Perplexity · LLM monitoring
Inline sources mean you see which URL won the citation in Perplexity
A competitor shows up with a link to a review site while you don’t—Perplexity makes that visible in the UI. Among major assistants it is distinct for showing references inline; tracking it exposes which domains “own” mentions in best-of lists.
What a finished report looks like
The sample run highlights Perplexity: mention rate plus excerpts from Perplexity answers, including citation-style signals captured in the report.
Carapelli
Mentions by model (demo run)
Highlight: Perplexity — 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 Perplexity
Add sibling models in the same check to see if the pattern is specific to Perplexity or repeats across the stack.
About this model
Perplexity’s public experience foregrounds footnotes and outbound links—when a roundup cites a specific publisher or marketplace, that chain is observable.
It is growing fast as an AI search surface for younger tech users; media, SaaS, and e-commerce brands live or die on linked “top tools” answers where Perplexity compresses winners aggressively.
How we measure visibility
Same organic Getllmspy scenarios: no brand name in the question; we analyze answer text and citation signals surfaced in the report.
- Compare Perplexity with ChatGPT, Claude, Gemini, and more
- Competitors, quotes, LLM-Score, share of voice
- Rerun after catalog or review-site updates
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 Perplexity 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 Perplexity-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.
If Perplexity rarely cites you, strengthen primary sources, comparisons, and third-party reviews.