Prompt Demand Without the Billion-Conversation Dataset

You do not need a proprietary dataset of a billion AI conversations to estimate what buyers actually ask AI engines. You can triangulate prompt demand honestly from three public, verifiable signals: the questions Google already surfaces through People Also Ask and autocomplete, your own Search Console query data, and the real questions people post in communities like Reddit and Quora. The one rule that keeps it honest: label the result as triangulated demand with its sample and its date, never as a precise search-volume number. This post explains why the public signals are enough to act on, what a proprietary dataset genuinely buys that they cannot, and how ClarAI assembles the three signals without inventing a single figure.

What a billion-conversation dataset actually buys

Credit where it is due. Profound's Prompt Volumes is a proprietary demand dataset the company describes as drawn from more than 1.5 billion real AI conversations, alongside a conversation explorer over hundreds of millions more. Those figures are Profound's published claims, not numbers we have verified, but the shape of the asset is real and genuinely powerful. A direct sample of real AI conversations sees demand that a public-web proxy can only infer. If you want to know how often one specific phrasing is actually typed into an AI assistant, a large first-party conversation corpus is the closest thing to a direct reading, and triangulation from public signals cannot fully match it.

So this is not a claim that the public route is strictly better. It is a narrower claim: the public route is honest, verifiable, and enough to act on, for the large majority of teams who will never own a billion-conversation dataset of their own. The question is not which method is purer. It is whether you can estimate demand credibly from signals you can actually inspect, and you can.

Signal one: the SERP already tells you what buyers ask

Google's search results page is a standing record of buyer questions, and two parts of it are especially useful. People Also Ask is the four-question accordion Google shows for a query, and each question is an explicit buyer-intent signal you can target directly. Autocomplete is the dropdown that appears as you type, a close mirror of the queries Google sees at sub-keyword granularity. Read together across a topic, they map the questions a category actually generates.

ClarAI reads both. Its SERP intelligence service captures People Also Ask, autocomplete suggestions, and the AI Overview box for a query through a search API, exposed on the intel endpoints the app calls. That gives you the observable question layer around a buyer topic without guessing: the actual questions Google is fielding, captured on the day you looked. It is a proxy for AI demand, not a direct read of it, but it is a proxy you can point at and defend.

Signal two: your Search Console is first-party demand

The second signal is the one most teams already own and underuse: Google Search Console. Its query-level performance data is a first-party record of the exact phrasings that brought people to your site, with impressions and clicks attached. That is not a proxy for someone else's audience, it is your demand, measured against your own pages.

ClarAI connects to Search Console and pulls query-level performance directly, through the same search analytics query the console itself runs, keyed on the query dimension. Where People Also Ask and autocomplete tell you what the category asks, Search Console tells you what your buyers already ask on the way to you. The two are complementary: the public SERP widens the map, and your own query data confirms which parts of it convert to real visits from real people.

Signal three: real questions in real communities

The third signal is where buyers ask each other, often before they know your brand exists. Reddit threads and Quora questions carry the unpolished version of a buyer's problem, in their own words, and AI engines cite these communities heavily when they compose answers. A question that recurs in the right community is demand evidence that never shows up in a keyword tool.

ClarAI's community scanner discovers the communities that matter for a project and mines the real questions inside them. It searches Reddit and Quora for threads where the category's keywords appear, scores them by engagement and recency, and classifies each by buyer-journey stage, so a decision-stage question is not lost in a sea of idle chatter. The output is real threads with real URLs, not a synthesised score standing in for them. You can open the thread and read the question yourself.

How ClarAI triangulates, and where it refuses to guess

Put the three signals together and you have triangulated demand: several independent public readings pointing at the same buyer question. ClarAI assembles exactly this. Its demand-evidence surface gathers the People Also Ask and autocomplete rows, the Search Console query rows when a site is connected, a Google Trends velocity reading, and the matching community threads into a single envelope, and it marks every source as present or missing so a thin sample can never masquerade as a full picture.

The honesty contract is the important part, and it is enforced in code rather than promised in copy. The per-product rollup reads measured:false until a real run has actually measured that product, and it never invents a search-volume number. Its own disclosure says as much: the panel shows evidence rows, not a search-volume estimate. A source that is unconfigured, exhausted, or failing moves into a missing list rather than being quietly filled with a plausible guess.

• People Also Ask and autocomplete rows, as captured on the day of the run.
• Search Console query rows, present only when the site is actually connected.
• A Google Trends velocity reading for direction, not a volume figure.
• Matching Reddit and Quora threads, linked so you can read the source question.
• An explicit list of which sources were available and which were missing.
• No synthesised search-volume number anywhere in the envelope.

Label it triangulated demand, with its sample and its date

None of this works if you present it dishonestly. The failure mode of every demand tool is the confident single number: a search volume with no sample, no source, and no date, sold as precision it does not have. Triangulation earns its credibility by refusing that move. Report what you found, from which signals, over what period, and stop there.

So the honest output is not a tidy 700 searches a month with a decimal point. It is a labelled read: this question surfaced in People Also Ask, appeared in your Search Console with these impressions, and recurs in these three community threads, captured on this date. It is a smaller claim, and a true one. A buyer can act on it, an analyst can audit it, and it never has to be walked back when the underlying engines shift, because it never pretended to be a fixed measurement in the first place. That is the trade a triangulation approach makes on purpose: less false precision, more that you can actually stand behind.