How Visipage Measures Entity Infrastructure
Transparency in how we assess entity readiness for AI citation.
Entity Completeness
What fields matter: bio, title, social links, verified sources, FAQ items, experience, education, and publications. Each field contributes to entity richness. Thin profiles with minimal data are excluded from directory listings and sitemaps to maintain quality.
Signal Consistency
How Visipage checks for conflicting information across sources. When data from different sources (LinkedIn, corporate bios, press features) contains differing dates, titles, or claims, AI confidence degrades. Visipage identifies and flags these inconsistencies.
Source Coverage
What counts as a verified source. Sources are external references that corroborate entity claims — LinkedIn profiles, company websites, press mentions, publication links. Minimum threshold: at least one verified source is required for directory eligibility.
Freshness
How recency affects entity confidence. Profiles that haven't been updated or reviewed lose signal freshness over time. Visipage tracks update cycles and encourages regular profile reviews to ensure generative engines are indexing current data.
Citation Readiness
What makes a profile citable by AI. Structured FAQs, disambiguation statements to resolve entity confusion, summary statements optimized for AI extraction, and proper semantic markers ensure models can confidently cite the provided information.
Structured Data Coverage
JSON-LD schema types emitted include Person, Organization, ProfilePage, FAQPage, BreadcrumbList, WebSite, and Speakable. Validation approach — each profile's schema is rigorously validated against Schema.org specifications to guarantee accurate crawling.
This is not a score, a ranking, or a guarantee. Entity infrastructure assessment is a framework for understanding how well your digital identity is structured for machine interpretation. It does not predict or promise placement in AI-generated responses. AI models make their own decisions based on proprietary algorithms. What we do is ensure your entity data is complete, consistent, and structurally sound — giving AI systems the best possible data to work with.