ENTITY TRUST BUILDING · エンティティ信頼構築

AIが信頼するブランドを構築する。あなたの会社は、AIに認識されていますか。

ChatGPT、Perplexity、Google Gemini、そして日本語版のBing Copilotは、あなたの会社ウェブサイトを直接参照して情報を収集しているわけではありません。これらのAIエンジンは、Wikipedia、Wikidata、Googleナレッジグラフ、そして帝国データバンクや東京商工リサーチといった権威ある情報源から構造化されたエンティティデータを読み込んでいます。 If your brand doesn't exist as a structured entity in those sources, AI engines either ignore you entirely or generate inaccurate descriptions that reach your prospects before you ever do. Ignited Nepal builds the entity infrastructure — in both Japanese and English — that makes your organisation recognisable, verifiable, and accurately represented across every AI platform and search algorithm your enterprise clients use.

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This is for you if

WHO THIS IS FOR

**Enterprise B2B companies entering or operating in Japan** — foreign companies establishing a Japan presence and Japanese companies seeking recognition in international markets both face the same structural problem: their entity records exist in one language and one set of directories, while the AI engines their clients use draw on multilingual, multi-source entity data. A company that is well-documented in English Wikipedia but absent from Japanese Wikipedia is effectively invisible to AI systems operating in Japanese, and vice versa.

**Financial services, insurance, and investment firms** — regulated entities operating under FSA oversight whose institutional clients and counter-parties use AI-assisted due diligence tools. An entity record that surfaces accurately in Nikkei company profiles, Teikoku Databank, and Tokyo Shoko Research carries the kind of weight that procurement committees and institutional investors recognise.

**Manufacturing and industrial supply chain companies** — Japanese manufacturers, Tier 1 and Tier 2 suppliers, and precision engineering firms whose international clients increasingly conduct AI-powered vendor qualification research before initiating procurement discussions. Accurate entity records in both Japanese and English language sources are the baseline expectation for enterprise supplier qualification.

**Professional services and consulting firms** — management consulting, legal, accounting, and technology advisory firms targeting the Japanese enterprise market where the decision-making process is thorough, relationship-dependent, and informed by verifiable third-party records from sources like Teikoku Databank rather than self-published marketing material.

What's broken

WHAT'S BROKEN

Japanese-language AI systems cannot find your entity.

When a Japanese procurement manager uses a Japanese-language AI assistant to research vendors in your sector, that system draws primarily on Japanese-language sources: Japanese Wikipedia (ja.wikipedia.org), Japanese Wikidata descriptions, J-CAST, Nikkei company profiles, and Japanese-language business registries. If your entity record exists only in English, the AI system either cannot find you or retrieves an incomplete, machine-translated description that communicates nothing about your actual capabilities and credibility.

Teikoku Databank and Tokyo Shoko Research show incomplete or missing records.

In Japan's enterprise B2B environment, Teikoku Databank (帝国データバンク) and Tokyo Shoko Research (東京商工リサーチ) are among the most trusted sources for company verification. When an AI system trained on Japanese business data encounters a company with a thin or absent record in these databases, it treats that company as an unverified entity — and that signal propagates through every AI-assisted research process that references it.

Your Wikidata entity lacks Japanese-language attributes.

Wikidata stores multilingual descriptions and labels for every entity. A record that has English-language descriptions but no Japanese label (日本語ラベル), no Japanese description (日本語説明), and no Japanese aliases means that Japanese-language AI systems querying Wikidata receive no structured data for your entity. This is a systematic gap that most international companies have not addressed, and it creates a compounding disadvantage as AI adoption in Japanese enterprise procurement continues to accelerate.

Your Knowledge Panel appears incorrectly or not at all in Japanese search.

Google's Knowledge Panel for your brand should display accurate, consistent information in both English and Japanese when users search from Japan or in Japanese. When the underlying entity data is incomplete or inconsistent, the Knowledge Panel either does not appear, displays incorrect information, or shows a poorly structured representation that undermines rather than reinforces the institutional credibility your brand has spent years building.

