Status bukti: halaman ini memakai sumber publik yang dapat dicek. Ini bukan klaim confidential client, bukan klaim hasil proyek Undercover, dan bukan klaim performa AI live kecuali ada prompt log yang ditempelkan secara eksplisit. Fungsinya adalah menjadi evidence-ready page untuk membaca pola entity, trust signal, schema readiness, dan risiko AI visibility pada sektor terkait.
Ringkasan
Schema coverage repair log ini dipakai untuk mencatat halaman mana yang sudah diberi struktur data, tipe schema apa yang digunakan, dan risiko apa yang dikurangi. Halaman ini bukan laporan hasil ranking. Fungsinya adalah governance trail agar perbaikan schema bisa dicek, diulang, dan diaudit.
Halaman yang menjadi prioritas patch
- Homepage: Organization, WebSite, WebPage, Service, FAQPage, BreadcrumbList.
- About: Organization, WebPage, BreadcrumbList.
- Legalitas: Organization, WebPage, credential references.
- Team: Person, Organization, WebPage.
- Portfolio dan case study: Article atau CreativeWork, evidence relation.
- Service pages: Service, Offer, FAQPage, BreadcrumbList.
- Evidence pages: Report, Article, Dataset where appropriate.
Cleanup rule
Structured data harus faktual. AggregateRating, Review, award, client claim, revenue claim, dan outcome claim hanya boleh dipasang jika bukti publiknya tampil di halaman atau dapat diverifikasi. Karena itu homepage schema sebaiknya tidak memakai rating dan self-review yang tidak didukung bukti kuat.
Validation process
- Cek source HTML untuk memastikan script application/ld+json keluar di frontend.
- Test dengan Rich Results Test untuk schema yang eligible di Google.
- Test dengan Schema Markup Validator untuk validasi schema.org umum.
- Audit ulang duplicate schema dari theme, SEO plugin, dan custom meta output.
Internal routing
- AI Visibility Audit
- AI Visibility Monitoring
- AI Visibility Measurement Framework
- Entity Graph Construction Process
- Request AI Visibility Snapshot
Schema Coverage Reference Set
Schema repair should match industry type, entity relationship, offer model, audience, location, and proof assets. These are public reference examples, not Undercover.co.id client claims.
| # | Industry | Public reference entity | AI visibility angle | Signals to evaluate | Public source |
|---|---|---|---|---|---|
| 1 | Technology & SaaS | Mekari | SaaS brands need clear product taxonomy, solution pages, integration signals, pricing context, customer evidence, and use-case level schema so AI systems can distinguish software category, buyer segment, and implementation fit. | Product suite clarity across HR, accounting, CRM, and business operations; Integration and implementation context for enterprise buyers | Mekari official website |
| 2 | Real Estate & Property | Ciputra Development | Property brands need project-level entity pages, location boundaries, unit/specification context, developer credibility, and legal or availability information because AI property queries evaluate trust before lead submission. | Developer, project, location, unit type, and facility hierarchy; Legal status and availability clarity | Ciputra Development official website |
| 3 | Healthcare & Medical | Siloam Hospitals | Healthcare providers need strict entity clarity, facility-level pages, doctor/service taxonomy, medical disclaimer boundaries, and verified contact/location data because AI answers in healthcare have higher trust and safety sensitivity. | Hospital, clinic, doctor, department, and treatment taxonomy; Location and emergency contact clarity | Siloam Hospitals official website |
| 4 | B2B Professional Services | PwC Indonesia | B2B professional services firms need service-line taxonomy, industry coverage, thought leadership, partner expertise, and proof assets because AI recommendations often shortlist firms based on category authority and evidence depth. | Service line, industry, and role-based solution pages; Partner/expert profile schema and authority signals | PwC Indonesia official website |