Your buyers ask ChatGPT, Perplexity, Claude and five other engines which product to choose. Those answers build the shortlist before anyone talks to sales, and you never get to see them. GrackerAI runs your prompts across all eight engines, reads every answer in full, and tells you the moment your name slips.
No credit card required. First monitors run in about a minute.
A Google ranking sits still long enough to look at. An AI answer is written fresh for each person, pulls from sources you did not choose, and hands back a recommendation instead of a page of links. If your name is missing, no rankings report will tell you. The first sign is a quiet quarter that nobody can explain.
Every answer happens inside a private chat. Without something reading those answers for you, your most influential sales channel is a room you are not allowed into.
By the time a drop in AI visibility reaches your pipeline numbers, the competitor who was watching has already banked a quarter of citations you now have to win back.
People treat an AI recommendation as advice, not an ad. When it names a competitor and not you, that trust transfers to them, often before you are even on the list.
Good monitoring stays quiet until something changes. Then it tells you which engine, which buyer, which market, and what moved.
A monitor is not a keyword lookup. It asks a real buyer question, the way a buyer would ask it, then takes the answer apart. Here is the path every run follows before anything reaches your dashboard.
Send your prompt to the engine as a genuine buyer query.
Repeat across 8 engines, by persona and by region.
Store the whole answer word for word, not a summary.
Pull six signals out of every answer automatically.
Check against history and alert you if something moved.
One prompt goes in. A stable, checked, comparable result comes out, every cycle.
The challenge is not asking a question once. It is asking it well, thousands of times a day, across engines that were never built to be measured. Most of them have no monitoring API, so we query them through the same surfaces a real person uses, while handling rate limits and session context so the answers stay representative.
The deeper problem is that language models are non-deterministic. Plainly put, ask the same question twice and the wording, the order of brands, even which sources get named can change. A single answer is closer to one response in a poll than a fact. So we run each prompt many times and combine the results. When your dashboard says you moved from second to fourth, it means the pattern shifted, not that one sample happened to wobble.
Most tools watch ChatGPT, maybe Perplexity, and call it done. Your buyers are not that tidy. A security researcher lives in Claude. An enterprise team defaults to Copilot. A developer audience leans on Grok and DeepSeek. A gap in any one of those is a gap in what you know.
GrackerAI tracks all eight engines that shape B2B buying, so a blind spot in one place never quietly becomes a hole in your strategy.
The same product looks different depending on who is asking and where they are. So one prompt is not one check. It multiplies across every engine, every buyer persona, and every market you care about, and each combination gets captured on its own.
And because every answer is sampled more than once, the real number behind the scenes is larger still.
Running that many queries on a schedule, inside each engine's limits, without the answers degrading, is a logistics problem most tools avoid by simply checking less. We built the queue and sampling system so coverage stays wide and the data stays fresh, rather than trading one for the other.
Knowing you ranked second tells you almost nothing useful. Were you praised or hedged? Which source convinced the model? Did it get a fact wrong? So every time a monitor runs, we keep the full answer and pull six things out of it that actually explain your visibility.
Reading the answer is its own challenge. The text is ordinary prose, so the system has to recognise your brand even when it is misspelled, shortened, or buried inside a sentence about something else. It tells a real mention apart from a word that just happens to match, links each competitor to the right entity, and judges tone the way a careful reader would. That is the gap between counting a word and understanding a sentence.
Ask "best DLP tools" as a CISO and you get one set of names. Ask as a hands-on analyst, or from Germany instead of the US, and the list changes. An average across all of them hides exactly the gap you need to fix.
So GrackerAI runs separate monitors for each persona and each market, across 40 countries and more, down to city level. You see precisely which audiences an engine shows you to, and which ones a competitor owns.
Engines invent things. Wrong pricing, a feature you do not offer, a rival's capability credited to you, the wrong founder. To a buyer in the middle of research, a confident mistake reads exactly like the truth.
GrackerAI checks what each engine says about you against a verified profile of your product, then flags anything that drifts from the facts — with the claim and the correction side by side. You review a short list of real problems instead of reading every answer yourself.
Not every signal deserves the same attention. Your brand and top rivals are checked daily so you catch a shift inside a day. Category and evergreen prompts run weekly or monthly, which keeps the trend lines clean and the noise down.
When something does move, the alert finds you where you already work. A drop, a souring tone, or a new competitor citation lands in Slack, in an email digest, or in your own systems through a webhook.
| A typical AI monitoring tool | GrackerAI |
|---|---|
| Watches three or four engines | Watches all eight that B2B buyers use |
| Saves a summary or a single score | Keeps the full answer, word for word |
| Reports one blended number | Runs a monitor per persona and per region |
| Reads one reply and trusts it | Samples repeatedly so the number is stable |
| Cannot tell when AI gets you wrong | Flags wrong claims about your brand |
| Waits for you to open a dashboard | Sends the alert to Slack, email or webhook |
Two moments where being first to know is the difference between owning the answer and chasing it.
The advisory that AI cites in the first 48 hours tends to own the story. Here is how teams use monitoring to get there first.
Monitoring across ChatGPT, Perplexity and Claude shows exactly where to act.
"AI-optimized content positioned us as experts in our niche. The results have been game changing."
Once you can see every answer, for every buyer, in every market, AI visibility stops being a mystery you account for later and becomes a channel you actually manage.
Build the library of buyer questions worth watching, grounded in real demand and ranked by what you can win.
Track every rival mention and see exactly where they are earning citations you are missing.
Turn what monitoring finds into a ranked list of fixes, so the gap you spot today becomes the work you ship tomorrow.
Get your free AI Visibility Score in about a minute. See exactly where you stand, where competitors are beating you, and the ranked fixes to get into the answer. No credit card, no commitment.
Free in about a minute. Trusted by 500+ B2B and security teams.
