Pricing and positioning have always decided who gets chosen
What’s changed is that an AI agent is consulted before the sales call and sometimes it’s the only visitor you’ll get.
The agent is equipped with your buyer’s context and doesn’t want to get its recommendation wrong.
So it reads your choices as evidence for its conclusion: your billing model, tiers, feature mix, the price, everything you decided by design (or not).
A human visitor will give you the benefit of the doubt, AI won’t.
Context-Market Fit is when you are the obvious choice for the buyer and the agent they send.
Being discovered is a visibility problem, but getting recommended is a GTM problem, and it starts with understanding what drives your buyers’ willingness to pay.
So we built an engine for it
A GTM engine calibrated to buyer value.
It learns what your buyers value, tests whether AI recognizes it, and lets you rehearse a GTM change before your market sees it.
We find where willingness to pay peaks.
Mixed-method buyer research identifies the contexts, outcomes, and value drivers that create pricing power.
Then we build a model of what makes the offer worth more.
Buyer evidence becomes a structured model connecting context, desired outcomes, competitive alternatives, pricing power, and required proof.
We test whether AI recommends you there.
We simulate how major AI services classify, compare, and recommend the company in the buyer contexts where value is highest.
And we see what would change the recommendation.
Test how alternative pricing, packaging, billing, positioning, promise, and proof affect buyer value and AI selection.
So you can close GTM gaps.
The engine identifies the changes that strengthen buyer value and AI selection together, giving the team evidence to make critical GTM decisions.
A GTM engine calibrated to buyer value.
It learns what your buyers value, tests whether AI recognizes it, and lets you rehearse a GTM change before your market sees it.
We find where willingness to pay peaks.
Mixed-method buyer research identifies the contexts, outcomes, and value drivers that create pricing power.
Then we build a model of what makes the offer worth more.
Buyer evidence becomes a structured model connecting context, desired outcomes, competitive alternatives, pricing power, and required proof.
We test whether AI recommends you there.
We simulate how major AI services classify, compare, and recommend the company in the buyer contexts where value is highest.
And we see what would change the recommendation.
Test how alternative pricing, packaging, billing, positioning, promise, and proof affect buyer value and AI selection.
So you can close GTM gaps.
The engine identifies the changes that strengthen buyer value and AI selection together, giving the team evidence to make critical GTM decisions.
Start making better calls on price, package, and positioning, so you get recommended for the buying moments that matter
A strategist runs the engine with you, calibrated to the decision you’re actually facing.
Build your Context-Market Model.
Together with your founder, product, or GTM team, we find the decision with the most at stake, choose the right research for it, and gather the buyer evidence.
Identify the buyers who’d pay the most, and why
Discover which outcomes carry a premium, and where the price walls sit
Pick the billing model, meter, and rate your buyers can defend
Craft what belongs in core vs premium
Learn how each major AI agent describes and compares you
Highlight the changes that move buyer and agent behavior
Recalibrate when it matters.
The model persists between runs while lightweight monitoring follows your offer, your market, the competitive set, and the major AI services. You do not re-buy the research; you rerun the parts that moved, when something makes it worth it.
When a model release changes what agents can compare
When a competitor launch redraws your comparison set
When buyer budgets, priorities, or alternatives move
When you change pricing, packaging, positioning, or scope
When you want a change rehearsed before your market sees it
When the answer needs new buyer evidence, not just a rerun
FAQ
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 does this cost?
Calibrations start at $5K and are shaped by the number of segments and the kind of GTM answers you need. We scope it with you in the intro session, so the price matches the decision rather than a package tier.
How long does it take to run a calibration?
About three weeks from kickoff to recommendation. That assumes a reachable audience and a clear decision to calibrate around, both of which we scope on day one. Reruns after that are lighter: the model and the calibrated panel already exist, so we only re-test what moved.
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.