No independent, apples-to-apples accuracy benchmark of these platforms exists publicly, including here. What does exist is a repeatable way to test any one of them yourself in an afternoon — this is that protocol, not a ranked score.
A fair warning before the framework: this article does not contain a scored comparison of Profound, AirOps, Peec, or any Share-of-Model tool’s accuracy. Producing one honestly would require running the same fixed set of prompts through every platform, repeated enough times to separate a real pattern from ordinary AI response variance, then checking every result by hand — work no publisher of a comparison post actually appears to do, judging by how few show their prompts or their method. Publishing invented numbers instead would be worse than publishing nothing. What follows is the test protocol itself, so you can run it against whichever tool you’re evaluating.
01 — Why "accuracy" means different things for different tools
Every AI visibility platform reports a headline number — mentions, citations, share of model. What differs, often unstated, is what sits underneath that number: how many prompts were actually run, how those prompts were chosen, how often each one is repeated, and whether the underlying AI response was read by a person or only pattern-matched by a script. Two tools can both report "you appear in 40% of relevant AI answers" while measuring genuinely different things. For background on what the underlying concept is measuring, see what is AI visibility.
02 — Four dimensions worth testing separately
Coverage
What to check: Which platforms does the tool actually query, and at what tier? A vendor advertising broad engine support often gates most of it behind a higher plan — our own Profound vs AirOps vs Peec comparison found each vendor's entry-level plan covers meaningfully fewer engines than the number in its marketing headline.
Sample size and repetition
What to check: How many prompts run, and how many times each one repeats? A single run of a prompt tells you what the model said once. AI answers vary by session, so a tool that runs each prompt once and reports the result as fact is reporting a sample of one.
Freshness
What to check: How recently was the dashboard number actually refreshed? Some platforms update daily; others batch on a longer cycle. A number that's three weeks stale can describe a competitive landscape that's already changed.
Verification
What to check: Is a "mention" counted by a person reading the response, or by a script matching your brand name in the text? Automated matching misses paraphrased references and can also over-count — your brand name appearing in a disclaimer or a competitor list is not the same as being recommended.
03 — A self-test protocol you can run in an afternoon
This is the same manual method covered in how to check AI visibility, applied specifically to test a tool rather than your own brand:
- Pick 10–15 real buyer questions you already know the likely answer to — ideally a mix of ones where you’re confident you're mentioned and ones where you suspect you aren't.
- Run each one manually on every platform the tool claims to track, on the same day you check the tool's dashboard, and record whether you're mentioned, in what position, and whether the description is accurate.
- Pull the same prompts from the tool (or the closest equivalent it tracks) and compare row by row.
- Flag every mismatch as a false positive (tool says yes, manual check says no) or false negative (tool says no, manual check says yes) rather than averaging them away.
- Repeat once more, a few days later, before drawing a conclusion — a single mismatch can be ordinary AI response variance rather than a tool problem, but a mismatch that repeats is a real signal.
Use the checklist's citation and mention tracking section if you want the same categories laid out as a standing checklist rather than a one-time protocol.
04 — Questions worth asking any vendor before you trust their number
- How many prompts run per report, and are they yours, a generated set, or drawn from real search or support data?
- How many times is each prompt repeated before a result is reported?
- Is a "mention" verified by a person, or only pattern-matched?
- How often does the dashboard actually refresh?
- Which specific platforms are included at the plan you'd actually pay for — not the plan in the marketing headline?
A vendor that answers all five specifically, rather than in marketing language, is telling you something. So is one that can't.
05 — A note on "Share of Model"
Share of model is used two ways in this space, and mixing them up is its own accuracy problem. As a metric, it's your brand's mentions as a percentage of all brand mentions in your category — the AI-era version of share of voice. Several vendors also sell a product under that literal name. Before comparing a share-of- model number pulled from two different tools, confirm they covered the same platforms and the same prompt set. Otherwise you're comparing two different measurements that happen to share a label.
Rather not build the test yourself?
The audit runs the same kind of verification described above across all six major platforms, with every mention checked by a person, not just pattern-matched.
Book an AI Visibility Audit →06 — Frequently asked questions
How do I know if an AI visibility tool's numbers are accurate?
Run the same 10-15 prompts manually on the same platforms the tool claims to track, on the same day, and compare. If the tool shows a mention the manual check doesn't confirm (or misses one the manual check finds), that's a real discrepancy — not necessarily a broken tool, since AI answers vary by session too, but a data point worth repeating three times before you draw a conclusion either way.
What's the difference between a false positive and a false negative in AI visibility tracking?
A false positive is a tool reporting you were mentioned when a manual check of the same prompt and platform doesn't show it. A false negative is the reverse — you were actually mentioned, but the tool's sample didn't catch it. Sampled tools produce more false negatives than false positives, since missing an instance is a lot easier than inventing one.
Do all AI visibility tools sample the same way?
No. Publicly described approaches vary: some tools run a large, repeated set of prompts and treat the response distribution as the signal; others draw prompts from real user-intent sources like search queries or support tickets; some rely on scraping each platform's own interface rather than its API. Each approach has different blind spots, which is exactly why a vendor's dashboard number and your own manual check can legitimately disagree.
Is Share of Model a specific tool or a metric?
Both, depending on who's using the phrase. As a metric, share of model is your brand's mentions as a percentage of all brand mentions in your category, across the AI platforms your buyers use — the AI-era equivalent of share of voice. Several vendors also sell a product under that name. Before comparing a "share of model" number between two tools, confirm both are measuring the same set of platforms and prompts, or the comparison is meaningless.
Should I trust a vendor's own accuracy claims?
Read them, but verify with your own prompts before buying — this is a self-test protocol, not a defense of any one platform. A vendor's marketing page will describe its methodology in the most favorable light available; your own core buyer questions, run on your own account, are the only test that tells you how the tool performs on the thing you actually need it to track.
For the full AI visibility strategy framework, see the hub. For published pricing and engine coverage across three specific platforms, see Profound vs AirOps vs Peec, and for the wider field, see the best AI visibility tools.
Hami Tahm is an AI visibility consultant based in Toronto.
Disclosure: This article is educational and also describes a service I sell. It does not contain an original accuracy benchmark of any named platform — see the note in Section 01 for why.