Different sectors. Different proof structures.

Buying logic

Buyers in financial services treat AI answers as due-diligence shortlists. Because these are Your-Money-Your-Life queries, engines apply their strictest trust filters before naming any institution.

What AI engines look for

Licences and regulator references (HKMA, IA, SFC), named qualified authors, consistent entity data across official registers, and zero contradictory claims anywhere on the public web.

The Mercury playbook

• Entity architecture anchored to regulatory registrations — licence numbers, regulated activities and corporate structure encoded in Organization/FinancialService schema
• E-E-A-T author graphs: every advisory page attributed to a named, credentialed professional with a persistent profile entity
• Compliance-safe content templates reviewed against PDPO, GDPR and EU AI Act disclosure expectations before publication

Buying logic

UHNW and institutional buyers ask AI assistants who is credible before they ever take a meeting. Engines answer by tracing expert citation paths — who publishes, who quotes them, which institutions vouch for them.

What AI engines look for

Named experts with verifiable credentials, citations in tier-1 financial media, consistent firm entities across jurisdictions, and methodology content that demonstrates process rather than claiming results.

The Mercury playbook

• Expert entity graphs linking partners to publications, speaking engagements and regulatory records
• Institutional-authority schema: sameAs networks across official registers, associations and verified profiles
• Methodology-first content re-architecture so engines can quote your process, not just your claims

Buying logic

Shoppers now ask AI assistants to shortlist and compare before they browse. Engines recommend products they can describe precisely — price, availability, reviews, differentiators — from structured sources.

What AI engines look for

Complete Product/Offer schema, review corpus with AggregateRating markup, comparison-ready content (vs. tables, "best for" framing), and a consistent product entity graph across marketplaces and your own site.

The Mercury playbook

• Product schema matrices: full Product, Offer, AggregateRating and FAQ coverage across the catalogue, not just hero SKUs
• Comparison-ready content blocks engineered for extraction — engines quote what they can parse in one pass
• GXO Engine tie-in: Mercury’s GXO layer keeps product feeds and schema synchronized so AI answers never cite stale pricing

Buying logic

Buyers and guests ask location-first questions — "which development near West Kowloon", "best ryokan-style hotel in Kyoto". Engines answer from local entity graphs and the review corpus, not from brand sites alone.

What AI engines look for

LocalBusiness/Residence schema tied to map and registry entities, a dense consistent review corpus across platforms, neighbourhood-level content, and direct-booking data that engines can verify.

The Mercury playbook

• Local entity graphs connecting each property to its district, transit anchors and landmark neighbours
• Review corpus engineering: structured review capture and markup across Google, Trip.com and regional platforms
• Direct-booking citation paths that displace OTA listings inside AI answers

Buying logic

Enterprise buyers use AI assistants to build vendor longlists before RFPs. Engines surface vendors whose capability, compliance and integration data is machine-legible — procurement language, not marketing language.

What AI engines look for

Capability pages written in the vocabulary of tenders, certification metadata (ISO, SOC 2), integration and partner-tier documentation, and case studies with extractable numbers.

The Mercury playbook

• Procurement-language content re-architecture: capability statements structured the way RFPs and AI parsers read
• Supply-chain and partner metadata encoded in schema — partner tiers, certifications, delivery coverage
• Case-study atomization so each engagement yields extractable, citable outcome claims

Buying logic

Cross-border buyers query in their own language and expect locally grounded answers. A brand cited in English answers is routinely invisible in 繁中 or Japanese ones — citation equity does not transfer automatically.

What AI engines look for

Language-parallel entity data, hreflang-consistent content pairs, locally authoritative citation sources per market, and Japanese-language nuance (honorific register, katakana brand forms, local platform signals).

The Mercury playbook

• Bilingual 繁中/EN entity architecture with mirrored schema, not machine-translated pages
• Japanese semantic layer: brand-name variants, register-appropriate content and local citation sources
• Per-market prompt panels so HK, GBA, Japan and SEA visibility is measured and improved separately