ENTITY & SCHEMA AUDIT · エンティティ・スキーマ監査

AI エンジンは、あなたのビジネスをどう認識しているか。

ChatGPT、Gemini、Perplexity — これらのAIエンジンは、ウェブページを読んで回答を生成しているわけではありません。構造化された知識データ、すなわちエンティティレコード、ウィキペディアの記事、ナレッジグラフ、スキーママークアップを参照し、ビジネスが何であるか、信頼に値するかを判断しています。エンティティデータが不完全であれば、AIツールはあなたのビジネスを引用しない — あるいは誤った情報を出力します。

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This is for you if

WHO THIS IS FOR

Established Japanese enterprises seeking AI citation. Your business has a verified corporate record, a Teikoku Databank entry, decades of operational history, and substantial press coverage in Nikkei and industry trade publications — yet AI assistants either omit you from generated answers or produce descriptions that are outdated and inaccurate. The structured entity data that should establish your authority is fragmented across sources that AI engines are failing to reconcile.

Foreign companies entering the Japanese market. Establishing a credible entity record in Japan requires presence across Japanese-specific data sources that international businesses typically overlook: Teikoku Databank, Tokyo Shoko Research, Japanese Wikipedia, and Japan-specific business directories. Without these, AI engines treat your Japan operations as an entity of unknown authority regardless of how strong your global presence is.

Professional services and financial institutions. Legal, accounting, consulting, and financial services firms operate in sectors where Japan-specific regulatory and professional body registrations carry determinative weight with AI engines assessing credibility. Missing or incomplete registrations in these directories create authority gaps that cannot be compensated for by website content alone.

B2B technology companies targeting enterprise procurement. Large Japanese corporations and government-affiliated organisations use AI research tools in supplier qualification processes. If your entity record does not meet the completeness and consistency standards that AI engines require to confidently cite a supplier, your business is filtered out before procurement teams evaluate your actual capabilities.

What's broken

WHAT'S BROKEN

Japanese and English entity records are disconnected.

Your business may have a strong entity presence in English-language sources while Japanese-language sources — Japanese Wikipedia, Teikoku Databank, Tokyo Shoko Research, Japanese industry directories — either lack entries entirely or contain different, outdated, or internally inconsistent information. AI engines serving Japanese-language queries draw primarily from Japanese-language sources. The two entity records need to be coherent and mutually reinforcing, and almost never are.

Schema markup was implemented for Google, not for AI.

Most Japanese enterprise websites have some form of structured data, typically added when Google introduced rich results requirements. That markup was designed to satisfy 2018-era Google guidelines. It was not designed for AI comprehension, does not reflect current Schema.org specifications, and has not been updated as the business has evolved. The result is a structured data layer that is technically present but functionally misleading.

Wikidata entries are absent or minimally populated.

Wikidata is the machine-readable backbone of the knowledge graph that every major AI engine references. For Japanese enterprises, Wikidata entries are frequently either absent or contain only a handful of basic properties — no industry classification, no parent-subsidiary relationships, no geographic data, no founding history. This creates a structural data gap that reduces AI confidence in any citation involving your business.

Corporate group structure is invisible to AI.

Japanese enterprises frequently operate through complex keiretsu and subsidiary structures. If those relationships are not encoded in structured data — parent organisation links, subsidiary declarations, affiliated entity references — AI engines cannot understand your corporate context and may conflate your entities, misdescribe your operations, or cite your subsidiaries in contexts that should reference the parent organisation.

What we engineer

WHAT WE DO

25-source bilingual entity map

a structured inventory of your business entity across Wikipedia (Japanese and English editions), Wikidata, Google Knowledge Graph, Google Business Profile, Teikoku Databank, Tokyo Shoko Research, Crunchbase, LinkedIn Japan, Nikkei company database, industry trade association directories (Keidanren, sector-specific associations), Japan Chamber of Commerce and Industry listings, Ministry of Justice corporate registry data, financial disclosure databases, Bing Japan, Apple Maps Japan, press archive databases including Nikkei Telecom and Kyodo News, and additional sector-specific sources identified during scoping.

Entity data quality scorecard

each source scored for completeness, accuracy, and consistency, with special attention to bilingual coherence: do the Japanese and English entity records agree on all material facts.

Full schema markup crawl

extraction of every structured data block on your domain, with analysis of both the content accuracy and the bilingual coverage of markup (are Japanese-language pages marked up as completely as English-language pages).

Schema validation and error report

each markup block tested against Schema.org and Google requirements; errors ranked by AI visibility impact.