What we engineer

WHAT WE DO

Bilingual entity infrastructure

Ignited Nepal's Entity Trust Building service for Japan constructs a bilingual entity infrastructure — Japanese and English — that positions your organisation as a recognised, structured entity across every authoritative source that AI engines operating in the Japanese market consult.

Japanese-specific entity gap audit

The work begins with a Japanese-specific entity gap audit that examines your current representation in Japanese Wikipedia, Wikidata (Japanese-language attributes), Google Knowledge Graph (Japan region), Google Business Profile (Japanese), J-CAST, Nikkei company profiles, Teikoku Databank, Tokyo Shoko Research, and 20+ additional authoritative directories relevant to your sector. We then design a bilingual entity architecture and execute the build in full.

Deliverables

- Japanese Wikipedia (ja.wikipedia.org) article creation or substantive improvement, written to Japanese Wikipedia editorial standards with citations to Nikkei, J-CAST, Teikoku Databank, and other sources recognised by the Japanese Wikipedia editorial community - Wikidata entity enrichment with complete Japanese-language attributes: Japanese label (ラベル), Japanese description (説明), Japanese aliases (別名), plus all structured data fields relevant to your organisation — industry classification, founding date, headquarters, official website in Japanese, key executives, and cross-references to Japanese business registration identifiers - English Wikipedia article creation or improvement (where not already complete) to ensure your entity record is coherent and consistent across both language versions - Google Knowledge Graph structured data submission and Knowledge Panel optimisation for both Japanese and English query contexts, including correct business category assignment for the Japanese market - Directory citation campaign across 20+ authoritative sources including J-CAST company profiles, Nikkei company database, Teikoku Databank (where recordable), Tokyo Shoko Research (where recordable), Bing Places Japan, Apple Maps Japan, and sector-specific industry association directories - Bilingual citation consistency audit ensuring your company name in Japanese (both kanji and romaji where applicable), registered address, founding year, and business description are uniform across all citations

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What changes

WHAT CHANGES

Before
After
Before AI systems operating in Japanese accurately describe your organisation.
After When a Japanese-language AI assistant — whether embedded in a search engine, a procurement platform, or an enterprise research tool — encounters a query about your sector, your company appears as a structured entity with verified Japanese-language attributes. That is the difference between being included in an AI-generated vendor shortlist and being absent from it entirely.
Before Enterprise procurement research returns accurate, detailed results.
After Japanese enterprise clients at the evaluation stage of a procurement process expect to find credible third-party records about your organisation. When your entity record is present and accurate in Teikoku Databank, reflected in Nikkei company profiles, and correctly represented in Japanese Wikipedia, the procurement team conducting due diligence receives the same signal they receive from any well-established Japanese supplier: this company is real, verifiable, and institutionally credible.
Before Your bilingual Knowledge Panel becomes a high-trust entry point.
After A Knowledge Panel that correctly displays your organisation's Japanese name, Japanese registered address, Japanese-language description, founding information, and official Japanese-language website is a strong institutional credibility signal — particularly for foreign companies operating in Japan, where the presence of accurate Japanese-language information communicates genuine market commitment rather than a superficial internationalisation effort.
Before You compound entity trust in both language markets simultaneously.
After As AI engines update their training data and knowledge graphs, organisations with coherent bilingual entity records accumulate trust signals in both Japanese and English contexts. The infrastructure built today continues to generate visibility advantages for years — and the gap between organisations that have built it and those that have not will widen as AI adoption in enterprise procurement accelerates.
Common questions

FAQ

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.

Start here

日本市場でAIに信頼されるブランドになる。今がその基盤を築く時です。

Japanese-language AI adoption in enterprise procurement is accelerating. The organisations building coherent bilingual entity records now are the ones that will appear in AI-generated vendor shortlists six months from now. Those that delay are ceding that ground to competitors who are already building it.

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