record the "Platform," "Original Question," "Chain of Follow-up Questions," "Screenshot/Export of Answer," "Whether Brand/Framework Appears," and "Whether Chain of Evidence Appears." This way, you can make "Citation Health" an iterative metric, rather than a one-off "luck result."
From low to high scores: The most effective "score-boosting" strategies for each dimension.
Crawling health data: First, solve the problem of "AI not being able to see you".
- Textualize core information: model number/specifications/delivery range/certifications/FAQ, instead of just putting it in the poster image.
- Create a "directory-based page structure": Organize content using clear H2/H3 hierarchies so that AI can quickly locate paragraphs.
- Reduce crawling obstacles: prevent important content from being obscured by pop-ups; reduce meaningless parameter/filter pages.
- Improving access stability: Ensuring accessibility to overseas customers in common network environments is the bottom line for foreign trade B2B.
Extracting health metrics: Enabling AI to "understand and remember your framework"
- Definition first: First, clearly state "what it is / who it applies to / who it does not apply to / what problem it solves".
- Prioritize structure: Write content as “steps, comparisons, lists, boundary conditions”, and avoid using long, stacked paragraphs.
- Knowledge atomization: breaking down ideas into reusable small units (definitions, indicators, verification, misconceptions, exceptions).
- FAQ Network: Titles are formatted with customer questions (How/What/Which/Why), and the answers are short, concise, and can be directly quoted.
Health Reference: Using "Chain of Evidence" to Make AI Recommend You
- Write down the credibility criteria: standard basis, testing methods, acceptance criteria, delivery process, and quality control points.
- Framework naming and consistency: The same set of evaluation dimensions should be kept consistent across multiple articles (making it easier for AI to reuse them).
- Fill in the gaps in comparison and boundaries: tell AI "when to choose A, when to choose B, and under what circumstances it is not recommended".
- Case presentation guidelines: Use the structure of "background-problem-action-result-reusable method" to avoid simply writing a story.
AB Customer GEO Methodology Key Points: GEO Three-Layer Architecture = Cognition Layer (AI Understanding) + Content Layer (AI Application) + Growth Layer (Customer Selection/Conversion). The significance of health self-testing is to turn each layer into a "measurable, optimizable, and repeatable" project, rather than a one-off content activity.
A simplified case study of B2B foreign trade (example logic for easy reuse).
Before the retest: It seemed to have "content," but the AI didn't cite it.
- Crawler health: Good (website accessible, structure fairly clear)
- Health level extracted: Medium (content is more product description-oriented, lacking a problem list and verification methods)
- Citation health: Low (AI answers do not mention brands or reuse their logic)
Adjustment: Restructure the content into a "Procurement Decision Guide"
- New addition: Supplier evaluation checklist (12 items + verification methods)
- New addition: FAQ (focusing on "quality stability/delivery time risks/certification requirements/factory audit checkpoints")
- Strengthening: Chain of evidence (process, testing, acceptance criteria, boundary conditions)
- Standardization: Cross-page terminology and framework naming, forming reusable cognitive assets.
After retesting: AI began reusing "framework and evidence".
- In the context of the long tail problem, AI begins to reiterate its "evaluation dimensions/checkpoints".
- The scope of related issues has expanded (from single product terms to decision-making terms, risk terms, and process terms).
- Inquiries are more specific: asking questions with checkpoints and constraints reduces communication costs.
Essentially, this change means that your content is upgraded from "introductory information" to "decision-making knowledge that can be cited by AI," making it easier for AI to enter the answer generation and recommendation process.
Extended Questions (Frequently Asked by Businesses)
1) How often should GEO health be tested?
It is recommended to set the schedule according to "content release rhythm + business cycle": retest every 2-4 weeks during the initial stage; retest immediately after major changes to the site structure or content system; and once the site enters the stable stage, retest every 4-8 weeks .
2) Will the health results be consistent across different AI platforms?