Competitive entity benchmark

your entity coverage compared against three direct competitors in the Japan market, showing where gaps exist relative to businesses already appearing in AI-generated answers.

Priority fix plan

entity gaps and schema errors consolidated into a sequenced action list ranked by AI visibility impact, with specific instructions for Japanese-source remediation and bilingual schema improvements.

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What changes

WHAT CHANGES

Before
After
Before AI engines begin citing your business accurately in both Japanese and English.
After When your entity record is consistent across Japanese and English sources and your schema markup correctly describes your organisation in both languages, AI tools serving queries in either language have what they need to include you in generated answers. The authority signal from coherent bilingual entity data is substantial.
Before Your corporate identity becomes legible to AI systems.
After Subsidiaries, joint ventures, affiliated entities, and parent company relationships become visible in the knowledge graph. AI engines stop misidentifying your corporate structure and begin presenting your organisation with the clarity and completeness it warrants.
Before Enterprise procurement AI tools include you in supplier shortlists.
After Japanese and multinational corporations using AI tools for supplier research will encounter an entity record that is complete, consistent, and well-defined — rather than an entity with uncertain authority that the AI tool declines to surface.
Before Schema markup begins driving rich results across both Google Japan and international search.
After The structured data improvements made for AI visibility have direct benefits for traditional search rich results, accelerating returns across the full search marketing programme.
Common questions

FAQ

エンティティとは何ですか？AI可視性においてなぜ重要なのですか。 / What is a business entity and why does it matter for AI visibility?

A business entity is the structured data record that AI engines use to identify, classify, and evaluate a business. It is not a webpage — it is an accumulated set of data points across Wikipedia, Wikidata, corporate registries, industry directories, and news archives that collectively tell AI systems what your business is and whether it can be trusted as a citation source. An incomplete or inconsistent entity record causes AI tools to either avoid citing your business or to produce inaccurate descriptions when they do.

スキーママークアップとは何ですか。 / What is schema markup?

Schema markup is structured code embedded in your website that describes your content in a machine-readable format. Rather than requiring AI engines to interpret the meaning of your page text, schema markup states directly: this is a product, this is its price, this is the organisation that makes it, this is what it does. Missing or invalid schema markup means AI engines must infer these facts — and they frequently infer incorrectly or decline to make the inference at all.

25のソースとは何ですか。 / Which 25 sources does the audit check?

The audit covers: Japanese Wikipedia, English Wikipedia, Wikidata, Google Knowledge Graph, Google Business Profile, Teikoku Databank, Tokyo Shoko Research, Crunchbase, LinkedIn, Nikkei company database, Ministry of Justice corporate registry, relevant industry federation directories, Japan Chamber of Commerce listings, financial disclosure databases, Bing Japan, Apple Maps Japan, Nikkei Telecom press archive, Kyodo News archive, PR Times, industry trade press databases, data aggregator feeds, academic citation databases, and sector-specific directories identified during scoping. The exact set is adjusted based on your industry and corporate structure.

監査にはどのくらいの時間がかかりますか。 / How long does the audit take?

The standard audit takes twelve to sixteen working days from receipt of your onboarding information. Enterprise clients with complex group structures or a large number of Japanese-specific directory sources may require additional time, which is confirmed before the audit begins.

費用はいくらですか。 / What does the audit cost?

Pricing for Japan-market Entity and Schema Audits starts at ¥350,000 for single-entity businesses. Engagements covering corporate group structures, multiple subsidiaries, or bilingual schema implementation across large site architectures are priced following a scoping call.

監査後はどうなりますか。 / What happens after the audit?

The priority fix plan is delivered as a structured action document that your internal team or a developer can implement directly. Ignited Nepal also offers a managed implementation service for clients who prefer to have every item in the fix plan executed and re-validated by our team, and a monitoring retainer that tracks entity data drift and schema changes on an ongoing basis.

Our team

The people behind the work

Not a black box. Real specialists you can call, with their names on the work.

Niraj Raut

Niraj Raut

Founder — Ecommerce SEO
Keshab Joshi

Keshab Joshi

PPC Expert
Hawrry Bhattarai

Hawrry Bhattarai

Google Ads Expert
Arogya Rijal

Arogya Rijal

SaaS SEO Expert
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Start here

AI engines are forming judgements about your business right now, using whatever entity data they can find.

For enterprises operating in Japan, where bilingual entity coherence and Japanese-source authority both matter, the gap between businesses with complete structured data records and those without is widening every month. The audit gives you a precise map of where that gap is and exactly what to do about it.

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