What is AI visibility tracking?
AI brand monitoring, LLM monitoring, AI search monitoring
AI visibility tracking is measuring how your brand shows up in AI answers over time. You run the same prompts across engines such as ChatGPT, Gemini and Perplexity, in each country and language you care about, and compare mentions, positions, sentiment and citations from run to run.
AI visibility tracking runs a fixed set of prompts across engines, countries and languages on a schedule.
It measures mentions, position in the answer, sentiment and cited sources for you and your competitors.
Trends across repeated runs matter more than any single answer, because AI answers vary.
How AI visibility tracking works
AI visibility tracking works by asking AI engines the questions your buyers ask, saving every answer and analyzing what each answer says about your brand.
Choose prompts: a set of questions that represent your category, a practice known as prompt tracking.
Choose engines and markets: for example ChatGPT, Gemini, Claude and Google AI Overviews, in each country and language you sell in.
Run on a schedule: send the same prompts again and again so you can compare like with like.
Parse the answers: detect brand mentions, their order, the tone of each mention and the sources cited.
Aggregate: roll results up into scores and trends by engine, market, prompt and competitor.
The result is a time series of how AI engines describe your category, not a one-off screenshot.
What AI visibility tracking measures
AI visibility tracking measures whether you appear, where you appear, how you are described and which sources sit behind the answer. The core metrics are:
Mentions: how often your brand is named across tracked prompts.
Visibility score: the share of answers that include you.
Average position: how early in the answer you are named.
Share of voice: your mentions compared with competitors.
Sentiment: whether the answer describes you positively, neutrally or negatively.
Citations: the pages and domains the engine credits.
Looking at these together tells you more than any one number. A high visibility score with negative sentiment is a different problem from a low score with positive sentiment.
AI visibility tracking has real limits
AI visibility tracking gives you a representative sample, not a full record of what real users see. You should know its limits before you act on the numbers.
No query logs: AI engines do not share what users actually ask, the way Google Search Console does for search, so tracked prompts are informed choices.
Variation: the same prompt can produce different answers from one run to the next.
Personalization: memory, location and conversation history can change answers for individual users.
Model updates: an engine update can shift results overnight, which is a real change but not one you caused.
You handle these limits with method. Track enough prompts to cover your category, run them repeatedly, keep the prompt set stable, and read trends rather than single answers.
How to set up AI visibility tracking
You set up AI visibility tracking by deciding what to measure before you decide how to improve it.
Map your category: list the problems, use cases and comparisons buyers ask about.
Balance prompt types: include branded and unbranded prompts, since unbranded prompts show whether you are recommended at all.
Pick competitors: track the brands you lose deals to, not only the biggest names.
Cover your markets: run prompts in the countries and languages where you sell.
Set a cadence: run prompts often enough to see trends, and note the dates of your own launches and campaigns.
Then review results on a fixed rhythm. Look for prompts where competitors appear and you do not, and for sources that engines cite again and again. Those are your clearest next steps.
How KIME tracks AI visibility
KIME runs your tracked prompts across ten AI engines, by country and language, and records how each answer mentions you and your competitors. Track & Analyze shows visibility, placement, share of voice and sentiment for every prompt, with trends over time and filters by engine, category and prompt. You can benchmark competitors side by side and open individual answers to see the sources behind them.
Frequently asked questions
How often should I track AI visibility?
Track AI visibility often enough to separate trends from noise. Because answers vary between runs, a single check tells you little. Running the same prompts on a regular schedule, and reviewing results weekly or monthly, shows whether your visibility is rising or falling. Note your launches, campaigns and site changes so you can connect them to the trend.
Can I see what real users ask ChatGPT about my brand?
No. OpenAI and other AI providers do not share user prompts with brands the way Google Search Console shares search queries. AI visibility tracking therefore uses a set of representative prompts that you choose based on customer research, search data and sales conversations. Treat the results as a reliable sample, not a complete record.
Is AI visibility tracking the same as rank tracking?
It is similar in spirit but different in practice. Rank tracking records your position in a fairly stable list of links. AI visibility tracking records whether you are mentioned in a generated answer, how early, in what tone and with which sources. Because answers vary, it relies on repeated runs and rates rather than a single position.