Automated SEO Tags and Metadata Engine

Whitepapper's metadata engine automatically generates SEO metadata while keeping final author control. The engine produces consistent, extractable fields (Open Graph, Twitter Card, JSON-LD) and exposes programmatic hooks so you can customize or override values per paper.

Features

• Auto-generate seo_title and seo_description optimized for search snippets and AI extraction.
• Produce Article JSON-LD with author, datePublished, headline, and image.
• Emit Open Graph and Twitter Card tags for predictable rich previews.
• Use content-aware defaults and per-collection templates to minimize manual editing while preserving brand voice.

How it works

1. Title normalization ensures SEO titles fit snippet lengths and retain primary keywords.
2. Smart summarization creates a short description from the first paragraphs when seo_description is not provided.
3. Entity extraction identifies named entities and surfaces them as keywords and tags.
4. Readability and extraction scoring adjusts descriptions to increase the chance of being quoted by AI systems.

Templates and overrides

• Set frontmatter keys to override: seo_title, seo_description, og_image, schema_type.
• Collection-level defaults enforce consistent voice across related papers.
• Call the metadata regeneration API after major content edits to refresh tags.

Example JSON-LD

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Automated SEO Tags and Metadata Engine",
  "datePublished": "2026-04-21",
  "author": {"@type":"Person","name":"Abhraneel"},
  "image": "https://.../og.png",
  "description": "Automated SEO tags and JSON-LD for extractable, AI-friendly metadata."
}

Best practices

• Provide a short seo_description for high-priority pages; use auto-generated descriptions as a fallback.
• Include a unique data point early in the content to increase citation probability.
• Add last_updated for freshness signals to AI systems.

Last updated: 2026-04-21