Ranked list

Best AI visibility tools in 2026, ranked by whether you can check their numbers

Rovoki makes an AI visibility tool. We did not rank ourselves below, because a vendor scoring its own list first is the thing that makes these pages worthless. We scored ourselves on the same four criteria in a separate section at the end: 1.5 out of 4, up from the 0 we printed in our first draft. Both numbers are part of the story. Nobody paid to be included and we take no affiliate commission.

The short answer

When we first drafted this page in August, we believed exactly one vendor published how many times it asked the question, and what the margin of error was. We were wrong then, and the true number has kept moving: Evertune, Gumshoe, Profound and Popsight all publish sampling and error bars today. If a vendor will not tell you how many runs a score came from, the score has an unknown error bar, and the same question asked twice does not return the same answer.

Fastest useful version: Gumshoe is the only tool scoring 4 out of 4 on the criteria below, with a standing methodology page (±5 points at 95% confidence from 800 observations per run) and a fully published $99/mo price. Evertune publishes the deepest sampling in the category (100 prompts × 100 runs per model, ±1 point) at $800/mo. Below $100, Rankscale ($20, ten AI surfaces) and Otterly ($29, four engines) are the cheapest real coverage, and HubSpot’s AEO at $50/mo is the cheapest name-brand option.

How did we rank these tools?

Four criteria, one point each, no weights. Weights are where a ranking gets quietly steered toward whoever wrote it. All four are checkable by you, on the vendor’s own public pages, in about ten minutes, without a sales call: 1) does it publish how many times it runs each prompt; 2) does it publish a margin of error, and name the unit that margin applies to; 3) does it publish a standing methodology page? A dated blog post that discloses the numbers earns half a point, because a post is a snapshot and a page is a commitment; 4) does it publish pricing without a sales call.

We deliberately did not rank on engine count, prompt quota, dashboard features, or “best for agencies” verdicts. Every vendor page already competes on those, and they are not the thing that decides whether the number you are shown is real.

We wanted a fifth criterion: can you click a reported score and read the verbatim AI answer it came from, with a timestamp? We could not verify that from public pages for any tool on this list, ours included. It sits behind a login on all of them. Ask it in the demo.

Why does the same question give a different answer every time?

Because these systems are not deterministic, and the effect is far larger than most buyers assume. Engineers at Thinking Machines Lab sent 1,000 identical requests at temperature zero and got 80 different completions. Measured on brand mentions specifically, repeats of the same question on the same day overlap at a Jaccard index of 0.327 to 0.477. The reliability literature now recommends trusting per-brand detection only at seven or more runs per prompt per day, and a 45-study survey published in July 2026 prescribes interval reporting as the minimum honest treatment.

A visibility score built from one run per question is therefore a single draw from a distribution nobody reported. It is not so much wrong as unfalsifiable: you cannot separate a real 6-point gain from noise, because no error bar came with it.

The ±1 and ±9 problem

Evertune’s methodology page shows 100 prompts each asked 100 times per model, with a worked example of “29% visibility, ±1 pt”. To its credit, it prints the single-sample comparisons itself: ±9 points overall, ±44 for an individual prompt. Both numbers are right; they quote different denominators. One prompt asked 100 times at a 30% mention rate has a standard error of ±9 points at 95% confidence. A hundred prompts asked 100 times each is 10,000 observations and lands at ±1.

Nobody is lying. But a buyer who reads “±1 pt” on a dashboard and then acts on one prompt’s week-over-week movement is using a number that does not describe what they are looking at. The honest reading of this entire category, including us: the best-disclosed sampling supports confident statements about overall visibility, and does not support confident statements about individual prompt movements.

The comparison table

Prices fetched from each vendor’s own pricing page on 2026-09-07. Monthly figures unless noted. “Not public” means the vendor does not publish it, which is the finding rather than a gap in our research.

The tools, ranked

Where Rovoki sits, since we wrote this

We score 1.5 out of 4 on our own criteria. The first draft of this page scored us 0 out of 4 and said so; three of the four lines have changed since, which is the test we asked you to apply.

