Differentiation
This is not SEO with a new label.
AI assistants do not return ten blue links. They return a recommendation. Gemmetric is built to measure what models use to decide who gets chosen.
Three layers: structural clarity, external verification, and model perception.
The category shift
From rankings to recommendation
Traditional SEO describes retrieval. AI visibility describes whether models can understand you, verify you, and recommend you.
| Old world | AI world |
|---|---|
| Rank for keywords | Be chosen as the best answer |
| Optimize for search engines | Optimize for AI assistants |
| Links + keywords | Clarity + verification + confidence |
| Click through rate | Model confidence |
Not just on page
Models cross check your identity against public sources. Inconsistency kills confidence.
Not just schema
Structured data helps, but recommendation also depends on clarity, intent coverage, and perception.
Not just volume
Content mills and keyword stuffing do not create trust. Useful answers do.
What we measure
The AI Visibility Stack
Recommendation is a gate. If you fail any layer, visibility collapses.
GEO — Generative Entity Optimization
On-site readiness: structure, metadata, schema, and intent coverage.
GEM — Generative Entity Model
Off-site corroboration: the strength, consistency, freshness, and coverage of external signals that support the entity.
AI Perception
How models currently define and understand the business, including current confidence and interpretation quality.
AI Identity
Canonical identity infrastructure: define, publish, validate, and observe a machine-readable business identity.
AI Visibility Score
Gemmetric's blended top-line score across GEO, GEM, AI Perception, and AI Identity.
It reflects the combined state of structural readiness, external corroboration, model interpretation, and canonical identity.
GEO score
Structural clarity. Headings, page purpose, schema, and content blocks that machines can parse quickly.
GEM score
External validation. Public listings and sources agree on who you are, what you do, and where you exist.
Perception index
Interpretation. We measure what models believe about you and whether they would recommend you for real intents.
What you get after a scan
Clear fixes you can apply
On supported plans, you get pillar scores for GEO, GEM, AI Perception, and AI Identity, plus the blended AI Visibility Score and Fix Packs with deployable schema and copy. Engineers can ship JSON-LD, marketers can update content blocks, and everyone can see the delta after the next scan.
See the workflow →GEO
On-site readiness
GEM
Off-site corroboration
Consistency • Freshness • Coverage
AI Perception
Current model understanding
Awareness, trust, and interpretation quality
AI Identity
Canonical identity loop
Ledger • Gateway • Validation • Observation
AI Visibility
Roll-up across four pillars
GEO Score
Schema + metadata opportunity
GEM Score
Corroboration gaps detected
AI Perception
Interpretation confidence is mixed
AI Identity
Gateway + validation incomplete
AI Visibility Score
Blended view across all four pillars
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 can mean GEO fixes, GEM corroboration work, AI Identity publishing steps, and copy updates shaped by what AI Perception is showing now.
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)
- GEO fixes: JSON-LD + metadata + intent-aligned copy
- GEM fixes: strengthen corroboration across profiles, listings, and trusted references
- AI Identity fixes: publish and validate a clearer canonical identity
- Priority ordering shaped by current AI Perception blockers
Export bundle
JSON-LD snippet, copy blocks, CSV diagnostics, and a PDF-ready summary. Everything you need to implement.
Trust moat
Operational truth, not marketing promises
We log every scan and expose reliability metrics like an SRE team would. Customers see success rate, latency trends, and domain level failures, then they can defend decisions with data.
Sample metrics shown for illustration.
Success rate (rolling)
99.2%
See reliability over time. No black boxes.
Avg scan duration
42s
Latency spikes can indicate site or routing issues.
Failure rate by domain
0.8%
Surface blocked crawlers, robots rules, and auth walls.
SLA compliance
On target
Enterprise posture: measurable, auditable delivery.
You get the same operational transparency we use internally.
Read the SLA story →Ready to see your delta?
Start with a scan. Gemmetric will show whether your bottleneck is clarity, verification, or perception, then ship Fix Packs to close the gap.