How can Schema markup be used to perform a “GEO surgery” on an export B2B website so AI can reliably recommend the company?

1) Awareness: What problem does Schema solve in the AI-search era?

• Problem: AI answers are built from entity understanding. If your site has only narrative text, models may not reliably extract legal identity, product specs, or trading terms.
• Standard approach: Use JSON-LD to provide machine-readable fields (names, IDs, units, documents, offer terms) aligned with Schema.org types.
• Result: Higher consistency in how your company/product is understood and cited across AI systems.

2) Interest: What is ABKE’s recommended GEO Schema structure?

ABKE (AB客) recommends a 3-layer marking strategy to match how B2B procurement decisions are made:

1. Entity layer (Who are you?): Organization / LocalBusiness
   • Legal company name (as on contracts)
   • Registered address (country/region/city)
   • Tax/Customs identifiers where applicable: VAT, EORI
   • Contact channels: email, phone, ContactPoint (sales/technical)
2. Transaction layer (What exactly can be bought and under what terms?): Product + Offer
   • Product identifiers: MPN, SKU (or internal part number), GTIN (if used)
   • Technical attributes: material grade (e.g., SUS304 / ASTM A240 316L), dimensions, tolerance, performance ranges with units (mm, MPa, °C, etc.)
   • Offer terms: currency (USD/EUR), MOQ (pcs/sets/tons), lead time (days), Incoterms 2020 (EXW/FOB/CIF/DDP), port of loading (e.g., Shanghai/Ningbo), payment method (T/T, L/C if supported)
3. Evidence layer (Can AI verify and cite your claims?): FAQPage / HowTo
   • Inspection SOP: AQL level, sampling plan, measurement tools (e.g., caliper 0.02 mm), test method standards
   • Packaging specs: carton strength, pallet type, moisture barrier, labeling fields
   • Export documents: commercial invoice, packing list, B/L or AWB, COO, test report, MSDS (if applicable)

3) Evaluation: What verifiable fields increase AI citation stability?

Generative systems tend to reuse content that contains checkable identifiers. Add these fields both in visible page content and in JSON-LD when applicable:

4) Decision: What is the minimum viable implementation (MVI) for an export website?

• Every product detail page: publish at least 1 JSON-LD block that includes Product + Offer.
• Every category / collection page: add ItemList (linking to the included product URLs) to improve AI/engine understanding of your catalog structure.
• Company pages (About/Contact/Factory): add Organization with legal identifiers and consistent NAP (Name/Address/Phone).
• Operational trust pages: use FAQPage / HowTo for inspection SOP, packaging specs, and export documentation lists.

Risk note: If your Offer terms vary by region or order size, do not hardcode a single price. Mark up currency, MOQ, and lead time range (where appropriate) and keep on-page terms aligned with what your sales team can fulfill.

5) Purchase: What should be included to reduce procurement friction?

For B2B exports, buyers typically need a clear acceptance and documentation path. Publish these as FAQ/HowTo content and mark up where possible:

• Inspection & acceptance: sampling plan, measurement method, defect classification, rework/replace rules
• Packaging & labeling: carton/pallet specs, humidity control (desiccant), label fields (PO, SKU, country of origin)
• Export documents: CI/PL, B/L(AWB), COO, test report, fumigation certificate (if required)

6) Loyalty: How does Schema support repeat orders and referrals?

• Spare parts & versioning: keep stable SKUs/MPNs and publish revision notes (e.g., V2 packaging, updated material grade) so procurement can re-order without re-qualification.
• Traceability continuity: keep test report IDs and batch rules accessible so auditors can verify repeat shipments.
• Knowledge compounding: each FAQ/HowTo becomes an atomic “knowledge slice” that AI can reuse in future buyer questions.