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AI VISIBILITY ENGINEERING · AIの可視性エンジニアリング
Google Workspaceが広く普及した日本のエンタープライズ環境で、Geminiは購買担当者のベンダー調査ツールになっています。GeminiはGoogle独自のエンティティグラフから情報を引用するため、Knowledgeグラフ上でのエンティティ権威性が引用率を左右します。Ignited Nepalは、日本語対応が強いGeminiにあなたのブランドが確実に引用されるよう、エンティティ最適化からコンテンツ構造化まで体系的に取り組みます。
Japanese enterprise teams use Google Gemini through Google Workspace for vendor research. Gemini has strong Japanese-language support, making it a significant channel for B2B visibility in Japan. Ignited Nepal audits your Google entity authority, corrects your Knowledge Panel, and restructures your content so Gemini cites your brand in Japanese-language queries — not just your international competitors.
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/ This is for you if
Japanese enterprise brand on Google Workspace — Your organisation uses Google Workspace. Your procurement and vendor evaluation teams increasingly ask Gemini questions in Japanese — about market options, vendor comparisons, and product capabilities. You are not confident your brand appears in those responses.
Visible on Google Search, absent from Gemini — Your site ranks on Google Search in Japan. But when buyers use Gemini to research your category in Japanese, Gemini cites international competitors rather than your brand. The gap is not content quality — it is entity structure and LLMO readiness.
Marketing director focused on AIO and LLMO — You understand that AI Overviews (AIO) and large language model optimisation (LLMO) require structured entity signals, not just SEO rankings. You want a specialist to audit your Google entity data and build the authority layer Gemini draws from.
SaaS company targeting Japanese enterprise buyers — Japanese enterprise procurement is thorough and research-intensive. Buyers compare vendors carefully. You want your brand to appear — correctly described, in Japanese — when those comparisons happen inside Gemini.
/ What's broken
GeminiはGoogleのエンティティグラフから直接引用します。Knowledge Panelに誤りや欠落があると、それがGeminiの回答にそのまま反映されます。カテゴリの誤分類や古い説明文は、日本語クエリへの回答にも影響します。 Gemini draws directly from Google's entity graph. If your Knowledge Panel is unclaimed or carries errors, those errors appear in Gemini responses — including Japanese-language responses, where Gemini's strong localisation means the inaccuracies surface clearly.
Many Japanese brands have strong domestic recognition but weak structured entity data in Google's global systems. If Google's entity graph holds limited information about your brand — thin category data, few entity relationships, no structured attributes — Gemini defaults to competitors that have invested in entity presence.
コンテンツがマーケティングコピーとして書かれている場合、Geminiはそれを引用しにくい。直接的な回答形式、FAQ構造、スキーママークアップを持つコンテンツが優先されます。 Content written as marketing copy — without direct-answer structure, FAQ sections, or schema markup — is consistently passed over by Gemini in favour of sources that answer the question explicitly. This is especially relevant for Japanese B2B content where structured knowledge articles are the preferred citation source.
Gemini is embedded in Gmail, Docs, and Meet through Google Workspace. When Japanese enterprise buyers ask Gemini vendor questions in that environment, brands without entity authority in Google's systems are not included. The Workspace integration makes Gemini a critical B2B research channel in Japan.
/ What we engineer
Each workstream is built by the team that audits it, and wired into the rest of your growth system.
We run 20 structured queries across your category in both Japanese and English — vendor comparisons, category definitions, feature queries — and record citation rates, cited sources, and gaps. You receive a baseline for both language contexts.
We claim, audit, and correct your Knowledge Panel in Google's systems: category, description, founding data, key attributes, and entity connections relevant to the Japanese market. Accurate KP data feeds directly into Gemini's citation behaviour.
We build the structured signals Google's entity graph uses: publisher profiles, third-party entity references, structured data markup, and category-aligned entity relationships. This is the LLMO foundation layer Gemini queries against for both Japanese and English responses.
We restructure priority pages with direct-answer introductions, FAQ sections, and schema markup — in both Japanese and English where required. Content is rebuilt so Gemini can extract a clean, citable answer in either language context.
Each month we run a structured comparison showing how Gemini cites your brand versus key competitors across the same Japanese and English query sets. You see where you are gaining ground and where citation gaps remain.
Citation rate changes across both language sets, Knowledge Panel status, entity accuracy scores, and content performance — documented each month with specific numbers.
/ What changes
/ Common questions
Straight answers on scope, timelines and cost. Anything else, ask us directly.
Ask us directlyGemini has strong Japanese-language support, which makes it a significant channel for B2B brand visibility in Japan. This means Japanese enterprise buyers asking vendor research questions in Japanese through Google Workspace will receive Gemini responses that are linguistically accurate — and brands with strong entity signals in Google's graph will be cited in those Japanese-language responses.
Gemini visibility is the rate at which Google Gemini cites your brand when buyers ask relevant questions. For Japanese brands, it matters because Gemini is embedded in Google Workspace — which is widely used across Japanese enterprise — and because Gemini's strong Japanese-language capability means buyers are increasingly researching vendors through Gemini in Japanese.
LLMO (large language model optimisation) is the practice of structuring content and entity signals so that AI systems like Gemini can accurately find, understand, and cite your brand. It extends traditional SEO into the entity layer — Knowledge Panels, structured data, third-party entity references — that Gemini draws from when generating responses.
Google Search and Gemini use different mechanisms. Search ranks pages. Gemini draws on Google's entity graph and prefers structured, direct-answer content. A brand ranking well in Japanese search results can still have thin entity data in Google's systems — and thin entity data means low Gemini citation rates.
Your Knowledge Panel reflects the entity data Google holds about your organisation. Because Gemini draws from this same entity graph, inaccuracies or gaps in your KP — wrong category, outdated description, missing attributes — appear in Gemini responses across all languages, including Japanese.
We run 20 structured queries in both Japanese and English: vendor comparison queries, category definition queries, feature and use-case queries, and brand-specific queries. You receive the full query set, results, citation rates, and source patterns across both language contexts.
Knowledge Panel corrections and content restructure typically produce measurable changes within four to eight weeks. Entity authority building operates on a one-to-three-month timeline. Both tracks run concurrently from the start of the engagement.
Engagements for Japanese brands — including bilingual query auditing and entity work across Japanese and English — typically start from ¥180,000 per month, depending on scope. We provide a fixed-scope proposal following the initial audit.
Start here
Every engagement begins with a structured audit — 20 queries in Japanese and English, your Knowledge Panel status, your entity data, and a clear map of where your brand appears in Gemini responses and where it does not. Gemini's strong Japanese-language support means this is no longer a future consideration for Japanese B2B brands on Google Workspace. The brands appearing in those responses now are the ones that have built the entity signals Gemini draws on. The audit tells you exactly where you stand.
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