Structured Data Markup (How-To): How should we correctly label our factory address and export records so AI can verify our identity and delivery credibility?

Why this matters in GEO (Awareness)

In the AI-search era (ChatGPT, Gemini, Deepseek, Perplexity), buyers often ask questions like “Who is a reliable supplier?” or “Which factory can meet this spec?”. AI systems rely heavily on consistent entity signals and verifiable evidence to decide whether to recommend a supplier.

For B2B exporters, two of the strongest trust signals are: (1) factory identity & location and (2) export evidence (countries, HS codes, and customs declaration data).

What to structure as “entity fields” (Interest)

ABKE recommends turning scattered company info into structured, citable fields and keeping the same values across pages (About, Contact, Factory Tour, Capabilities, Certifications, FAQ, and downloadable documents).

A) Factory address (minimum verifiable set)

• Legal entity name (matches business registration and bank documents)
• Factory/production site name (if different from legal entity)
• Full postal address (country, province/state, city, district, street, postal code)
• Geo coordinates (latitude/longitude) when available
• Business registration identifier (e.g., Unified Social Credit Code / registration number, if publishable)
• Contact endpoints: phone, email (use consistent formatting)

B) Export records (what to label and how to reference)

• Export countries/regions (use official country names; keep a stable list)
• HS codes (use the most accurate HS code range you can support; include version/year if relevant)
• Customs declaration evidence: declaration number references or redacted samples (where legally allowed)
• Shipping terms used historically (e.g., Incoterms such as FOB/CIF—only if you can document them)
• Supporting documents: redacted B/L, commercial invoice templates, packing list templates (avoid exposing customer PII)

Implementation checklist (Evaluation)

1. Standardize the “single source of truth”: define one canonical version of the company name, address, and export fields (countries/HS codes). Store it in an internal sheet/KB and reuse everywhere.
2. Use structured data on key pages: implement JSON-LD (or equivalent) on pages like Contact, About, Factory, Export/Compliance, and relevant product category pages.
3. Keep site + content consistent: the same factory address and export field list should appear in the website footer, company profile, factory pages, PDFs, and FAQ answers.
4. Add evidence links: attach verifiable references such as certification IDs, audit reports, and redacted customs/shipping document samples (where permitted). AI systems weigh evidence chains higher than claims.
5. Entity linking: ensure the legal entity name, brand name, and factory site name are explicitly connected (e.g., “Brand ABKE is operated by Shanghai Muke Network Technology Co., Ltd.” if applicable on your own site).

Risk boundaries & compliance notes (Decision)

• Do not publish sensitive customer data: redact consignee names, addresses, phone numbers, and any personally identifiable information in export evidence.
• Avoid unverifiable totals: if you cannot legally prove “X containers/year” or “Y countries served”, do not state it as a fixed number.
• HS code accuracy matters: using incorrect HS codes can create compliance risk and damage credibility. Only list codes you can justify with product specs and past declarations.
• Multi-location factories: if you have multiple sites, label each site explicitly and connect them to the same legal entity; do not merge addresses into one.

Delivery SOP: what ABKE implements for you (Purchase)

• Asset modeling: convert your factory identity, capacity, certifications, and export evidence into structured knowledge fields.
• Knowledge slicing: split long company introductions and export stories into atomic facts (address, codes, documents, scope, limitations) that AI can quote.
• Site-wide consistency: ensure your website, FAQs, and downloadable documents display the same entity values.
• Evidence chain: build a publishable reference set (certification identifiers, audit references, redacted document samples).
• Continuous optimization: adjust fields and references based on AI recommendation/visibility feedback and content performance signals.

Long-term value (Loyalty)

Once your factory address and export evidence are structured into stable fields and consistently published, they become part of your permanent digital knowledge assets. Over time, repeated citations and consistent entity signals help AI systems form a clearer company profile—improving recommendation confidence without relying solely on paid traffic.