Tokyo · AI Readiness Audit
Is your Tokyo restaurant found when customers ask AI?
When someone in Tokyo asks ChatGPT “best ramen in Shibuya for under ¥2,000 with an English menu”, does your restaurant come up — or your competitor? A $47 audit shows exactly what AI assistants can and can't see about your business, and how to fix it.
What stops most Tokyo restaurants from being found by AI
Most restaurant websites were built before AI-assistant discovery existed. These are the gaps we see most often — and exactly what the audit scores:
- ✗Menu lives in a PDF or an image — AI assistants can't read it
- ✗No Schema.org markup for cuisine, price range, or opening hours
- ✗Reservations locked inside a third-party platform that keeps the customer relationship
- ✗No llms.txt, so AI crawlers have no map of what matters on your site
What the $47 audit delivers
AI-readiness score (0-100)
How visible your restaurant is to ChatGPT, Claude, Gemini, and Perplexity today.
The specific gaps
Which Schema.org fields, robots.txt rules, and llms.txt entries you're missing — for a Japan restaurant.
Concrete next steps
A prioritized fix list you can hand to any developer — or have us build.
Local context
How Tokyo's platforms (Tabelog, Hot Pepper Beauty) and Japanese-first mix affect your discoverability.
Tokyo Restaurant AI-readiness — FAQ
How do Tokyo customers find a restaurant using AI assistants?
Increasingly, Tokyo customers ask ChatGPT, Claude, Gemini, or Perplexity questions like "best ramen in Shibuya for under ¥2,000 with an English menu" instead of scrolling Tabelog or Google. The AI returns a short list of specific businesses. If your restaurant isn't structured for AI to read — no Schema.org, no llms.txt, content locked in JavaScript — you're invisible in that answer regardless of how good your business is.
What does the $47 audit check for a Tokyo restaurant?
It scores your site 0-100 on AI-readiness: robots.txt rules for AI crawlers (OAI-SearchBot, Claude-SearchBot, PerplexityBot), Schema.org structured data, llms.txt presence, server-side rendering, and the content patterns proven to lift AI citation. The report is specific to your restaurant and the Japan market — including which local platforms (Tabelog, Hot Pepper Beauty, Google Maps, LINE Official Account) you're over-reliant on.
Why does the Japan market matter for AI-readiness?
Tokyo is Japanese-first, with a high-value English-speaking expat and tourist segment. AI assistants weigh language signals, local entity data, and market-specific platforms differently here than elsewhere. A generic audit misses this; ours accounts for Tokyo-specific discovery (Tabelog and Hot Pepper Beauty) and the bilingual surface that wins higher-value segments.
Want the full playbook? Read our deep-dive on how restaurants get cited by AI →
See where your Tokyo restaurant stands.
$47. 24-48 hours. 30-day money-back guarantee.
Get Your $47 Audit →