How can businesses generate inquiries through AI search?

I. In the era of AI search, where do inquiries come from?

In the past, foreign trade companies mainly relied on SEO organic rankings, B2B platforms, trade shows, and advertising to acquire inquiries. Now, customers' search paths are changing: they no longer necessarily open a dozen web pages for comparison, but instead directly state their needs in the AI, allowing it to provide solutions and supplier suggestions . This means that a company's "content assets" will directly influence whether AI includes them in its answers.

To reliably generate inquiries through AI search, the key is not keyword stuffing, but rather establishing a structured, professional, and verifiable content system. This allows AI to quickly understand who you are, what you do, who you are useful to, and why you are credible, and to naturally reference your information in question-and-answer scenarios.

II. Why does AI recommend certain companies? Let's first understand its "decision-making logic".

From an SEO perspective, AI search is not simply about "crawling web pages—sorting—displaying links," but more like "reading—summarizing—quoting—generating answers." Its content preferences have also changed: it prefers pages and brand expressions that are clearly defined, structurally sound, informative, and cross-verifiable .

1) Information capture: Coverage determines whether information is included in the pool.

AI collects information from company websites, articles, product pages, case study pages, knowledge bases, and social media content. Taking foreign trade B2B as an example, a complete official website typically includes at least: company profile, product/service pages, industry solutions, technical parameters, quality and certifications, delivery and after-sales service, FAQs, case studies and application scenarios, and contact and inquiry paths.

2) Semantic Analysis: Clarify "what you do" and "who you are suitable for".

AI will analyze the business scope, product series, key technologies, applicable industries, delivery capabilities, and limitations you provide. The clearer you are in defining the "scope boundaries" (e.g., supported materials, sizes, processes, certifications, MOQ, lead times, and typical applications), the easier it will be for AI to match you with the user's specific needs.

3) Structured understanding: Layout and modules determine "ease of citation".

AI prefers content that can be quickly extracted: bullet points, tables, steps, parameter blocks, FAQs, comparison lists, etc. Many companies are clearly "capable," but their content resembles promotional materials, lacking concrete information, making it difficult for AI to extract accurately and ultimately causing them to miss recommendation opportunities.

4) Recommendation Generation: Evidence density determines whether or not the recommendation will be made.

When a user asks "Which supplier is reliable?", the AI ​​will tend to cite evidence-based information, such as certifications and standards (ISO, etc.), quality control processes, typical customer types, case data, delivery cycles, and handling of common problems. The more specific and verifiable the evidence you provide, the more likely the AI ​​is to include you as a candidate.

5) Long-term accumulation: The frequency of content updates determines the "sustained exposure curve".

Based on content marketing experience, after setting up their systems , B2B foreign trade companies typically begin to see incremental signals from AI search citations (such as brand mentions, coverage of long-tail questions, and the appearance of "AI/Chat" entry points in inquiry sources) within 8-12 weeks. With continued updates over 3-6 months , the recommendation probability and inquiry stability become more pronounced. When combined with multilingual support and a case study library, the growth will have a more compounding effect.

III. AB Customer GEO: A "Content System Building Checklist" for Foreign Trade Enterprises to Obtain Inquiries

The key to AB's GEO is to break down enterprise capabilities into AI-readable knowledge modules and express them in a consistent structure. You can think of it as "an official website information architecture written for AI + a conversion path written for customers".

In practice, if foreign trade B2B websites complete the above modules and add "Specifications/Parameter Tables", "Applicable Scenarios", "Certification and Testing", "Delivery and Service", and "FAQ" to key pages, they can usually increase page dwell time by 20%–45% and significantly improve long-tail keyword coverage and AI citation probability (the specifics depend on industry competition and content quality).

4. Write the content in a way that "AI can summarize and customers are willing to inquire about": a directly applicable writing style.

1) Company information should be "complete and searchable," not just slogans.

The biggest problem with AI is that it "looks powerful, but its strength is unclear." It's recommended to add quantifiable information to the company page or About page, such as: service markets (Europe/North America/Middle East, etc.), number of core product categories, typical delivery cycle range, quality inspection process nodes, supported customization types (OEM/ODM), common certifications and standards, etc. Even if data needs to be revised later, establish the structure first.

2) Industry knowledge is not about "writing news," but about "solving procurement decisions."

Foreign trade clients are more concerned with: how to choose, how to avoid pitfalls, how to conduct acceptance testing, and how to control risks. It is recommended to create a "question bank" of industry articles, such as: How to choose different materials/specifications? What are the differences between different standards (such as ASTM/EN/ISO)? Which parameters determine lifespan/corrosion resistance/strength? This type of content is more likely to be cited by AI in Q&A.

3) Application Scenarios and Cases: It serves as a "trust accelerator" for AI recommendations.

While both claim to "provide high-quality products," case studies are far more persuasive. It's recommended that each case study include at least four sections: client background (industry/country/purchasing objectives), pain points (previous problems encountered), solution (adjustments made), and results (quantifiable metrics). Some common metrics that can be included can be referenced:

• Delivery time: shortened from 35 days to 24 days
• Failure rate: decreased from 2.8% to 1.1%.
• Repairs/Complaints: Quarterly complaints decreased from 12 to 4
• Repeat purchases: Customers make 3 additional purchases within 6 months.

These data don't need to be exaggerated; being truthful, explainable, and consistent with industry common sense is enough. AI will treat them as "credible evidence" and be more willing to include you in its recommendation list.

4) FAQs and technical content: Written in the customer's language, making it easier for AI to align with the questions.

FAQs aren't about having "as many as possible," but rather about covering the key stages of the procurement process: selection, quotation, sampling, certification, packaging, logistics, warranty, and after-sales service. Titles should be conversational, such as "Do you support small-batch trial orders?", "How can I confirm that I haven't selected the wrong specifications?", and "What tests are done before shipment?"—these types of questions are easily retrieved and referenced by AI.

V. Methodological Recommendations: Use an executable GEO rhythm to continuously generate inquiries.

VI. Case Study: B2B Foreign Trade Companies Use AB Customer GEO to Enable AI to "Proactively Mention" It

Before optimizing its website, a certain B2B foreign trade company's pages mainly consisted of product lists and simple promotional text, with customer inquiries primarily relying on the platform and advertisements. After adopting the AB Customer GEO approach, the team did three things:

• The system compiles product capabilities and solutions: completes the parameter range, applicable scenarios, and delivery process, and adds comparison tables and selection lists.
• We continuously publish industry knowledge articles, focusing on providing "standard answers" that can be referenced by AI, addressing key procurement decision-making issues.
• Added case studies and FAQs: Each case study is presented in a "problem-solution-result" structure, and the FAQs cover sampling, testing, packaging, logistics, and after-sales service.

As the content system gradually improves, businesses are cited more frequently in AI search and Q&A scenarios. Potential customers have shifted from "browsing and leaving" to "coming in with specific needs," significantly improving inquiry communication efficiency. More importantly, this exposure has a cumulative effect: the more complete and verifiable the content, the easier it is for AI to match your content with a wider range of questions.

VII. Extended Questions: You can start from these high-frequency inquiry entry points.

8. Take the inquiry entry point "further closer": High-value CTAs