What exactly is AiVIS Cite Ledger?

AiVIS Cite Ledger is an evidence-backed AI citation audit and remediation system. It measures how AI models, ChatGPT, Perplexity, Claude, and Google AI, interpret, trust, and cite your web content. The result is a 0–100 CITE LEDGER score across seven weighted dimensions, with BRAG evidence-linked findings and prioritized fixes.

How is this different from Semrush, Ahrefs, or Clearscope?

Traditional SEO tools optimize for keyword ranking in traditional search results. AiVIS Cite Ledger audits citation readiness, the specific signals that determine whether AI answer engines retrieve and cite your content. A page can rank #1 on Google and be completely invisible to AI answer synthesis.

What is BRAG and how does it make findings trustworthy?

BRAG stands for Based-Retrieval-Auditable-Grading. It ties every audit finding to a real, traceable element on your page, a specific heading, schema block, meta tag, or content section, each carrying a unique evidence ID. You can open the element, verify the finding, and recheck it after a fix. No black boxes.

Is the free tier genuinely useful, or just a teaser?

The Observer (free) plan gives you a full composite CITE LEDGER score and all seven dimension letter grades, so you can see exactly where you stand and which categories are hurting you. BRAG evidence IDs and prioritized fix packs require a paid plan. Three audits per month, no credit card.

Gatsby AI Visibility Audit | AiVIS Cite Ledger

Gatsby's static site generation gives you a head start, every page is pre-rendered HTML. But default Gatsby projects ship with zero structured data for AI models.

Gatsby's SSG Advantage for AI

Gatsby builds static HTML at compile time. Every page is a fully rendered document that AI crawlers can parse immediately without JavaScript execution.

This gives Gatsby a structural advantage over client-rendered React apps, but the advantage only matters if you add the right machine-readable signals.

What Gatsby Misses for AI Models

No built-in JSON-LD schema generation. Gatsby's SEO ecosystem relies on gatsby-plugin-react-helmet for basic meta tags, but structured data requires manual implementation.

GraphQL-driven pages often dynamically generate slugs and content but use the same meta description template, creating duplicate signals across hundreds of pages.

Many Gatsby starters don't include robots.txt or sitemap plugins, leaving AI crawlers without a starting point for discovery.

Fixing Gatsby for AI Engines

Install gatsby-plugin-sitemap and gatsby-plugin-robots-txt. Configure robots.txt to explicitly allow AI crawler user-agents.

Add a reusable SEO component that injects page-specific JSON-LD (Article, FAQ, Organization) into the document head using React Helmet or Gatsby Head API.

Audit your site with AiVIS Cite Ledger to validate that static output contains all required signals, what your dev server renders may differ from the production build.

Frequently Asked Questions

Is Gatsby good for AI visibility?: Gatsby's static HTML output is a strong foundation. However, you must add JSON-LD schema, AI crawler rules, and unique meta data manually, none of it is included by default.
What Gatsby plugins help with AI visibility?: gatsby-plugin-sitemap, gatsby-plugin-robots-txt, and a custom SEO component for JSON-LD are the essentials. Consider gatsby-plugin-canonical-urls for canonical tag management.
How does Gatsby compare to Next.js for AI visibility?: Both can achieve strong AI visibility. Gatsby excels for static content sites; Next.js offers more flexibility with SSR for dynamic content. The key difference is deployment complexity, not AI readiness.