Mentioned started as a one-off checker and is now a tracking surface: frozen prompts re-asked on a schedule, three metrics that disagree usefully, a competitive field built only from brands a model actually named, and a receipt behind every figure. Here is all of it.
The signed-in surface
Every screen below is the real interface, rebuilt in HTML from the same stylesheet the product uses — not a screenshot, so it stays sharp at any size and cannot fall out of date with the palette. The app itself also has a dark theme; these are shown in light. The numbers in them are sample data for a demo account, never a customer’s result.
Visibility, share of voice, average position and runs recorded — then where you land when you are named, and what changed. The second card is the top of the Actions ranking, computed by the same code the Actions screen runs, so the two can never disagree about which finding is biggest one click apart.
How often AI assistants name Pickleland when a buyer asks about the category — measured on frozen questions, so week over week means one thing.
Share of answers naming each brand, last 12 weeks. Days where nothing was measurable break the line rather than being drawn through.
Every scored answer from the last 12 runs of each keyword, by the rank the model gave you. “Not named” is counted here rather than dropped — it is the denominator.
The top of the Actions ranking: how much went the wrong way, in points times the answers it spans.
Claude named Pickleland in 9 of 12 answers; ChatGPT in 0 of 12. Same five frozen questions.
75 pt × 12 answersSee the 12 answers →67% last run, 27% this one, over 15 answers. Nothing else about the keyword changed — the questions are frozen.
40 pt × 15 answersSee the prompt →Named 47 times to your 39 across the most recent run of each keyword, at a better average rank on four of them.
8 namings × 4 keywordsSee the landscape →Sample data for a demo account. The trend window (4W / 8W / 12W) and every filter live in the URL, so a view you are looking at is a view you can bookmark and send to someone.
This is the central design decision, not an optimisation. Most tools rewrite their prompts on every run, which means part of every movement in their charts is the question changing rather than your visibility. Freeze the questions and a score moving 3/5 → 1/5 means exactly one thing.
12 of 25 keywords. Each is a brand and a category, with five questions frozen at creation.
Weekly by default. A keyword can run daily once you have saved your own API key for every model it asks — because a daily run spends that key, not ours.
Your score is an average of five questions, and averages hide the interesting half. This screen breaks it back apart: which questions name you every time, which ones never have, and who the model reaches for instead. A question that has never been asked stays blank rather than reading as a measured failure — “nobody named you” and “nothing has run” are different findings.
Every frozen question across your keywords, weakest first. Visibility is the share of answers that named you, not of runs.
Visibility here is the share of answers that named you, not of runs: a keyword asked on two models produces two answers per question per run.
Three readings of the same field. Times named counts every brand in the most recent run of each keyword, yours included. Reach vs. rank puts volume against placement, so a brand named often but always last is visibly different from one named rarely and always first. Share of voice catches the case where you hold your score but the model starts naming twelve rivals instead of three.
Every brand an AI answer named instead of — or alongside — you, counted from the most recent run of each keyword.
Every brand named across the most recent run of each keyword, yours included and in the darker fill. One hue: bar length already encodes magnitude.
Horizontal: times named, from zero. Vertical: average rank when named, inverted so #1 is at the top. Bubble area is how many of your keywords the brand shows up in.
Of all brand-namings in your answers, who got them. This catches the case where you hold your score but the model starts naming twelve rivals instead of three.
No competitor list is entered by hand. Every brand on this screen is one an answer actually named.
Claude answers on our key. Add your own key for ChatGPT, Gemini, DeepSeek or Llama 3.3 and the same five frozen questions get asked there too — so a gap between columns is a gap between models, never a gap in what was asked. A model at 0% is shown at 0% and never pooled into an average with the others: the spread is the finding.
The same frozen questions, asked of each assistant. Pooling a model that cites you with one that has never heard of you produces a midpoint true of neither — the spread is the finding.
claude-haiku-4-5 · our key · 60 answers read
gpt-4o-mini · your key · 60 answers read
The share of each model’s answers that named each brand. Read the rows: a brand strong everywhere is a different problem from one strong on a single assistant. A model at 0% is shown at 0% and never pooled into an average with the others — the spread is the finding.
| Brand | Claude | ChatGPT |
|---|---|---|
| Pickleland | 84% | 41% |
| Austin Pickle Ranch | 62% | 78% |
| Chicken N Pickle | 44% | 66% |
| Dreamland | 31% | 38% |
| The Picklr | 18% | 27% |
Being invisible on one assistant while leading on another is the commonest and most expensive thing this product finds.
A cue-based reading of the sentence that names you, and the whole classifier is on the screen: the listed words, how often each one fired, and the sentences they decided. It is not a model’s opinion of you and it is not a score out of ten. “No cue found” is shown as a row rather than hidden, because it is the commonest reason a sentence is neutral.
A cue-based reading of the sentence that names you — not a model’s opinion of you, and not a score. Each verdict is a plain count of the listed words shown beside it.
The listed words behind every verdict above, with how often each one decided a sentence. This is the whole classifier — nothing else is consulted. “No cue found” is a row rather than an omission: it is the commonest reason a sentence is neutral.
Each column is one day’s classified sentences, stacked. Column width is fixed; the count underneath is the denominator, so a day with three sentences cannot look like a day with thirty.
Every verdict above is one of these, with the cue that decided it named beside it.
You can disagree with a verdict here and check it in one click, which is not true of a sentiment number a model was asked to invent.
The stored replies, printed verbatim, with your brand and its rivals marked by the same matcher that scored the run — so a highlight can never contradict a score. If a figure looks wrong you can read the answer that produced it, and every check has a shareable receipt at its own URL.
