AI Visibility Tools Compared: How to Measure Whether LLMs Mention You
What AI-visibility platforms actually measure, the three methods underneath them, when the free stack is enough, and a buyer's checklist by team stage.
Every "AI visibility platform" is a wrapper around three measurements: prompt panels (does the assistant name you?), citation tracking (is your domain in the reference lists?), and share-of-answer over time (is that changing?). You can run all three free for a quarter before buying anything. This guide explains what the paid layer adds, where it still disappoints, and the checklist we use when a team asks us whether to sign.
The three methods under the hood
| Method | What it answers | Free version | Paid adds |
|---|---|---|---|
| Prompt panel | "Name me tools for X" → are you named? | Spreadsheet + logged-out sessions, weekly | Scale (hundreds of prompts), history |
| Citation tracking | Is your domain in AI Overview / answer references? | Manual SERP checks; SERP APIs | Automated polling, per-query deltas |
| Share-of-answer | Trend: mentions vs competitors over time | Hand-tallied from the panel | Dashboards, alerts, competitor sets |
The free stack is brutally simple: ten questions, four assistants, weekly, logged out, results in a sheet. It catches the big moves — appearing, disappearing, being described wrong — which is 80% of the value. What it cannot do is scale (fifty prompts × four engines × weekly is a job, not a task) or attribute (which new mention coincided with which citation appearing).
What paid platforms add — and where they disappoint
The add-ons worth paying for: historical prompt-panel data (so a change is a trend, not a mood), competitor mention sets, and integrations that tie visibility to traffic (Search Console, referral logs). The disappointments we see consistently:
- Sampling theater. Panels of twenty prompts sold as "AI visibility score" — a score you cannot decompose is a mood ring.
- Logged-in bias. Results pulled from authenticated sessions do not match what your buyers see.
- Citation lists that miss the engines you care about. If a tool only tracks Google AI Overviews and your category lives in ChatGPT and Perplexity, you are measuring the wrong room. Ask which engines and which retrieval modes (search vs conversational) each metric covers.
A buyer's checklist by stage
- Pre-launch / first year: free stack only. Spend the budget on the assets that get cited instead (question pages, listings, roundups).
- Growing, one marketer: pay for the prompt panel + history once the free sheet takes more than two hours a week. Keep citation tracking manual via a SERP API or our AI Overviews reference method.
- Team with a GEO owner: full platform, but contract on engines covered and prompt counts, not on "score".
- Everyone: pair any tool with the Search Console proxy for AI-driven traffic (method) — visibility without traffic is vanity.
The metric that matters
Not "are we mentioned" but "are we mentioned in the questions that precede purchase" — comparison and alternative phrasings. Track those separately from awareness questions; they move first when your work is working, and they are the ones a founder actually cares about.
FAQ
Is there a standard "AI rank" metric? No. Positions do not exist in synthesized answers; any single score is a vendor construct. Compare methodologies, not scores, across tools.
How often should I sample? Weekly for the head questions, monthly for the long tail. Retrieval sets shift on days-to-weeks cycles; daily sampling buys noise.
Do these tools help me get cited, or only measure? Measurement only. The earning happens in content, listings and mentions — the playbook and your listing footprint are where citations come from.
Measurement tells you whether the mention work landed. Start the work where models look first: submit your product on aat.ee and compare the networks and their DR.