AI brand monitoring
See how your brand is recommended in ChatGPT, YandexGPT, and GigaChat
Buyer journeys increasingly start in AI assistants. If your brand is absent from category shortlists, demand goes elsewhere before users ever open a SERP.
What a finished report looks like
The sample report highlights executive-ready blocks: metrics, reliability context, change summary, and next actions.
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 AI visibility
Add sibling models in the same check to see if the pattern is specific to AI visibility or repeats across the stack.
About this model
Model behavior diverges by platform, language, and prompt intent; one manual chat thread is never enough for decision-grade reporting.
Periodic snapshots let teams connect content, PR, and product changes to measurable shifts in AI visibility.
How we measure visibility
Getllmspy runs fixed scenario packs without inserting your brand name into prompt text and returns one standardized report.
- Parallel checks across ChatGPT, Claude, Gemini, YandexGPT, GigaChat, and more
- LLM-Score, share of voice, sentiment, competitors, and answer quotes
- Trend tracking and change summary since the previous issue
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.
Without regular monitoring, brands can silently disappear from AI recommendations long before teams notice.