We ask ChatGPT, Google AI Overviews, Google AI Mode, Gemini and Perplexity the questions your customers actually type — written natively in their language — and show you how often each one names you, who it names instead, and how confident that answer really is.
Free first measurement · no card · results in minutes · how we measure
| Platform | Cited | 0% ————— 100% |
|---|---|---|
| Perplexity | 92% | |
| Gemini | 67% | |
| Google AI Overviews | 33% | |
| ChatGPT | 33% |
The band is drawn before the point: the interval is the measurement, the point is where it sits.
A real report, not a mock-up: the questions we ask, the interval on every number, who is cited instead of you, and the verdict thirty days later.
Most tools in this category stop at a dashboard; most agencies never measure. This runs the whole loop, and the fourth step is the one everybody else skips.
Your customers' questions, asked repeatedly on five assistants, in the language they type. A citation rate with an interval, per platform and market.
The questions you lose, who is cited instead, and the exact pages the assistants relied on. That list is the work.
Pages that open with the direct answer and carry the facts only you have; outreach to the pages already cited. Delivered, on your domain.
Same questions, same repeats, thirty days later. Changed, within noise, or new baseline — decided by a test registered before the work began.
Citation rates from our published audit, which measured four platforms. One cites a source on nine answers in ten; another on one in three — and carries an interval so wide that a single measurement of it tells you almost nothing. Google AI Mode is not in these figures: we added it to the product after the study ran, and quietly folding it into published results would misrepresent what was measured.
Cites sources on nearly every answer, and is the most stable of the four.
Grounded answers cite well, but the set of sources moves between runs.
Cites least often of the four, and the interval is correspondingly wide.
Widest interval in the study — a single measurement here tells you very little.
Figures from the multilingual audit, recomputable from the open dataset at 10.5281/zenodo.22491737. The white band on each card is the 95% interval.
A live furniture retailer measured against two named competitors — same questions, same platforms, same intervals. It is a store we operate ourselves, not a customer: with no customers yet, the only measurement we can honestly show you is our own. Nothing here is illustrative — it is one run of the tool, and every figure is in that run's CSV.
of 168 AI answers cited the retailer. The interval is what a 168-answer sample supports — not the single number a competitor tool would print.
Its closest named competitor was cited on 14% of the same answers, interval 7–21%. The two bands do not overlap, so the gap is real and can be stated as fact.
The same brand, the same questions, two languages. Here the intervals do overlap, so the report declines to call it a gap. That restraint is the product.
Everything here exists because the measurements already on the market did not hold up when we tested them.
A rate is never shown without the range it sits in. Where a platform is volatile the range is wide, and you see that at a glance instead of discovering it three reports later.
Movement is only called a change when a Fisher exact test can demonstrate it, corrected across every platform in the report so the false-alarm rate does not climb with each one. Everything else is labelled within noise. Most tools draw a trend line through variation of this size.
Written natively per market, not translated. German buyers search Nachbau, the Dutch say namaak; neither is what a dictionary returns for "replica".
Every domain cited across the whole answer pool, ranked, with your position in it — so you see who is being recommended in your place, not merely that you were absent.
Worst-performing questions first, broken out by platform and language. A brand strong at home and invisible abroad is the most common finding, and the most actionable.
The prompts, the raw observations and the measurement code are published under an open licence, and your own results export as CSV. Nothing is a black box.
Pick what you sell and the countries that matter. We assemble the buying questions real customers ask in each market, written natively in that language.
ChatGPT, Google AI Overviews, Google AI Mode, Gemini and Perplexity — every question asked repeatedly, because these systems do not answer identically twice.
Citation rate per platform with its interval, share of voice against every competitor cited, and the questions you lose. Re-measured on your schedule.
Citation rate per platform and per market, each drawn on a 0–100% track with its interval. Wide band, volatile platform. You are never asked to trust a bare number.
Every domain cited across the answer pool, ranked, with your position marked — including when you fall outside the top ten.
Worst-performing prompts first, per platform and market, exportable as CSV so you can work through them.
