LLM Visibility Tracker
Baseline your brand across ChatGPT, Perplexity, Gemini, Claude and Grok - then watch it change. Free.
What is an LLM visibility tracker?
An LLM visibility tracker monitors how often AI engines recommend your brand over time - not just once. LLM answers drift constantly: models get retrained, competitors publish, and the live web index moves, so a single check goes stale within weeks. CitedSpy captures a baseline today across ChatGPT, Perplexity, Gemini, Claude and Grok, scores your presence 0-100, then re-runs the same prompts on a schedule - so you see your visibility trend, catch regressions early, and get alerted the moment a rival overtakes you.
LLM visibility is how often, and how favourably, large language models surface your brand when buyers ask about your category. A visibility checker answers that question once - a snapshot of where you stand today. A tracker answers a different, harder question: which way is it moving? It captures the same measurement on a schedule so you can see a trend line, not a single dot.
That distinction matters because LLM answers are not static. The same prompt to ChatGPT or Perplexity can name a different set of brands this month than it did last month: models get retrained on fresher data, engines re-rank their live web sources, a competitor ships a comparison page, or a Reddit thread starts getting cited. A one-time check tells you nothing about that drift - by the time you notice you have dropped out of an answer, you have already lost the buyers who asked.
An LLM visibility tracker fixes the prompts, the competitors and the engines so every run is comparable, then re-runs them and records the result over time. The output is a baseline today, a visibility trend going forward, and an alert when something meaningful changes - you slip in the rankings, a rival overtakes you, or an engine stops citing your site. This is the monitoring half of Generative Engine Optimization (GEO): you optimise, then you watch whether it worked and whether it holds.
Start tracking in four steps
Enter your brand name
Type your brand as buyers say it. We work out your category and the real questions where an LLM should recommend you, so your baseline is grounded in your actual market, not a guess from the name.
Add your domain (recommended)
With your domain we read your homepage directly, which is what makes niche and local brands work - and it lets us tell when an engine cites your own site versus merely naming you.
We capture your baseline
CitedSpy finds your competitors and buyer prompts, then queries every LLM live. That first run is week one of your trend: your visibility score, rank and share of voice, timestamped.
Track it over time
Sign up free to re-run the same prompts on a schedule. Watch your visibility line move week over week and get alerted when you slip, a rival overtakes you, or an engine drops your citation.
How to read your visibility trend
Your baseline is the anchor: a 0-100 score plus your rank and share of voice the moment you ran it, across every engine reached. On its own a score is hard to judge - 42 could be climbing or sliding. The trend is what gives it meaning. The first run plots point one; every scheduled run after it adds a point, so a flat line says you are holding, a rising line says your GEO work is landing, and a dip is an early warning you can act on before it costs you traffic.
Read the per-engine rows alongside the trend. Visibility rarely moves uniformly - you might gain on Perplexity (which leans on live web search) while sliding on an engine working from older training data, or hold steady everywhere except the one prompt a competitor just targeted. Tracking each engine and prompt separately is what turns a vague "are we doing OK in AI" into a specific, fixable list: this engine, this prompt, this week.
What to remember
- An LLM visibility tracker measures how often AI engines recommend your brand over time - a trend line, not a one-off snapshot.
- LLM answers drift as models retrain, competitors publish, and live web sources re-rank, so visibility must be tracked, not checked once.
- Fixing the same prompts, competitors and engines on every run is what makes results comparable week to week.
- Alerts on rank drops, a rival overtaking you, or a lost citation let you react before you lose AI-driven buyers.
- Tracking is the monitoring half of GEO: you optimise, then watch whether the gain holds across ChatGPT, Perplexity, Gemini, Claude and Grok.
Our methodology
Tracking runs in two phases, then repeats. First, CitedSpy works out who you are: when you provide a domain we fetch and read your homepage, then use that plus live web search to identify your category, the roughly five competitors engines tend to recommend in your space, and a set of natural buyer questions where you should rank near the top. Pinning down this set - brand, competitors, prompts - is what makes every later run comparable.
Second, it sends each of those prompts to every available engine - ChatGPT, Perplexity, Gemini, Claude and Grok - using their live web-search tools where supported, reads each answer for your brand and competitors, records who is named and in what order, classifies sentiment, and collects the cited domains. From that it computes your visibility score, share of voice, and per-engine and per-prompt rank. That first run is your baseline - week one of the trend.
From there, tracking is just re-running the exact same set on a schedule and storing each result, so the score, rank and share of voice can be compared point to point and an alert can fire when a number moves past a threshold. Only engines actually reached are counted; a slow engine is reported as not reached rather than guessed. This is the same measurement engine behind CitedSpy's paid monitoring - the free tool captures the baseline, the product keeps the trend running and watches it for you.
LLM Visibility Tracker, answered
Related tools & reading
Stop guessing. Track your AI visibility.
Run this across all engines on a schedule, watch competitors, and get alerted when your brand slips from AI answers.