ABKE GEO Quarterly Audit Report: How are core metrics generated and used to verify "Will AI recommend you?"

In the era of generative search, many companies encounter three types of "invisible problems":

• The content on your website clearly exists, but AI cannot access it or enter the referrable entry point (technical and visibility issues).
• AI can see the page, but it cannot extract clear conclusions, steps, and boundaries (semantic structure problem).
• AI understands your message, but it won't cite it because it lacks a verifiable chain of evidence, entity connections, and differentiated representations (trust and citation issues).

Therefore, the principle of AB Guest's GEO audit is: don't ask "how much content we published", but only ask "what changes have occurred in the answers due to AI". In this way, the GEO is upgraded from "executing actions" to "manageable growth system".

1) AI Crawling Data Engine (Visibility Layer) → Generate AI Crawling Rate

This layer addresses whether the content has entered the AI's data entry point. A common problem in foreign trade B2B is not a lack of content, but rather insufficient technological accessibility and structured support, which makes it "invisible to AI."

2) AI Semantic Parsing Engine (Understanding Layer) → Generate AI Extraction Rate

This layer assesses whether "AI can explain things clearly to you." AB Guest GEO inputs the page content into the model under unified rules, requiring the model to output: conclusions, key points, steps, boundary conditions, and evidence . This is then aligned with the standard answer/target structure to obtain an extraction stability score.

A low extraction rate is usually not due to "insufficient writing length," but rather a lack of structural anchor points (definitions/boundaries/steps/evidence) that can be reliably extracted by the model. AB Guest GEO will output a "missing element list," turning optimization into an actionable revision task.

3) AI Multi-Model Behavior Engine (Reference Layer) → Generates AI Reference Rate + Weight Index

This layer involves "testing the same question on multiple models." Because different models differ in retrieval, citation preferences, and answer organization, the conclusions of a single model can easily be misled by chance. ABKEGEO uses multi-model cross-validation to assess whether citation behavior occurs consistently .

4) Industry Intent Matching Engine (Coverage Layer) → Generate Core Intent Coverage

Inquiries in B2B foreign trade are not "buy on sight," but rather go through a series of questions: Does it meet the standards? Is it customizable? Delivery time? Warranty? Case studies? Certifications? Risks? Alternative solutions? This layer uses an industry question bank to structure the procurement decision-making process and calculates how many "high-intent question entry points" you have covered.

The coverage output is not just a percentage, but also includes: a list of gap issues (which key issues are not covered by the page) and priorities (addressing the issues closest to the transaction first).

Step 1: Create a set of highly intentional questions that are “testable” (50–200 questions)

• Issues are extracted from inquiries/emails/quotes/sales records and categorized according to the "decision chain hierarchy".
• For each question, clearly state: the target language , the expected key points of the answer , and the page to be displayed (or a new page to be displayed).
• Avoid including "brand names" in the question itself (otherwise you will overestimate the mention rate).

Step 2: Fix the testing conditions (same question, same classmates, multiple models)

• Test within the same time window (to reduce noise caused by model/index fluctuations).
• Same question, same language, same prompt format.
• It covers at least several mainstream generative search/question-answering ecosystems as cross-samples.

Key principle: Compare trends quarterly, don't chase single "accidental hits".

Step 3: Output a "Four-Layer Gap List" (from issue to page)

• Crawling gaps: Which key pages are unreachable/weakly indexed?
• Extraction Gap: The model cannot extract the page for "Conclusion/Steps/Boundaries/Evidence".
• Citation gap: The question is relevant but doesn't cite you (homogeneity/insufficient evidence/weak entity).
• Coverage gaps: Which issues in the decision-making chain are not covered by pages?

Step 4: Complete the evidence chain with "knowledge atoms" (to make AI dare to use it)

ABKE's GEO suggests breaking each key topic down into the smallest verifiable units, and then combining them into a content network:

Step 5: Quarterly retesting and trend analysis (making optimization manageable)

• By retesting the same set of questions quarterly, a trend chart is generated: which indicators have entered the stable range, and which are fluctuating.
• Map "indicator changes" to "action changes": which pages were modified, which issues were fixed, and what evidence was added.
• For foreign trade B2B, prioritize monitoring two types of changes: citation rate (answer placement) and intent coverage rate (high-intent entry point).

When the AI ​​crawling rate is low:

First, improve technical accessibility and site structure; otherwise, content investment will be difficult to get into AI-accessible entry points (which is an "invisible" loss).

When the AI ​​extraction rate is low:

Prioritize the reconstruction of semantic structure: conclusions first, steps outlined, boundaries clear, and evidence verifiable, so that AI can "understand".

When AI citation rate is low:

Complete the evidence chain and entity connections (standards/parameters/operating conditions/comparison points), and change homogeneous content into "citationable answer sources".

When core intent coverage is low:

Complete the issue matrix according to the procurement chain: selection comparison, compliance risks, delivery and after-sales service, cost ROI, etc., and prioritize covering the issue entry points "closest to the transaction".