Yaward Industrial Development Limited
江门市雅伟德实业发展有限公司
CN · Home Products · Est. 1992 · yaward.goldsupplier.com
Last refreshed 2026-06-01
Based on 2 AI engines across 1 buyer language.
AI Readiness pillars
How Yaward Industrial Development Limited performs against the three pillars buyers care about when sourcing via AI.
Weight 40% · Do multiple AI engines recognise you and tell a consistent story
Weight 30% · When buyers ask AI to source in your niche, do you come up
Weight 30% · How AI engines rate you on quality, reliability, pricing
What AI says
AI引擎关于Yaward Industrial Development Limited的信息非常有限,该公司是一家位于中国的家居产品供应商。在近期的查询中,该公司综合得分为100分中的33分(暗信号),其中最强维度为数字足迹,得分为100分中的70分,最弱维度为买家信任,得分为100分中的0分。在测试的2个AI引擎(Claude Sonnet、deepseek-chat)中,有2个就公司身份、采购及信任相关提示返回了实质性信息。AI呈现的产品包括家用清洁工具、塑料厨具及收纳用品。AI回复中未出现任何认证或第三方验证信息,这导致买家信任维度得分为100分中的0分,AI推荐率为100分中的20分。最大的改进机会在于买家信任维度,下方的改进路线图按预期得分提升幅度列出了供应商可执行的具体行动,并按优先级排序。
AI engine breakdown
One row per AI engine. Aggregates identity, sourcing, trust, and verdict queries.
| AI Engine | Found | Sentiment | Rating | Products mentioned | Certifications |
|---|---|---|---|---|---|
| Claude Sonnet | 1 / 4 | neutral | — | None | None |
| DeepSeek | 1 / 4 | neutral | — | household cleaning tools, plastic kitchenware, storage items | None |
Improvement Roadmap
Concrete actions to lift this supplier's Signal Score, ranked by impact. Generated from the latest audit.
- 1
Register and optimize LinkedIn company page with schema markup
+15 ptsCreate or claim a LinkedIn company profile for Yaward Industrial Development Limited with complete founding year, employee count, and office location. Add Schema.org Organization markup to your website header. Both actions increase AI discoverability and signal legitimacy to LLM training pipelines and web crawlers.
Identity & PresenceLow effort
- 2
Publish LFGB and GMC certification documents on About page
+28 ptsCreate a dedicated Certifications section on your website and upload scanned copies or official attestations of your LFGB and GMC certifications with issue/expiry dates visible. This directly addresses AI's detection of zero trust signals and enables LLMs to cite concrete verification during sourcing queries.
Buyer Trust SignalsLow effort
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