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AI VISIBILITY ENGINEERING · AIの可視性エンジニアリング
Japanese content writing excels at depth, thoroughness, and formal authority. These qualities are exactly what causes AI engines to skip it. AEO and LLMO (Large Language Model Optimisation) require a structural shift: the answer first, context second. We build that system for Japanese businesses operating domestically and internationally.
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/ This is for you if
The Japanese firm ranking on Google, absent from AI answers — Your site performs well in traditional search. Query your category in ChatGPT or Perplexity — with LLMO optimisation becoming the new competitive standard — and foreign competitors appear while your brand does not. The gap is structural, not a quality deficit.
The content team using formal Japanese writing conventions — Japanese corporate communication values comprehensive context before conclusions. AIO and LLMO citation engines operate on the inverse principle: the conclusion must come first. Understanding this shift — and implementing it without sacrificing editorial quality — is the core AEO challenge for Japanese content teams.
The B2B founder who found competitors in ChatGPT searches — You searched your service category in ChatGPT Japan and watched international competitors dominate the responses. Their content is not more thorough than yours. It is structured differently — direct answers, scannable paragraphs, FAQ schema. That structural gap is what LLMO strategy closes.
The marketing director whose content investment is not generating AI citations — Your team produces high-quality, well-researched content in Japanese. None of it appears in AI engine answers. The problem is not volume or depth — it is the absence of answer-first structure and FAQPage/HowTo schema that AIO and LLMO systems depend on to identify citable content.
/ What's broken
Traditional Japanese content writing — rooted in academic and journalistic convention — frames context before delivering conclusions. AEO and LLMO systems extract from the first one to two sentences of a section. If the opening provides background rather than a direct claim, the AI finds nothing to cite and moves on.
Japanese corporate websites have very low schema adoption rates. ChatGPT, Gemini, Perplexity, and Google AIO all weight structured Q&A markup when identifying quotable content. FAQPage and HowTo JSON-LD tell AI engines exactly where the answers are. Without schema, your content is structurally invisible regardless of its quality.
Japanese business content typically targets educational or informational intent. LLMO and AEO optimisation requires targeting the specific queries buyers ask AI engines when making purchasing decisions: "どのAEO会社を選ぶべきか" (which AEO company should I choose), "AEO戦略の費用はいくらか" (how much does AEO strategy cost). These queries need dedicated, direct-answer pages.
Comprehensive coverage is a strength. For AIO and LLMO citation, it is also a liability when answers are distributed across long paragraphs. AI engines cannot extract a claim buried in sentence eight of a 200-word paragraph. The fix is not to reduce depth — it is to restructure depth so each section opens with its conclusion.
/ What we engineer
Each workstream is built by the team that audits it, and wired into the rest of your growth system.
We score your existing pages against 14 AI citability criteria calibrated for Japanese content: answer position, paragraph structure, schema presence, and LLMO/AIO alignment. You receive a prioritised report with a citation-potential score for each page and a restructuring roadmap.
We build a 100+ question inventory by querying ChatGPT, Perplexity, and Google AIO directly in Japanese — not keyword tools. Every question is classified by buyer intent, mapped to an existing page or content gap, and annotated with LLMO citation priority. This inventory is the foundation of your AEO content system.
We rewrite your top 10 pages in answer-first format. The first sentence of every section states the direct claim. Context, evidence, and nuance follow. The restructure preserves the depth and authority of your original content while making it extractable by AIO and LLMO systems.
Every restructured page receives FAQPage and HowTo JSON-LD. Schema is written, validated, and delivered ready for developer implementation. Japanese-language FAQ entries are structured to maximise LLMO citation probability in both Japanese and English AI engine responses.
We build a repeatable monthly brief template for new AEO-ready content in Japanese. Each brief includes the target question, required answer structure, schema template, and an LLMO/AIO citability checklist. Your team produces citation-optimised content independently after handover.
We track 20 target queries monthly across 12 AI engines — including Japanese-language queries in ChatGPT, Gemini, and Perplexity. Monthly reports show citation rate, LLMO position, and competitor share for both Japanese and English query sets.
/ What changes
/ Common questions
Straight answers on scope, timelines and cost. Anything else, ask us directly.
Ask us directlyAEO is the practice of restructuring content so AI engines can extract and cite it in direct-answer responses. LLMO — Large Language Model Optimisation — is the broader Japanese-market term covering the same discipline applied specifically to LLM-based systems like ChatGPT, Gemini, and Perplexity. Both terms describe the same structural requirement: answer-first content, FAQPage schema, and buyer-decision query targeting.
Traditional Japanese content writing places context and background before conclusions — a convention rooted in academic and journalistic norms. AIO and LLMO citation systems extract claims from the first one to two sentences of a section. Content that opens with context before delivering the answer is systematically skipped by AI citation engines regardless of its depth or quality.
Yes. Question mapping, page restructuring, and FAQ schema entries are produced in Japanese for domestic AI engine queries and in English for international AIO responses where applicable. The programme covers both language contexts.
Pages restructured in answer-first format and marked with FAQPage schema typically appear in AI engine responses within 30 to 60 days. LLMO citation rate across 20 tracked queries becomes measurable within 90 days.
No. The restructure modifies content within existing pages — rewriting section openings and shortening paragraphs. Schema is added via JSON-LD and requires no architectural site changes.
⚠️ OWNER TO FILL — prices in JPY. Suggested framing: "The AEO / LLMO strategy programme starts at ¥[X] for the audit and restructure phase. Monthly tracking is available from ¥[X] per month."
The programme monitors ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, Bing Copilot, and six additional AI-powered surfaces in both Japanese and English. Citation tracking covers 12 engines in total.
Yes. The AEO content brief template is adapted for Japanese-language content production. It includes the target question in Japanese, required answer structure, schema template, and an LLMO/AIO citability checklist. Most teams produce AEO-ready content independently after one handover session.
Start here
Your content depth and authority are assets. The structural gap — answers arriving too late, no schema, wrong question targeting — is what prevents AI engines from citing you. We identify which pages to restructure, rewrite the top ten in answer-first format, implement LLMO-aligned schema, and track AIO citation rate every month. Five-day audit. No retainer required to begin.
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