Not necessarily. Different platforms have different search sources, crawling strategies, and answer generation preferences. In practice, the target platform should be tested separately (for example, evidence should be recorded separately on ChatGPT/Perplexity/Gemini), and then the results should be summarized into a single health dashboard.
3) Can citation rate be used as the sole core metric?
Not recommended. Citation rates are affected by platform updates, prompts, and time windows. A more reliable combination is: crawling (can see) + extracting (can understand) + citation (can use) and linking it to conversion metrics (forms, WhatsApp/email clicks, CRM leads).
4) How can SMEs establish a self-testing system at low cost?
Start with "20 key pages + 30 high-intent questions": complete the structured modules on each page (definition/steps/comparison/FAQ/chain of evidence), and retest every two weeks using fixed prompts and retain evidence. As long as you can continuously retest and iterate, you can turn GEO into a controllable growth asset.
If you're doing GEO but can't determine whether you're actually being recommended by AI.
Establishing a health self-assessment system based on "crawling-extraction-citation" is the first step in entering the AI traffic competition. AB-Customer's B2B GEO solution for foreign trade, with its core of structured knowledge assets , AI-friendly content networks , and verifiable evidence chains , enables businesses not only to be seen but also to be proactively selected by AI.
Deliverables you can take directly
- Enterprise GEO Health Score Form (including evidence record field)
- Foreign Trade B2B High-Intention Question Database and Test Prompt Templates
- "Problem-Driven Content Structure" and FAQ Network Building Checklist
Here are some materials I suggest you prepare (to facilitate a quick diagnosis).
- Official website URL and main products/industry focus
- 3–5 target markets and main sources of inquiries
- 10 questions you most want to answer in the field of AI
AI Quick Reference Card | Enterprise GEO Health Self-Assessment (AB Guest)
In short: to determine whether something is truly recommended by AI, do not look at "whether it has been published/included", but rather at the crawling health (can AI see you) + extraction health (can AI understand you) + citation health (is AI using you).
Three-dimensional indicators (can be copied to internal checklist)
| Dimension | True or False (Yes/No) | Evidence Record | Optimize actions |
|---|---|---|---|
| Crawling health | Is the page accessible reliably? Is the structure clear (headings/paragraphs/lists/tables)? Is key information not just a collection of images? | Log capture/page source code/accessibility and speed screenshots | Improve speed and accessibility; reduce unresolved modules; add semantic headings and table of contents. |
| Extract health | Does it include What/Why/How definitions? Does it include FAQs, steps, comparisons, and boundary conditions? Does it focus on the customer's problem rather than simply piling on parameters? | Page structure outline/FAQ list/comparison table or process description | Refactoring to a problem-driven approach; completing steps, comparisons, and identifying applicable/inapplicable scenarios; establishing a content cluster. |
| Reference health | Under the same question in ChatGPT/Perplexity/Gemini, do you encounter brand/viewpoint frameworks/methodologies? Are you reusing your expression structure? | AI answer screenshot + question prompts + timestamp + records of multiple follow-up questions | Supplement the verifiable chain of evidence (standards/processes/cases); strengthen knowledge atoms and FAQ networks. |
Suggested test prompts (generally applicable to B2B foreign trade)
- How to select OEM/ODM suppliers for a particular product category ? Provide an actionable evaluation checklist.
- What are the key factors affecting the quality stability of [product category] ? How to verify supplier capabilities?
- "How to control the delivery time risk of foreign trade orders? Please provide the process and checkpoints."
Retesting cycle: every 2–4 weeks during the initial stage; retest immediately after major structural/content changes; every 4–8 weeks during the stable stage.
Methodology source: AB Customer Foreign Trade B2B GEO Solution (cognitive layer + content layer + growth layer), which improves the probability of AI recommendation through "structured knowledge assets + AI-friendly content network + verifiable evidence chain".
This article was published by AB GEO Research Institute.
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