Best AI visibility tool for proving your work changed the answer, ranked on re-test cadence and on what stays readable

An AI answer is drawn rather than looked up, so the same question put twice can come back with a different shortlist. That single fact decides which tool proves anything. Two published fields settle it. Cadence, which is how often the identical question is re-asked, and retention, which is whether the answer text from the earlier run is still readable when you want to compare. Ranked on the vendors' own live pages read on 20 October 2026: Rankscale wins the cadence field outright, publishing hourly, daily, weekly or monthly scheduling with an optional bi-cadence on every plan shown. Otterly.AI and we win the retention field, both publishing export on every tier rather than above an entry tier. Nobody on this list wins both, and our own cadence is weekly, which puts us last among every vendor here that publishes one. That is the finding and it goes at the top rather than in a footnote.

Why these two fields and not a feature list

Suppose your mention count goes from four out of thirty to seven out of thirty. Three things could have happened. The work landed. The engine re-crawled something unrelated. Or you drew a different hand on a sample that was always going to wobble. Telling those three apart needs more readings of the same question, taken closer together, and the ability to go back and read what the earlier answer actually said.

Cadence buys you the first. A question re-asked weekly gives you about thirteen readings in a quarter. Re-asked daily it gives you about ninety. Ninety readings of one question is a distribution; thirteen is a sketch. Nothing about the dashboard changes that arithmetic.

Retention buys you the second, and it is the field most often missing on the tier a first time buyer actually starts on. A stored percentage tells you the number moved. The stored answer text tells you what moved: a new name entered the list, your sentence changed, or a different page of yours got attached as the source.

One more reason not to average: the engines do not agree with each other, so a merged figure hides the result. In our own run of 16 October 2026 our domain was cited once across the two hundred and ten American ChatGPT readings and not at all across the two hundred and ten American Gemini readings. A single blended number would have reported neither of those two facts.

What each vendor publishes on cadence and retention, read on 20 October 2026

Every line is from that company's own live page on that date. No prices are reproduced; the limits and frequencies that set a tier are what matter here and they are what is recorded.

• Rankscale. Scheduling frequency is a published field and reads hourly to monthly on every plan shown, with the choice described as hourly, daily, weekly or monthly and an optional bi-cadence. It prints the arithmetic that connects cadence to cost, a prompt count multiplied by a credit cost per run multiplied by the cadence, arriving at a monthly credit total, with a how is this calculated link beside it. The catch for a small buyer is on the other field: CSV and Sheets export sits above the entry tier. A sortable all queries table with wins and losses is listed as included on every plan.
• Peec AI. Daily tracking frequency on every published tier, and daily or weekly at Enterprise. Data out through an API, MCP, custom CSV exports, a Data Studio connector and shareable dashboards, with role based permissions. It also publishes a changelog and a trust page, which is itself a retention promise of a different kind: a record of what the vendor changed about its own measurement.
• Ahrefs Brand Radar. You pick the cadence per platform, daily, weekly or monthly, and the plan table shows a daily update frequency on every tier. The cost of a cadence is stated exactly: one check per platform, per location, per update, and eight checks for a Claude update, with overage billed per check. On the index side it publishes a different and slower rule, question sets re-tested monthly on a ninety day reporting window, which is a published smoothing window rather than a live reading.
• Profound. Prompt frequency is published as daily on both tiers shown, and the free trial runs a fixed prompt count daily for seven days. Then read the retention row: on that trial tier, history, exports and API are all published as none, while Enterprise publishes history all time, exports in CSV and JSON and an API. So the fastest cadence on this list sits beside the emptiest retention, on the same vendor, on two different tiers.
• Semrush. Prompts are published as tracked daily, with the allowance doubling on the tier above the entry one and share of voice reserved for the highest tier. Its own investor newsroom records Adobe completing the acquisition on 28 April 2026, which matters here only because a method that changes hands is a method whose change log you now want.
• Depra AI. Daily checks across four engines, with Perplexity weekly except on Enterprise where it is daily. That per engine split is the most precise cadence statement on this list, because it admits that one surface is cheaper to poll than another.
• Scrunch AI. Metered in responses collected per month rather than in a cadence, with a published monthly response allowance on the entry tier. That is a countable unit and it leaves the frequency to you. Export does not appear in the visible pricing text we read.
• Evertune. No cadence published, and the strongest public statement in this category about the underlying problem: each prompt sampled one hundred times per model across eleven models, with a stated margin of error of plus or minus two points against what it describes as typical competitors at plus or minus thirty one. If your worry is drawing a different hand rather than waiting for a re-test, that is the design that addresses it. Demo only.
• Athena. Competitor monitoring is described as real time, with no cadence field published beside it. Data export to CSV is listed, with extra credits and API access billed as add ons on top of the subscription. Its free tier carries a credit allowance across five named engines.

