GEO monitoring
GEO monitoring reveals how your brand appears in AI recommendation answers
Generative Engine Optimization requires measurement. GEO monitoring helps teams see where they are present, missing, or misrepresented in category-style AI recommendations.
What a finished report looks like
The sample report demonstrates how to read GEO signals and convert them into quarterly priorities.
Carapelli
Mentions by model (demo run)
Highlight: — — the focus of this landing page. Numbers are illustrative.
Competitors in this slice
Your real report uses the same layout: scores, per-model breakdown, quotes, competitors, and citations — with your brand and the models you select.
Benchmarking
Timestamped snapshot
Completion time is stored with every run—clean before/after comparisons when you change positioning or content.
Method
Organic-style prompts
Your brand name is not pasted into the question text; we score whether models still mention you in realistic category queries.
Context
Around GEO monitoring
Add sibling models in the same check to see if the pattern is specific to GEO monitoring or repeats across the stack.
About this model
Traditional SEO does not fully capture answer-level framing from LLMs; dedicated GEO metrics are needed for recommendation surfaces.
With repeatable snapshots, teams can connect content and authority updates to measurable recommendation shifts.
How we measure visibility
Getllmspy runs category prompts without naming your brand and scores mention quality across selected models.
- GEO metrics: share of voice, list position, sentiment, and stability
- Model-to-model and issue-to-issue comparisons
- Actionable priorities for content, trust signals, and PR
Inside the report
Snapshot header
Completion time and which models ran—your anchor for before/after benchmarking.
LLM-Score & share of voice
Aggregated 0–100 signal plus the share of models that mentioned your brand at least once.
Competitors & roundups
Who appears next to you in ChatGPT answers: names, frequency, comparison or recommendation context.
Quotes & wording
Answer excerpts for manual review—how the model talks about the category and your brand.
Same prompts on other models
Parallel runs (Claude, Gemini, Perplexity, …) to see if the pattern is ChatGPT-specific.
From check to PDF-ready snapshot
Brand & niche
You set brand context, site, category, language, and check type—this selects the prompt pack.
Model mix
Pick the LLM families to include; the same scenarios run in parallel across all of them.
Server run
The job executes on our side; you can close the tab and open the report from History when ready.
Report
LLM-Score, share of voice, competitors, quotes, citations—exportable and rerunnable on demand.
GEO monitoring works best as an operating cadence, not a one-off experiment.