See how AI systems understand and recommend your business
Gemmetric measures AI visibility: a roll-up score combining GEO (on-site readiness) + GEM (model strength). GEO measures how well your structure, metadata, and schema help generative engines parse each page. GEM measures model strength using awareness, understanding, trust, and reach.
We look at on-site readiness (structure, schema, metadata, intent coverage) and the signals that affect model strength. Then we turn gaps into Fix Packs with deployable schema and copy.
Built for teams who need evidence they can stand behind.
Private beta: we review requests and invite teams as capacity opens.
AI is already deciding who gets seen
Search engines return lists. AI assistants return answers.
AI Visibility is driven by two things: how readable your site is (GEO) and how strong the model’s understanding is (GEM). It’s computed as a roll-up score combining the two.
GEO — Generative Entity Optimization
On-site readiness: structure, metadata, schema, and intent coverage.
GEM — Generative Entity Model
Model strength: awareness, understanding, trust, and reach.
AI Visibility
Roll-up score combining GEO (on-site readiness) + GEM (model strength) — likelihood to be surfaced in AI answers.
The three questions the model is really asking
If those questions can’t be answered cleanly, recommendation confidence drops. You usually do not see that in analytics, because the user never clicks through. Scores are computed from what we can observe. If access is blocked, we’ll show what’s missing and what to fix first.
- Can the site be parsed cleanly (structure, schema, metadata)?
- Does the model have a strong, stable understanding of the business?
- Will it surface and recommend the business for the user’s intent?
Traditional SEO optimizes for
Being found
- Keywords, backlinks, metadata
- Clicks, impressions, and rankings
- Retrieval: which page should show up?
AI visibility optimizes for
Being chosen
- Clarity (GEO) + model strength (GEM)
- Answerability for real user intents
- Confidence: can the model recommend this?
This is why “more content” does not automatically help. If your schema is incomplete, your business identity is inconsistent across listings, or your pages are hard to parse, the model hesitates.
What you get after a scan
Clear fixes you can apply
You get core signal scores (GEO, GEM, and AI Visibility), diagnostics like AI Perception, 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
On-site readiness
GEM
Model strength
Awareness • Understanding • Trust • Reach
AI Visibility
Roll-up of GEO + GEM
GEO Score
Schema + metadata opportunity
GEM Score
Trust + reach gaps detected
AI Visibility Score
Roll-up of GEO + GEM
AI Perception
Misidentification risk detected
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 GEO fixes (JSON-LD, metadata, intent coverage) and GEM fixes (actions that improve model strength by improving the inputs that influence it—especially trust and reach—plus copy 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)
- GEO fixes: JSON-LD + metadata + intent-aligned copy
- GEM fixes: inputs that influence model strength (especially trust + reach)
- Priority ordering + estimated impact delta
Export bundle
JSON-LD snippet, copy blocks, CSV diagnostics, and a PDF-ready summary. Everything you need to implement.
Trust & accountability
Enterprise posture built in
The difference between a cool AI tool and a platform teams can rely on is operational truth. You need traceability, repeatability, and transparency.
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 →Avoids
- Rank tracking dashboards
- Keyword volume charts
- Content-at-scale generators
- Black-box automation
Focuses on
- Machine-readable clarity (structure + schema)
- Model strength: awareness, understanding, trust, and reach
- AI perception diagnostics (what models believe and recommend)
- Deployable Fix Packs with measurable deltas
If AI visibility matters to your business, this is the platform built for it.
We’ll review your request and invite you when a slot opens. No hype. No shortcuts. Just clarity you can defend with data.