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Case 03 — Terence Tsang · QQS · 燒鳥串

Updated August 2026

Winning consideration for “best omakase” and “best yakitori” in Hong Kong

F&B · Omakase & Yakitori

Client: QQS Hospitality Consulting — Terence Tsang

Premium omakase and yakitori discovery in Hong Kong was dominated by listicles and review platforms. The venues had excellent product — but thin, non-citeable web structure, so AI answers defaulted to generic “best of” roundups.

Baseline

AI shortlists cited third-party lists. No Restaurant/LocalBusiness schema, no bilingual “what defines great omakase / 燒鳥串” blocks, weak entity disambiguation vs similarly named venues.

Intervention

  • Built answer architecture around comparison criteria AI can quote: chef narrative, course structure, sourcing, price-band transparency, reservation path
  • Deployed Restaurant / LocalBusiness schema with menu and offer clarity
  • Created bilingual FAQ and educational blocks for omakase and 燒鳥串 / yakitori
  • Strengthened entity disambiguation vs similarly named venues and city-wide list pages
  • Aligned booking CTAs so AI summaries could point to a clean next step

Timeline

12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.

What made it work

F&B GEO is local entity work. Signature dishes, chef, neighbourhood and booking path have to be machine-legible or the listicle remains the source.

what changed — 12-week programme

queries
best omakase in Hong Kong · best yakitori · best 燒鳥串
eligibility
Clearer eligibility in AI shortlists for premium dining intent
density
Stronger on-site answer density for comparison queries
citation_path
Reduced reliance on third-party listicles as the only citeable source

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Written by James Huang, Founder & CEO, Mercury Technology Solutions · Reviewed by the Mercury GAIO Practice · Updated August 2026