Every stored reply behind the numbers, printed verbatim with your brand and its rivals marked by the same matcher that scored the run — so a highlight cannot contradict a score.
“What are the best indoor pickleball courts in Austin, Texas?”
“Where can I play pickleball indoors in Austin when it’s raining?”
The matcher folds accents and ignores spacing and punctuation, but still requires a word boundary in the original text — without that, “abandoned” scores as a hit for a brand called “Done”.
Six derivations run over the runs already on your account — model blind spots, prompt blind spots, visibility drops, competitors ahead, weak topics, keywords that never ran — and each finding is scored by the gap in percentage points times the stored answers it spans. Every row restates a measurement that is already on another screen, and links to it.
Ranked by how much went the wrong way: the gap in percentage points times the stored answers it spans. Every row restates a measurement already on another screen, and links to it. Nothing here is advice.
Claude named Pickleland in 9 of 12 answers on this keyword; ChatGPT in 0 of 12. The five questions are frozen and identical, so the gap is between the models, not between what they were asked.
75 pt × 12 answersSee the 12 answers →67% on the run of Aug 17, 27% on Aug 24, over 15 answers. Same question, same model, same wording.
40 pt × 15 answersSee the prompt →Named 47 times to your 39 across the most recent run of each keyword, at a better average rank on four of them.
8 namings × 4 keywordsSee the landscape →33% visibility across the 3 keywords tagged Leagues, against 71% across everything else on the account.
38 pt × 45 answersSee the topic →Both have been asked 15 times and scored zero, while other prompts on the same keyword score above 80%.
80 pt × 30 answersSee both prompts →3 further findings ranked below these five and are not shown.
Nothing on this screen is advice. It is a ranking of measurements, and it says how many findings it cut rather than truncating in silence.
The right axis is inverted — rank 1 at the top — so up is better on both lines, and position is dashed because the two series are in different units. Below it: a row per frozen question with its own visibility, average position and rivals, and the stored answers printed verbatim.
indoor pickleball courts in Austin · 5 questions frozen 29 Jun · asked daily on Claude and ChatGPT · 9 runs recorded
The right axis is inverted — rank 1 at the top — so up is better on both lines. Position is dashed because the two series are in different units.
The stored answer the top row was counted from, printed verbatim and marked by the same matcher that scored it.
“Best indoor pickleball courts in Austin, Texas?” — Claude, Aug 24
An unlabelled reversed axis is a trap rather than a chart, so this one says so in words.
Optional, and never required: every weekly run works on our key. Save one and you can re-ask a keyword’s frozen questions on a model we hold no key for, and move that keyword to a daily cadence. Keys are sealed with AES-GCM using a secret that is not in the database, and the page says plainly what that does and does not protect you from.
you@company.com · free plan
Add a key and you can re-ask any tracked keyword’s frozen questions on that model whenever you like, and set that keyword to run daily instead of weekly. Those runs spend your budget with the provider, not ours — which is exactly why daily is available with a key and not without one.
Keys are encrypted before they are written down, with a secret that is not in the database — so a copy of our database on its own gives up nothing. Be clear about the limit of that: this site can decrypt what it stores, so it protects you against our database leaking, not against us. If that is not a trade you want, leave this page empty — every weekly run still works on our key, and one-off checks let you paste a key that is used for that request and never written down.
A result from your own key gets its own receipts page and is deliberately not spliced into the weekly trend — that line is one model answering five frozen questions, and it stays that way.
A dashboard is mostly small choices about what to do when a number is missing. Here are the ones that shaped this one.
The section you are on, the brand and topic filters, the model filter and the trend window are all URL state. That is what makes a view bookmarkable, reloadable and sendable — and it is why the dashboard renders the same for a reader with scripts blocked as for everyone else.
“Not measurable” and “measured, and zero” are different findings and this product never prints one as the other. A bar at 0% renders as a bare track rather than a sliver of ink, because a mark where nothing was measured reads as a small result instead of no result.
The export carries whichever brand and topic filters you are reading, correctly quoted, so the file matches the screen rather than the whole account.
One timed-out request used to leave a keyword idle until the following week. Failures now retry at the next sweep and then back off — and only the requests that can clear on their own are ever re-sent.
Raw IP addresses are never stored. A key you paste into the one-off checker is used for that request and thrown away; a key you save is encrypted with a secret that is not in the database.
A tour that only lists strengths is a brochure. These are the limits as they stand today.
Which sites an assistant leaned on when it answered would be the tenth view, and it is deliberately not shipped: nothing in the product stores a per-citation domain today. A sources screen built from anything other than a link a model actually returned would be the one screen here that estimates.
They are generated, not looked up in a ranked list. That is why a keyword asks five questions with three samples each rather than one question once, and why the trend is the number worth reading rather than any single run. It is also why the questions are frozen: it removes the one source of variance we control.
It counts listed praise and criticism cues in the sentence that names you, and shows you every cue that fired. It will miss sarcasm and it will miss praise phrased in words that are not on the list. Showing the whole classifier is the compensation: you can see exactly why a sentence was called what it was called.
There is no outbound mail anywhere in the product, so nothing here promises to send you anything. When that changes, this page changes with it.
5 checks a day per account, 25 tracked keywords, weekly sweeps on our key. Every run spends real API budget, and the unattended sweep is capped in dollars per month — a keyword we cannot afford this month is deferred rather than failed, because a red mark on a healthy keyword would be a lie about the keyword.
A free account gives you 5 checks a day and up to 25 tracked keywords. No card, and no anonymous check — every check spends real API budget, so it has to belong to someone.