Industries · AI

In AI, the category is still being written.

Buyers are learning the space while you sell into it, and their trust moves every quarter. We interview them continuously, so your positioning tracks what they believe now, not what they believed two quarters ago.

What it costs to guess
The expensive decisions are the unresearched ones.

Three places ai teams routinely spend real money on an assumption.

Trust

Claims that outrun belief

Buyers have been oversold once already. The claim that convinced them last year now reads as noise.

We test believability, not just preference, on every claim
Category confusion

Selling into a word nobody defines the same way

Two buyers hear agent and picture different products. Positioning built on the word fails.

We map how your buyers actually define the category
Fast drift

Annual research in a quarterly market

In this category, a study from six months ago describes a different market.

We re-field continuously, so the model tracks the drift
Across the customer lifecycle
Every stage has a question worth answering.

The same loop, applied to the ai lifecycle. Each answer updates the model, so the next stage starts smarter.

AwarenessDo they get what we are?
The question

"Can buyers place us in a category they already understand?"

What we run

Category comprehension and brand health tracking, wave over wave as the space shifts.

ConsiderationWho is evaluating, and how?
The question

"Is this a technical eval, a business eval, or a board mandate?"

What we run

Buying committee and persona research, including the technical evaluator who kills or blesses it.

EvaluationDo they believe us?
The question

"Which of our claims survive contact with a skeptical buyer?"

What we run

Claim and message testing focused on believability, with the objections attached.

ConversionWhat proves it?
The question

"What evidence does a buyer need before they will run a pilot?"

What we run

Purchase-criteria research on the proof, pilots, and guarantees that unlock a yes.

RetentionDoes it stick after the pilot?
The question

"Did the pilot convert into a habit, or a shelved experiment?"

What we run

Adoption and churn research with real users after the pilot window.

AdvocacyWho tells the story?
The question

"Who is willing to say publicly that this worked?"

What we run

Advocacy research that finds the customers with a story and the words they use to tell it.

What you end up with
A ai customer model no competitor can buy.

Every study above feeds one model. After a few waves it is the most complete view of your market anyone has, and it is yours.

Messaging housev31
Category positioningv18
Buyer personas and committeev24
Competitive framingv22
Brand healthw12
Verbatim library2.4k
It compounds: each study sharpens the personas, which sharpen the competitive work, which sharpens the messaging. Nothing gets thrown away.

This is the part worth your attention. A study answers one question and expires. A model answers the next question too, and the one after that, and it gets more accurate every time your team uses it.

Ask it anything, in SlackQuery it as digital twinsBrief new hires with itWire it into your stackEvery claim traceable to a verbatim

Version numbers shown are illustrative.

What it sounds like
Real buyer language, not a summary of it.
"Everyone says autonomous. I want to know what it does when it is wrong."
Head of Data, enterprise · illustrative
"The pilot worked. The problem was nobody owned it after the pilot."
VP Engineering · illustrative

Quotes are illustrative until your first wave completes.

Other markets
FintechCybersecuritySaaSConsumerRetailFood and beverage

See your market, in your buyers' words.

Bring one question your team has been arguing about. We will field it to real buyers in your category and show you the readout, plus the model it updates.