Find out what ChatGPT tells your buyers about you.
A growing share of your category research now happens inside an AI assistant, and nothing in your analytics shows it. We poll the four models your buyers actually use with the questions they actually ask, record every brand mentioned and every source cited, and turn the gap between you and the sites getting cited into work you can ship.
The answer engine is picking your shortlist, and you cannot see it.
When someone asks an assistant "what should I use for X?", the reply names three or four products and cites a handful of sources. That reply is the shortlist. It is generated fresh each time, it never appears in Google Analytics, it leaves no referrer, and it is assembled from pages you almost certainly did not write. Most teams find out they are missing from it by accident, months later, from a prospect who mentions it in passing.
A measurement loop, not a screenshot.
Anyone can ask ChatGPT about your category once. The value is in asking four models the same set of buyer questions every day, attributing every citation, and wiring the result into the channels that change the answer.
Four providers, every day, same questions
ChatGPT, Claude, Gemini, and Perplexity are polled on each of your tracked queries on a daily cycle, and providers run independently so one bad API key or rate limit never blanks the whole run. You get a mention matrix of query by provider, plus a mention-rate trend rather than a single anecdote.
Competitor share of voice, per model
Every domain the models cite is counted and attributed against your competitor set and your own domains, producing a share-of-voice table with a per-provider breakdown. You can see that a rival owns Perplexity while you hold your own in Claude — which is a different problem with a different fix.
Citation gaps, mined not guessed
When two or more competitors show up in at least a fifth of the runs for a query and your own cite rate stays under that, it is flagged as a competitor-owned gap with the citations that prove it. We also pull question-shaped headings out of the pages that do get cited, so the gap arrives as a specific thing to write.
The Reddit citation bridge
The models cite Reddit constantly. When one of them cites a thread while answering a question about your category, we resolve the post and drop it straight into your Reddit match queue with the query that surfaced it, and notify you. Those are the exact threads shaping what the models say — and you can reply in them today.
A GEO score on every page you own
Each page is scored out of 100 on the structural features that get content quoted: length, FAQ blocks, comparison tables, outline depth, and schema.org markup. Run it across your whole inventory and you get a ranked list of pages to rewrite instead of an opinion about what LLMs like.
Cross-referenced against Search Console
Your ranking Google queries are matched to your tracked AI queries by embedding similarity, so paraphrases still line up. The output is the list that matters most: pages that win on Google and get ignored by the models — your highest-leverage rewrites, because the authority is already there.
Describe your product once. The loop runs daily.
We build the BRAIN
Onboarding produces a structured profile of your product, positioning, ICP, niche, and competitor set. Everything downstream reads from it, and you can edit it at any time. Your competitor list in particular is what makes share of voice meaningful.
Buyer questions get generated, not brainstormed
We generate long-tail conversational queries a prospect from your ICP would actually type into an assistant, spread across tool roundups, comparisons, how-to, best-practice, and troubleshooting intent. Each one comes with the reasoning behind it. Edit them, add your own, deactivate the ones that miss.
Four models get polled daily
Every active query goes to ChatGPT, Claude, Gemini, and Perplexity on a daily cron. Responses are cached and stored, so you get history rather than a live lookup you have to re-run to remember.
Mentions detected, citations classified
Each response is scanned for your brand and every cited URL is resolved to a domain and classified as yours, a competitor, Reddit, or another source. That is what feeds the mention matrix, the citation leaderboard, and the share-of-voice table.
Gaps get mined and queued
A miner reads the accumulated runs for competitor-owned queries and pulls question-shaped headings from the pages winning them. Reddit citations are pushed into your Reddit queue in the same pass, with a notification.
Your pages get scored and re-tuned
A whole-inventory pass fetches every published page and scores its structure out of 100. Ninety days of citation history is aggregated into patterns — which page templates get cited, and the length and structure percentiles of the pages that do.
A digest lands every Monday
New gaps, suggested rewrites, and fresh citations from the week, summarised in one place, so the loop produces a to-do list instead of another dashboard you have to remember to open.
A number for the channel nobody else can measure.
Everything that ships with the channel.
The questions people actually ask.
Which AI models do you track?
What is GEO, and is it different from SEO?
Where do the tracked questions come from?
How do I actually get mentioned more often?
How does the Reddit citation bridge work?
How is this different from Profound, Peec, or Otterly?
How long before the numbers mean anything?
Do I need to connect Search Console for this?
Find out what the models say about you.
We are onboarding a small group of B2B SaaS teams. Drop your email and we will reach out when access opens.