How It Works
How Gemmetric turns AI visibility into a repeatable system
AI does not rank your business. It decides whether it can trust it. Gemmetric measures how visible, understandable, and confident AI systems are when they encounter your business online.
Not guesses. Not vibes. Signals you can point to and improve.
The workflow
Five steps. Defensible outputs.
We analyze the same inputs modern AI engines rely on to decide whether to recommend a business. Every step produces evidence, not opinions.
1) Scan what AI can actually see
Crawl access and restrictions, HTML structure, semantic clarity, schema coverage, and business identity signals across your site and key public sources.
2) Measure confidence, not just visibility
Score how certain an AI system can be: clear versus ambiguous signals, verified versus inferred facts, and known versus unknown data points.
3) Explain why the score is what it is
See which signals were evaluated, which were missing or blocked, what was unknown due to scan limits, and what evidence supported the result.
4) Translate findings into actionable clarity
Get clear prioritization, trust gaps that cause AI hesitation, structural issues holding you back, and Fix Packs you can deploy.
5) Track progress as AI systems evolve
Each scan creates a historical snapshot with preserved evidence, trendlines over time, and proof of improvements or regressions.
The questions we answer
Gemmetric is built around the same evaluation loop AI assistants run behind the scenes.
- Can I crawl this site?
- Do I understand what this business actually does?
- Is this information consistent and trustworthy?
- Should I confidently recommend this business, or hedge?
What you get after a scan
Clear fixes you can apply
You get three explainable scores, signal-level evidence, and Fix Packs with deployable schema and copy. This is designed to plug into a real workflow. Engineers can ship JSON-LD, marketers can update content blocks, and everyone can see the delta after the next scan.
See the workflow →GEO
Structural clarity
GEM
External verification
Perception
AI interpretation
GEO Score
Schema + headings opportunity
GEM Score
Listings disagree on category
AI Perception
Misidentification risk detected
Answerability
Missing intent coverage
Top Fix Pack (example)
Add LocalBusiness + Service schema, clarify primary category language, and publish an FAQ block aligned to customer intent.
Deployable output
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Your Business",
"url": "https://example.com",
"sameAs": ["https://..."]
}Fix Packs
Go from audit to deploy without the hand waving
Traditional tools stop at diagnostics. Fix Packs bundle the evidence, the recommended change, and deployable outputs. That usually means JSON-LD, updated metadata, and content blocks written for real intent queries.
See what you get →What’s wrong (evidence)
- Missing Service + FAQ schema on key pages
- Inconsistent primary category language
- Thin intent coverage for “comparison” queries
The fix (deployable)
- Generated JSON-LD (Organization / Service / FAQ)
- SEO-ready copy + metadata updates aligned to intent
- Priority ordering + estimated impact delta
Export bundle
JSON-LD snippet, copy blocks, CSV diagnostics, and a PDF-ready summary. Everything you need to implement.
What makes Gemmetric different
Built for recommendation, not rankings
Traditional SEO tools assume rankings explain outcomes. Gemmetric measures the signals behind AI confidence and makes them actionable.
| Traditional SEO tools | Gemmetric |
|---|---|
| Keyword focused | Entity and trust focused |
| Ranking assumptions | Confidence measurement |
| Black box scores | Explainable signals |
| Optimized for humans | Optimized for AI systems |
| Traffic thinking | Recommendation thinking |
The bottom line
When an AI is asked about your business, how confident is it, really? Gemmetric gives you the honest answer, the evidence, and the fixes to improve it.