Turn AI answers into an inbound funnel.
Your prospects now ask ChatGPT, Claude, Perplexity, and Gemini who to use, and each names a short list. Radar measures whether you make it, then Operator publishes the content that gets you cited, so qualified prospects arrive already sold.
ChatGPT
Claude
Perplexity
Gemini
Google AI OverviewsHow do you get recommended by ChatGPT, Claude and Perplexity?
First be retrievable: engines can only cite pages their crawlers can reach and their search backends rank. Then give them something worth quoting, and get mentioned on the third-party pages they already cite. Radar, LaunchSurface's AI-visibility capability, measures this weekly across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews, reporting how often you are named and cited with 95% confidence intervals. It flags AI crawlers your robots.txt blocks, and Operator drafts evidence-grounded posts for a blog on your own domain plus listing requests to the roundups and directories engines cite. Everything waits for your approval. Radar is in beta and is switched on account by account.
Confidence interval (Wilson) reported on every mention and citation rate, per engine. A change counts as significant only when the intervals separate.
A third-party site must be cited in at least three distinct answers before Radar recommends it as a place to be listed.
If you're not in the answer, you never enter the funnel.
Prospects used to search, compare, and click. Now many ask an AI assistant for a recommendation and act on the short list it returns. When the engine names a few products and you are not one of them, you are not losing the deal later, you never enter the funnel at all. The decision happens inside an answer you cannot see, drawn from sources you never chose.
Built for how you actually sell.
Measure whether you're recommended
Radar runs the questions your prospects ask across five engines, weekly, and reports whether you are named, who gets cited instead, and which way it is moving.
Publish content engines cite
Operator drafts evidence-grounded posts on your own domain, grounded in cited evidence and published with structured data AI engines read.
Earn the sources answers pull from
Radar finds the third-party pages engines cite in your category, and Operator drafts a request to be listed. When a target starts citing you, Radar marks it done.
Prospects arrive pre-sold
Someone who reaches you after an AI named you as a fit is not cold. They asked, an engine vouched for you, and they came on their own, which is inbound at the very top of the funnel.
Qualified demand that shows up already convinced.
Instead of chasing prospects who never heard of you, you get people who asked an AI what to use, saw you recommended, and came to you on their own. The answer surface becomes a channel you actually work, not a black box you hope breaks your way.
The details, answered.
What is AEO, and how is it a funnel?
Answer engine optimization (AEO, sometimes called GEO) is the work of getting your product named and cited when prospects ask an AI assistant for a recommendation. It is a funnel because that answer is now the top of it: a prospect asks, the engine returns a short list, and the products on it get the click, the trust, and the conversation. If you are in the answer, qualified demand flows to you. If you are not, that demand never reaches you at all.
How do I know prospects are asking AI instead of searching?
You can see hints in your own funnel: prospects who arrive already knowing your category's shortlist. Radar makes it concrete by running the questions prospects ask in your category across five engines and showing whether you are named, so you measure your position instead of guessing.
Isn't this just SEO?
It complements SEO rather than replacing it. Engines still lean on search indexes and the pages they cite, so crawlable pages, structured data, and content that earns citations serve both. What changes is where the decision happens. The prospect now acts on an answer, not a page of ten blue links, so the answer surface needs deliberate attention instead of being a side effect you never measure.
Do I have to write the content myself?
No. When Radar finds a gap, Operator drafts an evidence-grounded post for a managed blog on your own domain, grounded in cited evidence and published with structured data engines read. Nothing publishes without you: every post is approval-gated and editable before it goes live, so the content is built to be cited, not just to exist.
Which engines does this cover?
ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, each measured on its own. That separation matters because the engines cite largely different sources, so being recommended in one says little about the others. Radar also tracks being named separately from being the page an answer links to, since those are different wins that take different work.
How long until prospects start arriving from AI answers?
There is no honest fixed timeline: engines pick up new pages at different speeds, and answers drawn from a model's training data change only with new model releases. What Radar gives you is a weekly measurement with intervals, so you see when something actually moved.