
Citation Architecture for AI Search: A JSON-LD Validation Workflow
A release pipeline for crawler access, answer blocks, stable entity IDs, structured data, source checks, and claim freshness.
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A release pipeline for crawler access, answer blocks, stable entity IDs, structured data, source checks, and claim freshness.
A practical fixture schema and CI workflow for detecting sustained source loss without confusing ordinary answer variance for failure.

A developer-ready schema for measuring AI citations by buyer question shape instead of one blended visibility score.

A Machine Relations guide to source quality, citation readiness, and generative AI optimization.

A technical guide to accessibility, relevance, attribution, and claim support in AI answer citations.

A technical guide to the metadata answer engines need before they can retrieve, attribute, and cite a source reliably.

The structural properties that make G2, Crunchbase, and market intelligence platforms the most-cited source category across AI answer engines

The structural patterns that determine whether AI engines cite your content or skip it

Evidence floors, confidence tiers, and multi-engine methodology that separates real measurement from randomness