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 ten weighted families (avs-v3), 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 ten family scores (avs-v3), 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.

Documentation Page Templates for AI Citation: Structures That Get Extracted | AiVIS Cite Ledger Blogs

By Founder, AiVIS Cite Ledger · 2026-06-11 · 12 min read · IMPLEMENTATION

The difference between a page that gets cited and a page that gets read is structure. These three templates hard-code the structural decisions — entity placement, answer-first sections, extractability-optimized formatting — so you are building citation-ready by default.

Key Takeaways

Three templates — Reference, How-To, Concept — cover the content types most frequently cited by frontier models.
The entity declaration in JSON-LD is the first structural invariant; without it, extracted facts get credited elsewhere.
Tables have the highest extractability ratio of any prose format — use them for any data with two or more attributes.
The "what X is not" section on concept pages dramatically increases citation accuracy by eliminating attribution to confused concepts.

Article

# Documentation Page Templates for AI Citation: Structures That Get Extracted

The strongest citation targets share a structural pattern that has nothing to do with word count or topical authority.

They define their entity early. They answer the query before elaborating. They use headings that function as standalone query responses. They contain a high density of extractable facts.

These three templates hard-code those decisions. Use them as starting points. The blanks marked with `[brackets]` are where your content goes — everything else is the structural skeleton that makes that content citable.

---

Template 1: The Reference Page

**Best for:** API documentation, parameter references, configuration guides, integration docs, rate limit tables, error code listings.

**Why it gets cited:** Reference pages have the highest extractability ratio of any content type. Every row in a table is a standalone fact. Every parameter entry is a discrete, reusable data point.

**Target score on citation rubric:** 85–95 (entity + structured data + extractability all naturally high in this format)

---

[Page Title: clear noun phrase describing the reference subject]

> **One-sentence summary:** [What this page covers and what it enables. No marketing language.]

#### Entity declaration (JSON-LD — place in `<head>`)

```json

{

"@context": "https://schema.org",

"@type": "TechArticle",

"name": "[Page title]",

"url": "https://[yourdomain.com]/[this-page]",

"description": "[One sentence matching the summary above]",

"author": {

"@type": "Organization",

"name": "[Organization name]",

"url": "https://[yourdomain.com]"

"publisher": {

"@type": "Organization",

"name": "[Organization name]",

"url": "https://[yourdomain.com]"

"datePublished": "[YYYY-MM-DD]",

"dateModified": "[YYYY-MM-DD]"

}

```

---

#### Overview

[2–3 sentences. Answer: what is this, what does it do, what does the user need to know before reading the table below.

Enable JavaScript for the full interactive reading experience with related articles and discussion.