LLM visibility tools: how brand trackers work and how to pick one
·4 min read
LLM visibility tools track how often AI assistants like ChatGPT, Gemini and Perplexity name your brand or cite your site when people ask the questions your buyers ask. They all work the same way underneath. They send a fixed set of prompts to several engines on a schedule, read each answer, pull out the brands and cited links, and report rates over many runs. The differences that matter are which engines they reach, how many samples sit behind each number, and whether you can open the raw answers.
For definitions and the manual method, see what AI visibility is and how a score is calculated.
How LLM visibility trackers work
A tracker is a loop of four steps, and each step is a choice that shapes the number you see.
- Prompts. You or the tool write questions such as "best payroll software for a 20-person agency". Category and problem prompts are the honest test. Prompts that contain your name inflate every rate.
- Engines. Each prompt goes to every engine in your plan. A tool can call a model through its API with web search switched on, or drive the consumer chat app the way a person would. The two can return different answers, because the app carries its own settings and the API carries none.
- Extraction. A language model or a name match reads each answer and lists the brands in order. The cited links get matched against your domain.
- Aggregation. Counts become rates per prompt, per engine and per date.
Step 3 is where trackers disagree. A brand called "Linear" is also an ordinary word, and a sloppy matcher counts every lowercase use.
Why one answer is not enough
The same prompt sent twice to the same engine often names different brands in a different order. Sampling, web search results, model updates and location all move the answer, as explained in why ChatGPT gives different answers. A tracker that runs a prompt once is showing you one draw.
Repeated runs fix this. In an illustrative case, a brand named in 2 of 3 answers could turn out to be named usually or rarely. Named in 20 of 30 runs over a month, the rate holds steadier week to week. Read position as a rough guide, since your place shifts whenever one more name enters the list.
What LLM visibility metrics mean
Most trackers report some mix of these four.
| Metric | What it counts | Read it as |
|---|---|---|
| Mention rate | Answers that name you, out of answers collected | Whether you make the shortlist at all |
| Citation share | Your cited links out of all cited links, or answers that cite you | Whether engines trust your pages as sources |
| Position | Your place among the brands named | How early a reader meets your name, noisy |
| Sentiment | A model's score for how the answer talks about you | A flag to go read the answer, not a verdict |
Distrust sentiment most. It is one model grading another model's text, and "positive" says nothing about whether the facts are right. To compare yourself with named rivals, share of voice for AI search explains the calculation.
What to look for in AI monitoring tools
Judge AI monitoring tools against your buyers, not by feature count.
- Engines your buyers use. Coverage of Google AI Overviews and AI Mode varies a lot between tools.
- Runs per prompt. Ask how often each prompt runs on each engine.
- Raw answers. You need the full text and cited URLs behind each count, or you cannot fix anything.
- Your own prompts. Generated prompt lists tend to be generic. You should be able to write and freeze your own.
- Locations and languages, if you sell in more than one market.
- Data export or an API, if the numbers need to land in a report you already have.
Some LLM visibility tools and what their sites say
These descriptions come from each vendor's own home page, checked in October 2026. Features and engine lists change, so confirm on the vendor's site before you buy. No prices are listed here.
| Tool | Engines named on its site | Stated details |
|---|---|---|
| Peec AI | ChatGPT, Perplexity, Gemini | Runs each prompt once every 24 hours on each selected model. Reports visibility, position and sentiment. Offers a REST API and MCP |
| OtterlyAI | ChatGPT, Google AI Overviews, Google AI Mode, Perplexity, Gemini | Simulates prompts, monitors responses and extracts brand and competitor data. Alerts for brand mentions |
| Profound | ChatGPT, Claude, DeepSeek, Gemini, Google AI Overviews, Microsoft Copilot, Perplexity | Lists citation analytics, sentiment and prompt volumes |
| AthenaHQ | ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot on all plans; AI Mode, Claude, Grok, DeepSeek, Meta AI on paid plans | Mentions citation tracking, competitor benchmarks and sentiment scores |
All four are dashboards for tracking over time. If you only need today's picture on a few prompts, a spot check costs less.
Spot-check your LLM visibility on one prompt
Our AI Answer Visibility tool asks ChatGPT, Perplexity and Gemini one question of up to 300 characters, with web search on. It uses the brand name you enter, or reads it from your home page's Organization, WebSite or og:site_name markup and falls back to your domain. For each engine it reports whether the answer names your brand and in what place, whether it cites your site and which URLs, plus the full answer. It lists up to 10 other brands and 10 other domains the answers named and cited.
A run costs 120 credits and takes one answer per engine, so it is a sample. It does not track answers over time, does not cover Google AI Overviews or AI Mode, and does not score sentiment. For what the engines say about you, use Brand Narrative Check.