エンティティとは何ですか。なぜ検索とAIにとって重要なのですか。
An entity is a structured, machine-readable record that defines your organisation as a distinct, identifiable subject — with verified attributes, relationships, and identifiers that AI systems can reference with confidence. In Japanese enterprise AI contexts, entity records in Japanese Wikipedia, Japanese-language Wikidata attributes, and authoritative Japanese business databases are the primary sources AI systems consult when constructing answers about companies in your sector. Without a coherent entity record in Japanese-language sources, AI engines operating in Japanese either omit your brand from relevant answers or generate inaccurate descriptions. The entity record is the infrastructure that all other forms of AI visibility depend on.
日本語Wikipediaの記事作成には、どのような条件が必要ですか。
Japanese Wikipedia (ja.wikipedia.org) applies a notability standard that requires significant coverage in reliable, independent Japanese-language sources — Nikkei, J-CAST, industry trade publications, or major business news outlets. Companies with limited Japanese-language press coverage may not currently qualify for a standalone Japanese Wikipedia article. In the Entity Gap Audit, we assess precisely what coverage exists, what sources carry sufficient weight for the Japanese Wikipedia editorial community, and whether a Wikidata enrichment and Knowledge Graph entry can achieve the entity trust goals in the interim. We are direct about what is immediately achievable and what requires a press coverage development pathway first.
作業完了までにどのくらいの期間がかかりますか。
The Entity Gap Audit and Entity Structure Plan are completed within two to three weeks. The Core Entity Build — Japanese Wikipedia article, Wikidata enrichment (both languages), and Knowledge Graph submission — takes four to six weeks, reflecting the additional complexity of bilingual entity architecture and the Japanese Wikipedia editorial review process. The Directory Citation Campaign runs in parallel and is typically complete within four weeks. Most clients should plan for a total timeline of ten to fourteen weeks from engagement to a fully deployed bilingual entity infrastructure. Japanese Wikipedia editorial review timelines can vary as they depend on volunteer editorial processes; we manage that process throughout.
費用はどのくらいかかりますか。
Entity Trust Building for Japan-market organisations is priced from ¥650,000 for the complete programme, covering the bilingual audit, entity architecture design, Japanese and English Wikipedia work, Wikidata enrichment, Knowledge Graph submission, 20+ directory citations, and the first quarterly monitoring review. Organisations with more complex entity structures — multiple legal entities, regulated industry memberships, or extensive subsidiary relationships — are priced based on audit findings. All pricing is confirmed in writing before any work begins.
日本市場で最も重要なディレクトリはどれですか。
For Japanese market entity trust, the highest-weight sources are those that Japanese AI engines and institutional research tools treat as authoritative. Teikoku Databank (帝国データバンク) and Tokyo Shoko Research (東京商工リサーチ) carry the most weight for enterprise B2B credibility — these are the sources procurement teams, banks, and institutional clients verify against. J-CAST and Nikkei company profiles are significant for AI entity training data in Japanese-language contexts. For international companies operating in Japan, consistency between your Japanese Corporate Registry record, your Google Business Profile in Japanese, and your Wikidata Japanese-language attributes is the foundation. We select citations based on your specific sector and the sources that carry the most weight for your entity type.
エンティティ構築はAIの視認性をどのように向上させますか。
AI engines — including the large language models powering ChatGPT (Japanese), Bing Copilot in Japanese, Perplexity, and Google Gemini — reference structured, authoritative data when constructing answers. In Japanese-language contexts, these systems prioritise Japanese-language sources: ja.wikipedia.org, Japanese-language Wikidata attributes, and Japanese business databases. When your entity record is coherent and well-documented in those sources, AI systems have the structured data they need to include your brand in relevant answers, attribute correct information to you, and represent your organisation accurately to the Japanese-market prospects who are asking AI tools to help them evaluate vendors in your space. Entity building is the foundational work — without it, every other content and visibility investment operates on an uncertain basis.