The right-hand column describes the common pattern across AI-visibility tools we examined while writing the measurement study. Individual products vary, and some do better.
| AnswerRange | Typical AI-visibility tool | |
|---|---|---|
| What is measured | AI search products people actually use | Often raw LLM APIs, which is not the same thing |
| Confidence interval on each figure | Shown on every rate, always | Typically a single percentage |
| Repeat measurements per question | Three, by default | Usually one |
| Change reporting | Withheld unless an exact test demonstrates it | Trend line drawn through all movement |
| Non-English prompts | Authored natively in six languages | Commonly machine-translated from English |
| Published methodology | Three open-access papers, full datasets | Method generally not disclosed |
| Underlying data | Exportable, CC BY 4.0 | Usually retained in-product |
You do not have to take our word for any of this. Most tools in this category publish three numbers on their pricing page — prompts tracked, engines covered, and responses included. Those three numbers tell you how many times each question actually gets asked.
responses ÷ (prompts × engines × days in the period)
= how many times each question is asked before the number on your dashboard is printed.If that comes to one, the figure you are shown is a single draw. Our published audit found a platform contradicting itself on around 40% of repeated identical questions, so a day-over-day move on one draw is mostly the platform changing its mind.
| A real configuration, from a published price list | Answers behind one report | Times each question is asked | Range around a 2% score | Smallest change it can detect |
|---|---|---|---|---|
| A weekly tracker, 8 models, one question each | 8 | one | 0.0 – 36.9% | none at any size |
| A $37 daily plan: 40 prompts × 1 engine | 40 | one | 0.1 – 13.2% | +17.5pp |
| A $97 daily plan: 100 prompts × 2 engines | 200 | one | 0.5 – 5.0% | +4.5pp |
| A $297 daily plan: 400 prompts × 5 engines | 2,000 | one | 1.4 – 2.7% | +1.0pp |
| AnswerRange: 12 prompts × 4 platforms, monthly | 144 | three | 0.4 – 6.0% | +6.3pp |
Every row is a live configuration from a vendor's own published price list, read first-hand. We have not named them, because the point is the sum rather than the company — and because a reader can run it on their own invoice in a minute. The last row is ours, computed identically and with nothing set aside for it: at 144 answers our own smallest detectable change is 6.3 points, which is why this product reports monthly rather than daily and refuses to draw a line through anything smaller. Our method is at /methodology and our data is open.
Every measurement spends real money at four API providers. The plans reflect that rather than an invented seat price.
Prices in euro, excluding VAT. Billing is not yet switched on — every measurement is currently free while the tool is in its first release.
One-off: measured placement audit — does a sponsored “best of” article get you cited and recommended? €249 per placement · done-for-you work on services.
Because a single percentage would overstate what any sample of AI answers can tell you. Ask the same question twice and these platforms often answer differently — in our published audit, one contradicted itself on around 40% of identical repeated questions.
The range is the honest form of the number. A narrow range means the measurement is precise; a wide one means the platform itself is volatile that week. Both are worth knowing, and hiding the second behind a confident-looking figure is how a measurement product misleads you.
It will — once a two-sided Fisher exact test on the two measurements can demonstrate the change. Until then movement is labelled within noise.
One detail matters more than it looks. A report compares five platforms at once, and five separate tests at the usual 5% threshold would carry a 23% chance that at least one says “changed” when nothing did. So the tests in a report are corrected together, which holds the chance of any false alarm across the whole report at 5%.
This is deliberate. Telling a merchant their visibility dropped when it did not is worse than telling them nothing moved, because they will spend money reacting to it.
Mostly in what it refuses to do. The category norm is one question, asked once, reported as a confident percentage with a month-on-month trend. We measured what that method can actually support and published the result: at those sample sizes the smallest detectable change is often larger than the citation rate being measured.
So this asks more questions, repeats each one, and declines to report changes it cannot demonstrate.
There aren't any, because there are no customers yet. This is a first release built on top of three published studies.
We could fill this page with logos and a satisfaction score, as is normal in this category. But the product's whole argument is that measurements should be checkable, and inventing the social proof would refute that argument on the same page that makes it. When there are real users willing to be named, they will appear here.
ChatGPT, Google AI Overviews, Google AI Mode, Gemini and Perplexity, reached through provider APIs and Google's AI Overview results. These are not identical to the consumer apps — personalisation, session history and app-only retrieval differ. We state that rather than bury it, because it bounds what the number means.
No. Every prompt is authored natively in its market's language. Translation measures the translation: German furniture buyers search Nachbau and Dutch buyers say namaak, neither of which a dictionary returns for "replica". A translated corpus quietly measures the wrong query.
Yes, and that is the point. The measurement study and the multilingual audit are both open access with their full datasets — 4,320 observations, both prompt corpora, the code — under CC BY 4.0. You can reproduce the analysis or disagree with it.
One domain, up to three markets, five AI assistants, every question asked repeatedly. Results in a few minutes, and nothing to cancel.
Already measured something? Your report link is permanent — keep it and re-run it whenever you like.
This tool exists because the measurements already on the market did not hold up when we checked them. All three studies are open access, with the full datasets attached.