The first open standard for measuring website discoverability by AI search engines.
AI Visibility Score (AVS) measures how discoverable your website is to both traditional search engines (Google, Bing) and AI-powered search engines (ChatGPT, Perplexity, Gemini, Copilot).
AVS = SEO Score × 0.5 + AEO Score × 0.5
One number. 0-100. Grade A-F. Tells you: "Can AI find you?"
In 2026, a growing share of web traffic comes from AI search interfaces. Users ask ChatGPT "recommend a good restaurant" instead of Googling. If your website isn't optimized for AI retrieval, you're invisible to this new channel.
No standardized, free, open metric existed to measure this. Until now.
| Component | What it measures | Checks | Cost |
|---|---|---|---|
| SEO Score | Traditional search engine optimization | 30+ checks, 8 categories | $0 |
| AEO Score | AI search engine optimization | 32+ checks, 8 categories | $0 |
| AVS | Combined AI Visibility | SEO + AEO weighted average | $0 |
The entire analysis is deterministic (pure HTML parsing, no LLM calls), completes in < 50ms, and costs $0.
Web UI: ultralab.tw/probe
npm install @ultralab/scannersimport { runSeoScan, runAeoScan } from '@ultralab/scanners'
const html = await fetch('https://example.com').then(r => r.text())
const seo = runSeoScan(html, 'https://example.com')
const aeo = runAeoScan(html, 'https://example.com')
const avs = Math.round(seo.score * 0.5 + aeo.score * 0.5)
console.log(`AVS: ${avs}/100`) // AVS: 47/100| Grade | Score | Meaning |
|---|---|---|
| A | 90-100 | Highly visible to Google AND AI |
| B | 75-89 | Good SEO, some AEO gaps |
| C | 60-74 | Fair. Competitors may be preferred by AI |
| D | 45-59 | Poor. Largely invisible to AI search |
| F | 0-29 | Invisible. Immediate action required |
Full specification: AVS v1.0
Covers:
- Scoring formula and grade mapping
- SEO sub-score: 8 categories, 30+ checks
- AEO sub-score: 8 categories, 32+ checks
- Measurement protocol
- Validation study methodology
We are conducting an empirical validation study:
- 250 queries submitted to AI search engines (OpenAI web_search)
- Cited URLs scanned with AVS reference implementation
- Correlation analysis between AVS scores and AI citation behavior
Key findings (155 queries, 816 citations, 721 AVS scores):
- Median AVS of cited websites: 77 (Grade B)
- 59.8% of cited sites scored B or above
- Recommendation queries cite highest-AVS sites (mean 80.2)
- Local queries cite lowest-AVS sites (mean 60.0)
- SEO-AEO gap: mean SEO 80.6 vs mean AEO 64.5
Paper: DOI: 10.5281/zenodo.19410475
| Standard | What it measures | Scope | Open | Free |
|---|---|---|---|---|
| Lighthouse | Web performance + SEO basics | Google-centric | ✅ | ✅ |
| Core Web Vitals | Page experience metrics | Google-centric | ✅ | ✅ |
| CVSS | Vulnerability severity | Security | ✅ | ✅ |
| OWASP Top 10 | Security risk categories | Security | ✅ | ✅ |
| ATR (PanGuard) | AI agent threat rules | Agent security | ✅ | ✅ |
| AVS | AI search visibility | SEO + AI search | ✅ | ✅ |
AVS fills the gap between traditional SEO metrics and the emerging AI search landscape.
AVS is designed to be community-owned. We welcome:
- New AEO checks: Propose checks that improve AI citation prediction
- Validation studies: Run independent studies with different AI engines
- Language support: Test AVS with non-English queries
- Weight calibration: Help us optimize the scoring weights with data
See CONTRIBUTING.md for guidelines.
- Specification: AVS v1.0
- Reference Implementation: ultralab-scanners
- Live Scanner: ultralab.tw/probe
- Blog: ultralab.tw/blog
- Discord: discord.gg/ultralab
AVS is an open standard initiated by Ultra Lab. It is not affiliated with Google, OpenAI, Anthropic, or any other AI company.