Your AI product is
probably underpriced
aThereThere builds your GTM intelligence engine from your buyers and the agents they send, so pricing, packaging, and positioning run on evidence.
Software buying is changing faster than software pricing
Buyers still want what they have always wanted: a price that maps to value, and a bill they can predict.
But agents now do the work of teams, and increasingly, the buying itself.
When a buyer delegates the evaluation, your price is weighed against the value an agent can see and verify.
Get misread, and you are compared to the wrong alternatives at the wrong price.
Context-Market Fit is when your model, price, package, and story make you the obvious choice for the buyer and the agent they send.
Find your Context-Market Fit
aThereThere measures the trade-offs your buyers make when they have to choose, not the opinions they offer when they don’t. Those choices feed the engine, and the answers come back decision-grade: a model, a price, a package, and a story you can defend to your board, your buyers, and their agents.
One engine, calibrated on three measurements
Who’d pay the most for this and why?
The outcomes your buyers rank above everything else, the value story that carries a premium, and the price walls around it. Best when there is demand, but you are not sure which buyer, use case, or story makes the offer worth more.
How do they want to pay?
The billing model, meter, and rate buyers can understand and defend. Best when the value is clear but the structure feels wrong and you’re weighing seat, usage, flat fee, tiered, hybrid, or outcome-based.
What belongs in core vs. premium?
The line between core, premium, add-on, and roadmap, drawn with evidence. Best when the buyer and billing unit are settled, but the offer architecture is not.
Who’d pay the most for this and why?
The outcomes your buyers rank above everything else, the value story that carries a premium, and the price walls around it. Best when there is demand, but you are not sure which buyer, use case, or story makes the offer worth more.
How do they want to pay?
The billing model, meter, and rate buyers can understand and defend. Best when the value is clear but the structure feels wrong and you’re weighing seat, usage, flat fee, tiered, hybrid, or outcome-based.
What belongs in core vs. premium?
The line between core, premium, add-on, and roadmap, drawn with evidence. Best when the buyer and billing unit are settled, but the offer architecture is not.
Your place on the shortlist is always moving
Agents interpret you inside the context they have now, not the one you launched into. Three forces keep changing that context.
New capabilities change what agents can understand, compare, and do—and what they expect your product to do.
Every new offer redraws the comparison set, the category, and the price anchors around you.
A feature, package, proof point, or price gives agents a new version of you to interpret.
Willingness to pay and willingness to recommend are moving signals—not launch-day facts. Your GTM intelligence has to move with them.
Start making better calls on price, package, and positioning
Your GTM intelligence infrastructure tracks two signals: willingness to pay from buyers, and willingness to recommend from buyers and AI agents. A three-week calibration builds the baseline; quarterly signals keep both current.
Who’d pay the most for this and why?
How do they want to pay?
What belongs in core vs. premium?
Positioning
Pricing
Packaging
We scope the decision, field real trade-offs with qualified buyers, and establish the baseline: the recommendation and calibrated panel behind it.
Three weeks assumes a reachable audience and a clear question; timing is scoped on day one.
Each quarter—and whenever a model ships, a competitor launches, or your product changes—we re-read how AI agents describe, compare, and rank your offer, then test the right response with the same buyer panel.
Priced by the dimensions your decision needs. Quarterly signals are scoped during calibration.
What a small lift is worth
Pre-revenue? Use the ARR you’re pricing toward.
At this ARR, a 0.33% price lift
covers the $10K starting fee.
At a 10× revenue multiple, a 5% lift adds $1.5M in valuation.
McKinsey puts a 1% price improvement at roughly 11% of operating profit for the average company. ProfitWell found monetization moves growth about four times harder than acquisition.
A few of the views the engine returns, drawn with sample data. Yours are built from your buyers, and go deeper.
What founders ask us first
How is this different from asking buyers what they would pay?
Asking produces opinions, and opinions are polite. Our instruments force trade-offs: buyers choose between real configurations at real prices, and we measure what they pick. That gap between what people say and what they choose is why the output holds up in a board meeting.
When is the right moment to calibrate?
Before a decision hardens: when you are launching an AI feature, setting or raising a price, redrawing a package, or deliberately trading margin for market share. It is also time when customers say yes too easily or pricing is the board slide you cannot defend. Measure the ceiling before you lock in the decision—not after.
Who answers the questions?
Your market, not a rented panel. You bring the audience: a waitlist, target accounts, customers, or the communities you sell into. We design the screener, qualify every respondent against the buyer profile we set at kickoff, and manage the fielding.
We are early and do not have many customers. Does this still work?
Yes, with one requirement: an audience to field to. A waitlist, target accounts, early customers, or the communities your buyers live in all work. If you can reach the people you want to sell to, we can measure what they would pay, and you set your first price with evidence instead of anchoring low and repricing your way out later.
How much of our time does it take?
Two working sessions: the kickoff workshop on day one and the implications session at the end. Plus the outreach, since invitations reach your list under your name. The screening, fielding, analysis, and reporting are on us.
Will you talk to our customers for us?
The calibration measures what buyers choose; talking to them tells you why. We encourage you to take the findings into your own conversations, where you will hear the motivations behind the numbers and confirm them first-hand. If you would rather we run those interviews for you, we can.
What do we walk away with?
Not a slide deck. A recommendation you can act on, plus a calibrated panel of your buyers: segment weights, price walls, structure preferences. Every later decision gets tested against it, through quarterly signals and in rehearsals before your buyers see a change.
What happens after the calibration?
The first calibration gives you a baseline we can return to. Each quarter, we check what has changed: how buyers see your offer, how AI agents compare it, and whether new models, competitors, or product updates have shifted your position. Before you change your price or package, you can test the move with the same buyer panel.
Three weeks to find your number.
A quarterly signal to keep it current.
Start with a quick read on how your offer is being understood, or bring us the pricing, packaging, or positioning decision already in front of you.