See how ChatGPT and Gemini describe your brand right now. Run hundreds or thousands of your customers' real prompts, in your language and your market, and watch how often you get named, how you get described, and who gets named instead.
You are already accountable for how the brand is perceived. AI answers are now part of that perception, and until now they were the one channel nobody could report on.
Share of voice in AI answers, tracked like any other audience metric. You can show the trend, name the competitor taking your place, and point at the exact work that moved it.
Placements used to end at the clipping. Now you can show which outlets feed the answer, whether a campaign changed how you are described, and which publications shape your category without ever mentioning you.
Six numbers that answer the questions your board actually asks: are we showing up, how often compared to them, how early in the answer, and does the model speak well of us.
Where the answer came from.
The domains and the exact URLs the models pull from when they answer for your category. That is your real earned media map, ranked by how often it feeds the answer.
Slice any of it by brand, topic, model, period, and label, and cross filter as you go. The question is never "what is our score", it is "where are we losing, and to whom".
You are connected to the live models, not to a monthly extract. Run hundreds or thousands of prompts, repeat each one as many times as you need, and do it again next week to see what moved.
Your whole category, every persona, every way a customer might phrase it. Prompt discovery proposes them, you approve what enters the corpus.
So you read stability, not luck. A brand named nine times out of ten and a brand named once out of ten have very different problems.
Ask them live with web search, simulate the path a real customer walks, or read what a model believes about you with no web search at all. That last layer moves the slowest and costs the most to ignore.
Every project carries its own location and language: fr-CA, fr-FR, en-CA, en-US. A brand can be first in Montreal and absent in Paris.
You stop guessing which question matters. The full category gets asked, so a segment where you are invisible shows up as a number instead of a hunch someone raises in a meeting.
You know whether a mention is reliable or a fluke. That is the difference between telling your board "we are named nine times out of ten" and telling them "we saw it once".
You measure the answer your customer gets today. Ship a campaign on Monday, see whether the models absorbed it by Friday, and cut the work that changed nothing.
You defend budget market by market. Being first in Montreal and absent in Paris is a plan, not a mystery.
You learn how you are characterized, not just whether you appear. Named as the expensive option is a positioning problem you can brief a team on.
You get a target list. The domains a model already trusts for your category, ranked, including the ones that never name you. That is where PR and content spend goes next.
You see the market, not just yourself. When someone takes your place in an answer, you know who, where, and from which week.
You spend your time on the decision instead of the deck. Same data, ten sections, in the language of the room you are walking into.
Most AI visibility reports are written by a model. Ours is written from your collected data, and it would rather say nothing than fill a gap.
Comparing two periods will not run if the model coverage or the scope changed in between. Another tool compares anyway and hands you a variation. This one hands you an error. Less pleasant, more honest.
You live in the dashboard. Once a month, one click turns the same data into something a client or an executive committee can read on its own.
You can look once. You cannot run a thousand prompts, repeat each one, split the result by city, language, and model, or prove next month that something moved. A look is an anecdote. This is a measurement.
SEO measures your spot in a list of links. There is no list here. There is an answer, the brands named inside it, and the domains the model went and fetched to build it. The coverage gap, domains answering for your market that never name you, has no SEO equivalent.
A number missing from the data cannot be written. A quote missing from the answers cannot be written. The methodology sits right in the dashboard, so any claim traces back to the run that produced it.
They will. That is why model coverage is an explicit setting, and why period comparison refuses to run when it shifts rather than quietly comparing two different things.
The product measures and diagnoses: where you are absent, how you are described, which domains feed the answer. The fix is editorial work, and it starts from this data.