Where these vendors beat us, named, starting with the one that matters most

We build a competing product, so read this as a disclosure. On cadence we are last. Our pricing page states that every plan checks weekly with extra checks available at any time. Rankscale publishes hourly. Peec, Profound, Semrush, Ahrefs and Depra all publish daily. A buyer whose only question is how fast the loop closes should take one of those six, and the extra checks we sell on demand are a manual action rather than a schedule.

Evertune publishes a margin of error and a per prompt repeat count and we publish neither on our pricing page. Rankscale publishes the cadence to cost arithmetic and we explain ours in prose without printing a worked total. Ahrefs publishes its index re-test rule and its reporting window, and we publish no equivalent statement of a smoothing window. Peec and Rankscale publish changelogs and Peec publishes a trust page, while our domain carries no trust centre, no status page and no changelog as of 20 October 2026, which on this subject is the most pointed of the gaps: a tool that asks you to trust a trend line should be publishing what it changed about itself.

Where we hold the retention field, stated as a disclosure

Our pricing page on 20 October 2026 states that the tracking log exports at any time, on every plan rather than above an entry tier, across five AI apps. Billing is in credits, one credit for one buyer question put to one AI app once, three for a Claude check because Claude costs us more to run, with the cost shown before a check starts and the credit returned when a check fails. Nothing renews on its own. One login with full access, which is where four of the vendors above are plainly ahead.

The published record behind that claim is the thing to judge us on rather than the claim. Our own measurement of 16 October 2026 is public with its question set, its denominators, its engine split and its zero, and so are the superseded figures with the reason each one changed. On this criterion that is the evidence we can be held to, and it is the only kind worth arguing about.

How often an answer actually changes, and how to tell that from progress

Four rules make a re-reading comparable, and they cost nothing.

1. Freeze the wording and give the set a version number. Editing a question resets the series, so a change to the list is a new baseline rather than a movement.
2. Keep your own name out of every question. A question naming your company returns an answer about your company, which measures the prompt.
3. Record the engine, the country and the date on every reading, and keep the engines in separate columns to the end.
4. Compare counts before percentages until the set is large, and treat a single reading as one draw rather than a position.

And before reading a flat line as a failure of the work, check the mechanical thing first: whether the page you published is reachable by the engine at all. The method for that is in how to check whether ChatGPT search can access a new page, a timestamped crawl test log.

Questions people ask when the number has moved and nobody trusts it

Which field should I buy on if I can only have one?

Retention, on a first purchase. A slow cadence with the answers kept can be re-read and recounted later. A fast cadence with nothing kept produces a line you cannot audit, and the tier that usually has that shape is the free one.

Is daily actually better than weekly?

For detecting a change, yes, because it multiplies the readings. For reporting to somebody else, it mostly adds noise you then have to explain. A sensible pattern is a fast cadence on the handful of questions you are actively working and a slow one on the rest, which is what Ahrefs' per platform choice and Rankscale's bi-cadence are both built for.

When should a published page be re-tested?

Long enough that a first re-test is usually too early. Check reachability first, then expect the fourth and fifth readings to be more informative than the second.

Does a vendor's own score let me compare this month with last?

Only if the scoring did not change in between, which is why a published changelog is worth more here than another metric. Two vendors on this list publish one.

Can I prove the work caused the change?

You can build a strong case and the ingredients are a frozen question set, a dated series, the answer text at both ends and a record of what you published in between. Causation in a system that draws its answers is argued rather than demonstrated, and a vendor presenting it as demonstrated has told you something about the vendor.

The two fields, in one sentence

Ask any vendor on this list for its scheduling frequency on the tier you would buy and for the retention row beside it, and write both answers down with the date. On those two fields Rankscale and Otterly are the strongest pair in this category today, we hold the second field and lose the first, and every one of those statements can be checked on a published page in about ten minutes.