See how AI systems understand and recommend your business
Gemmetric measures AI visibility. That means how reliably an LLM can parse your site, confirm your identity from public sources, and recommend you for real user intent.
We look at structure, structured data, citations, and answerability. Then we turn gaps into Fix Packs with deployable schema and copy.
Built for teams who need evidence they can defend in a meeting.
AI is already deciding who gets seen
Search engines return lists. AI assistants return answers.
Visibility now depends on whether models can understand, verify, and choose your business with high confidence. That confidence is driven by signals they can parse quickly and corroborate across sources.
1) Understand
Can AI parse what you do, who you serve, and what you’re best at?
2) Verify
Do trusted public sources corroborate your identity and claims?
3) Choose
Would the model recommend you with confidence for the user’s intent?
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.
- What is this business, exactly?
- Are its claims consistent across trusted sources?
- Can it be recommended without uncertainty?
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, verification, and trust
- 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 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.
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.
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)
- External verification (identity consistency)
- Perception accuracy (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.
No hype. No shortcuts. Just clarity you can defend with data.