Runs per prompt: seven or more, but not yet published where you can check it. In August we ran one sample per prompt and called that the weakest possible position on the axis this page argues is the important one. The sampling change shipped on 24 August: every audit now asks each question at least seven times per engine, the floor the reliability literature sets. Half a point, because our criterion says publishes, and until our methodology page is live this paragraph is a claim on a listicle, not a page you can audit.

Margin of error: computed, not yet published. Same half point withheld. At one run per prompt there was nothing honest to publish. At seven-plus there is, and the report itself now refuses to render a movement that sits inside the noise floor.

Methodology page: not published. Still the honest zero. We held it while the measurement was n=1: publishing it then would have been the exact dishonesty we accuse others of. That reason expired on 24 August. Now it is just unshipped, and this sentence is here so you can hold us to it.

Pricing: published. rovoki.com/pricing is live, no sales call. The private beta is over: you can sign up and your first audit is free.

What we do have, and cannot prove to you from this page: every number Rovoki reports opens into the verbatim AI answer it came from, timestamped. That is the fifth criterion we dropped from the ranking, because no vendor here, us included, lets you verify it before buying. Come back in a quarter and check whether the four lines above changed again. That is the only test of this section that means anything.

What should you ask a vendor before you buy?

Six questions. Each has a short answer that a vendor either has or does not. 1) How many times do you run each prompt, per engine? 2) What is the margin of error, and is it for the aggregate or for this one prompt? 3) Below what movement do you refuse to call a change real? 4) Can I click this score and read the verbatim answer it came from, with a timestamp? 5) Where do the prompts come from, and can I add my own? 6) Which engines are live today, as opposed to on the roadmap?

Question 3 is the one almost nobody is ready for: the answer tells you whether the vendor has thought about their own noise floor or is reporting every wiggle as a trend. Question 6 matters more than it sounds: several tools above advertise a headline engine count that only the Enterprise tier buys.

Is a free AI visibility checker good enough?

For finding out whether you have a problem, yes. For deciding what to do about it, no. Free checkers run a small number of prompts once and return a score: enough to learn that AI answers in your category name somebody else, not enough to act on, and none of them prints the error bar that would tell you the difference. Worth ten minutes today: Semrush’s and Ahrefs’ checkers, HubSpot’s AEO Grader, SE Ranking’s (no signup), xfunnel’s free one-time audit, AthenaHQ’s free tier, Gumshoe’s free sample, and Geoptie’s standing audit.

Does any of this work in languages other than English?

Less than the marketing suggests, and the reason is not the tools. A Stanford-led team put 2,100 questions to six commercial chatbots across six language regions; for questions asked in Hindi, the single most-cited source was English Wikipedia, and answers to Hindi questions were 79.3% accurate against 89 to 91% elsewhere (Suzgun et al.). Your brand is being described to a large share of your buyers out of sources in a language you did not choose. Of the seventeen tools above, two (Pallix and Rovoki) are built around Indian-language answers. Check the language list against the languages your buyers actually use, not against the count.

What does an AI visibility tool cost in 2026?

The public SaaS range runs from $20/mo (Rankscale) to $800/mo (Evertune), with Enterprise above that on request; Popsight sits below the SaaS floor at €149/year plus your own API costs, and Pallix starts at ₹2,499/mo in India. Three patterns worth knowing: the headline engine count is usually the Enterprise count; annual discounts cluster at 15 to 20%; and the giants are pricing the bottom out: HubSpot at $50, Ahrefs from $50, Semrush at $99. What is not commoditized, anywhere on this page, is a defensible number. Sampling depth, error bars, and evidence you can open. That axis had one occupant in August and has four in September. It is where this category’s real competition is moving.

Corrections

A comparison page that quietly fixes its own errors is not doing the thing this page claims to do, so ours are listed. “Only one vendor publishes both its sampling and a margin of error”: wrong when written in our August draft; Profound had published both five weeks earlier, and by September Gumshoe and Popsight had too. “SE Ranking does not publish pricing”: wrong; its subscription page publishes every figure, and its score rose accordingly. “The Semrush $99/mo AI toolkit is a myth”: wrong; it is the live Base tier on Semrush’s own page.