MethodologyMethodology
How we measure AI visibilityHow we measure AI visibility
We score a website’s AI visibility from 0 to 100 across four axes. The formula is open and we welcome peer review.We score a website’s AI visibility from 0 to 100 across four axes. The formula is open and we welcome peer review.
Version 1.2 · Last updated · Next review: 2026-10-17 · methodology@shortcutsistem.com
How the 4 axes map to the 7 user-facing dimensions
The Omni Audit reports 7 dimensions (visibility, security, speed, trust, business clarity, conversion, AI Search Readiness) so business owners can see at a glance where to focus. The 4 axes on this page feed those dimensions. Each axis contributes to one or more dimensions:
| Axis | Points | Feeds dimensions |
|---|---|---|
| Crawler access | 40 | Visibility, AI Search Readiness |
| Structured data | 25 | Visibility, Trust, AI Search Readiness |
| Entity consistency | 20 | Trust, Business Clarity |
| Freshness signals | 15 | Small boost to every dimension |
The remaining dimensions (Security, Speed, Conversion) use separate, hard checks that run alongside the AI visibility formula: security headers and OWASP, Core Web Vitals, and CTA presence. We keep them out of the 4-axis AI visibility score because they measure something different, but we include them in the Omni Audit report because business owners care about them.
The 4 axes
1. Crawler access (40 points)
For each of 7 known AI crawlers, we check robots.txt. Allow = points, Disallow = 0.
| Crawler | Points |
|---|---|
| GPTBot | 7 |
| ChatGPT-User | 6 |
| ClaudeBot | 7 |
| Claude-User | 6 |
| Google-Extended | 4 |
| CCBot | 4 |
2. Structured data (25 points)
We parse schema.org JSON-LD on the homepage and up to 3 product/inner pages.
| Check | Points |
|---|---|
| Organization schema present | 5 |
| sameAs links to ≥2 third-party profiles | 5 |
| Product / Article / FAQPage schema on relevant pages | 5 |
| BreadcrumbList | 3 |
| Valid JSON-LD (passes Schema.org validator) | 4 |
| llms.txt file present at root | 3 |
3. Entity consistency (20 points)
LLMs trust entities that look the same across the open web. We check:
| Check | Points |
|---|---|
| Consistent NAP across website + 2 third-party sites | 7 |
| Wikipedia or Wikidata entry (if eligible) | 5 |
| About page with author/reviewer names and roles | 4 |
| Public contact email with matching domain | 4 |
4. Freshness signals (15 points)
LLMs deprioritize stale content. We check:
| Check | Points |
|---|---|
| Sitemap.xml present and updated within 30 days | 5 |
| ≥1 content update within 90 days | 5 |
| Visible “last updated” timestamps on key pages | 3 |
| GitHub-style changelog or release notes | 2 |
What this does not measure
- How often a site is actually cited by ChatGPT or Claude. We sample, not enumerate.
- Content quality or factual accuracy. That is a different scoring system.
- Page speed or Core Web Vitals. (Crawl budget matters less for AI than for Google.)
- Backlinks. Backlinks help SEO; they help AI less than primary data does.
Peer review
Our methodology is open to independent review. Two reviewers we are looking for:
- An independent AI or ML researcher willing to challenge the 4-axis formula.
- An Indonesian university partner for local-context review.
Disagree with the formula? Email us. Public corrections will be credited in the next quarterly update.
Version history
| Version | Date | Change |
|---|---|---|
| v1.2 | 2026-07-17 | Added llms.txt to structured-data axis. |
| v1.1 | 2026-04-12 | Adjusted entity-consistency weights after Q1 2026 calibration. |
| v1.0 | 2026-01-30 | Initial release. |
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See how your site scores across the 4 axes and 7 dimensions.See how your site scores across the 4 axes and 7 dimensions.