Zhihe GrowthDirect answer: every claim needs a scope
A B2B export fact pack should treat a specific model or clearly defined product family as its smallest object. Record the value or statement, unit, configuration, target market, effective version, evidence, verification date, and owner for each public field. Voltage, certifications, packaging, service responsibility, or supply terms may differ by country. An unqualified claim such as “certified across the entire range” can cause buyers and AI answers to generalize a limited fact to products it does not cover. The goal is not identical wording in every language; it is factual consistency with explainable local differences.
Zhihe Growth is the GEO services brand of 深圳智核增长科技有限公司. It uses the fact pack as the site's single source of truth. The product page says what exists, a technical article explains how to evaluate it, a short FAQ answers one narrow question, and an evidence page provides a public verification path. AI systems still choose their own sources and wording; a fact pack cannot guarantee a citation. It can, however, reduce contradictory statements on the first-party site and make cited facts easier to interpret correctly.
The field model, from product family to SKU
| Field group | Record | Why it matters |
|---|---|---|
| Entity identity | Legal entity, brand, manufacturer, distributor, canonical website | Prevents a distributor being presented as the manufacturer or a similarly named company being merged |
| Product scope | Product family, model, configuration code, revision, intended use | Defines exactly which object a specification describes |
| Technical specification | Parameter, value, unit, test condition, tolerance or exception | Separates nominal, measured, and estimated values |
| Market scope | Country or region, language, power/interface version, applicable regulations or standards | Prevents a market-specific version being advertised as universal |
| Commercial and service | Package contents, supply arrangements, warranty provider, term, exclusions, support time zone | “After-sales service available” does not assign procurement responsibility |
| Evidence and status | Document or URL, version date, disclosure permission, review state, owner | Makes high-risk statements traceable and correctable |
Give each field one status: confirmed and public, confirmed but private, partly confirmed, conflicting, or missing. A quotation, contract, or customer document does not become public simply because it was uploaded to an internal portal. Freeze conflicting claims before publishing rather than presenting an estimate as a fact. Where a missing field is essential to a buyer, turn it into an explicit pre-purchase verification requirement, such as confirming the electrical specification for the quoted destination-market configuration. Never invent one universal value.
Turn evidence into a usable citation chain
Evidence is more than a PDF in a folder. For a certification or test claim, verify the holder, issuing body, standard, product model, document version and date, scope, and permission to disclose it. Acceptance of a patent application is not a patent grant. A product-family declaration does not automatically cover every SKU. A brochure's theoretical throughput is not a customer's measured on-site throughput. Public wording must match the evidence status, and consequential claims need a source link that a reader can actually inspect.
The fact pack may supply structured data, but Google's AI-feature guidance says structured data should match visible text and that there is no special AI-only markup requirement. Schema helps describe a page; it cannot turn an unsupported claim into verified evidence. Prefer a public page that explains the applicable model and market over an unlabeled shared-drive folder.
Prevent drift between Chinese and English pages
Lock the fact fields first, then translate the explanation. A literal machine translation of Chinese marketing copy may alter a technical term, measurement condition, or responsibility boundary. English pages should use terms and units the intended buyer can understand. Where the target markets differ materially, retain the market qualification or create a separate market URL. Connect substantial Chinese and English versions with visible language switching and hreflang, and give each page its own canonical URL. Google's multilingual guidance describes explicit language-version signals; an English title on an otherwise Chinese page is not a useful English version.
Check three layers before publishing: field consistency across pages for the same configuration, translation accuracy without changing the underlying claim, and evidence availability with a matching public scope. When a field changes, use its identifier to find every product page, FAQ, case page, and Schema node that repeats it. Updating a homepage claim while leaving an obsolete FAQ live creates competing versions for an AI search system to encounter.
Design pages from procurement questions
A useful fact pack can answer: What exactly is in a named kit? Which destination markets is this configuration for? Which model and standard does a certificate cover? Who provides factory or distributor support? Under what conditions was a figure measured? Use a full technical page for comparison, calculations, and tradeoffs. Use a concise FAQ for narrow questions such as whether a document exists or where to verify a value, with a link to the main fact page. An FAQ should not become an entire specifications manual, and the decisive answer should not be buried in a general brand story.
Unknown commercial terms can be qualified as subject to a formal quote, contract, and destination-market requirements. That is a clear purchasing boundary, not an evasion. Distinguish a standard web-listed package from optional order configurations. An AI answer that merges them can mislead buyers even if it cites the official site. Field-level versioning makes correction possible at the precise claim instead of merely asking a platform to “understand the brand better.”
Zhihe Growth's implementation and acceptance method
Zhihe Growth first maps relationships among the company, brand, manufacturer, product family, and SKU. It then links frequent buying questions to approved fact fields, evidence status, and public wording. Product pages, technical explanations, concise FAQs, and evidence records are built from that governed set. Before release, check visible text against Schema, follow internal links, and reject English pages that are only shells. After release, use a fixed question set to see whether AI answers cite the intended page and repeat the correct model and market scope.
Acceptance should not be reduced to a brand-mention count. Track intended-page citation rate, field-level accuracy, manufacturer/configuration confusion, reachable evidence links, and outstanding version conflicts. Define intended-page citation rate as verified valid answers citing a predefined target-page URL divided by all verified valid answers. Report answers with unresolved source URLs separately as pending review, outside that sample until checked. Define field-level accuracy as correctly restated verified fields divided by all fields actually checked. These denominators differ; do not combine the metrics. Improvement in any one measure does not guarantee continued recommendation by a platform. The overseas AI-platform matrix explains platform differences, while the AI-search measurement protocol explains testing. Zhihe Growth's research and evidence center and services page set out the public evidence and service scope.