How it works
How your queries are generated
You've approved your foundation. Next, we turn it into the set of buyer-intent queries we run across the engines, before your numbers come back. This page shows the shape of that set: where the queries come from, what makes them buyer-intent, and why they stay fixed. We show the shape, not the exact recipe. The example queries are illustrative.
If you've just approved your foundation, your query set is generating now.
A query set is the list of real buyer questions we run against the AI engines to see where you show up. It's generated from the foundation you just approved, it spans the buyer's journey, and it stays fixed so every cycle measures the same thing. Below is the general shape of how we build it, with illustrative examples. The precise dimensions and weighting, the part that makes it work, stay in-house.
From your foundation to your queries
Your queries aren't generic. They're built from the Foundation Review you approved, so they reflect your category, your buyers, and the field you compete in. The foundation supplies the raw material:
That's the reason the foundation was worth reading closely: it's the source every query is drawn from. Get the foundation right and the queries follow.
What we run: buyer-intent questions
Every query is a question a real buyer asks AI while trying to solve a problem or choose a tool. That's the whole point: we measure the conversations that decide shortlists, not the ones where someone already knows your name.
What we run
The questions buyers ask while they're deciding, from problem-level ("how do remote teams keep work on track across time zones?") through head-to-head comparisons, including branded ones like "[Client] vs the alternatives" or "is [Client] right for distributed teams?" These are the moments your visibility is won or lost.
What we don't run
Pure navigational lookups, like "[Client] login" or "[Client] status." Someone searching those has already chosen you and just wants to get somewhere, so they say nothing about whether AI puts you on the shortlist in the first place.
Across the buyer journey
Buyers don't ask one kind of question. They move from naming a problem, to exploring solutions, to building requirements, to comparing and shortlisting vendors. Your query set spans that whole journey, weighted toward the early stages, where buyers first describe the problem and where shortlists get built before most brands ever enter the conversation. That's why your visibility report breaks down by funnel stage instead of landing as a single number.
Tagged so your results can be sliced
Each query carries tags, the persona it belongs to and the journey stage it sits in, so your results can be read by segment instead of in aggregate. That tagging is what powers the by-persona and by-funnel-stage views in your visibility report: not just "how visible are you," but "visible to whom, and at what point in the decision."
Example queries
A small, illustrative slice of what a query set looks like, tagged by persona and stage. These are examples to show the shape, not a real set.
"how do remote teams keep projects on track across time zones?"
"best project management tools for distributed teams"
"what to look for in async project management software"
"track cross-team dependencies without daily standups"
"[Client] vs the category incumbent for distributed teams"
"project management with capacity planning for agencies"
What to notice
Every one is a question a real buyer types while choosing, from someone who doesn't yet know the category exists to someone comparing finalists head-to-head. Some name a competitor, some name you, most name no one. What they share is live buyer intent, the decision is still open. That's the difference between measuring where buying decisions happen and measuring vanity lookups.
Locked: the same ruler every cycle
Once your set is generated, it's fixed. Every audit re-runs the same queries, unchanged, so the movement you see month over month is real progress, not the result of a moving target. If your market shifts enough to need new queries, that happens deliberately, by refreshing your foundation, not by quietly swapping questions between cycles. Same ruler, every time.
Why we don't publish the formula
The shape, not the recipe
You've seen everything that goes into a query set and what one looks like. What we keep in-house is the exact recipe: the precise dimensions, how many queries map to each, and how the set is weighted and assembled. That's the methodology, and keeping it private is part of what keeps your measurement rigorous and hard to game. The shape is what you need to trust the numbers; the recipe is what makes them hold up.
How to read your query set
When your set is ready, you'll be able to see it in your account. There's nothing to approve here: the decision point was your foundation, and the query set is simply that foundation applied. Skim it the way a buyer would, as the real questions being asked about your category, and you'll already know which answers you want to be winning. Those are the answers your visibility report scores, one by one.
What happens next
Your numbers are close behind
Your query set lands within 20 minutes of approving your foundation, and your full visibility report follows within 24 hours, scored across ChatGPT, Claude, Gemini, and Perplexity. If your audit includes the action plan, it arrives with the report.
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Frequently asked questions
What people ask most about how the query set comes together.
Do I approve the query set?
No. The decision point was your foundation. The query set is the foundation applied, so there's no second approval. You'll be able to see your queries in your account, and they're the basis for every score that follows.
Can I add my own queries?
Changes happen by revising the foundation, not by editing the query list. If a persona, competitor, or pain point is missing, fix it in the foundation and the query set regenerates from there. That keeps the set internally consistent.
How many queries, and which engines?
150 buyer-intent queries, run across the four engines your buyers use: ChatGPT, Claude, Gemini, and Perplexity.
Do the queries change month to month?
No. Once generated, the set is locked and re-run unchanged each cycle. That's what makes month-over-month movement real and comparable rather than noise from a moving target.
Are these my real queries?
No. The examples shown here are illustrative, to demonstrate the shape. Your set is generated from your own foundation: your category, your buyers, and your competitors.
Why don't you show the exact formula?
We show the shape, not the recipe. The precise dimensions and how the set is weighted and built are our methodology. Keeping that in-house is part of what keeps your measurement rigorous and hard to game.