Methodology
How do you know why AI recommends one company instead of another?
We start with your buyer’s problem and the requirements it creates. Then we test your company against the questions real buyers ask AI, add those requirements one at a time, and watch exactly where the recommendation holds or collapses. That shows us how AI reads your fit, why competitors get put forward instead of you, and what would have to become true for AI to recommend you.
The five steps below are how we do it, the same way every time. The runs themselves live in the research library.
The process
Five steps, the same every time.
- 01
Understand the buyer problem
We begin with the category, buyer, competitor, or selection outcome your leadership team needs to understand.
- 02
Observe the initial recommendation
We determine how major AI platforms interpret the problem, compare alternatives, and form an initial shortlist.
- 03
Apply requirement pressure
We introduce the security, integration, scale, geography, budget, and operational requirements a real buyer adds as the evaluation advances.
- 04
Observe how the recommendation evolves
We identify the exact follow-up where you are removed, displaced, or remain the obvious choice, and which requirement got a competitor put forward in your place.
- 05
Prioritize evidence-based decisions
We translate what we observed into a ranked set of decisions: what would have to become true for AI to recommend you, what matters most, what to test next, and what the evidence does not yet support.
The method, shown
A recommendation is built one requirement at a time.
- 01
Initial buyer question
“What is the best customer data platform for a healthcare organization?”
- 02
Initial recommendation
A broad set of enterprise CDPs, before any real requirement is introduced.
- 03
Requirement follow-up
The buyer adds detail: their EHR, where data already lives, who builds audiences, batch or real time.
- 04
The set narrows
Platforms that do not fit the stated architecture drop out.
- 05
More requirements
Compliance, data residency, and activation targets are introduced.
- 06
Final recommendation
It reflects the buyer's complete list of requirements, not the opening question. The recommendation evolved because the requirements did.
Illustrative example, shown to explain how AI narrows the list it recommends. It is not a record of a specific evaluation and names no companies as an observed outcome. Real, condition-specific runs live in the research library.
What the method does not do. It does not tell you how to game an evaluator, and it does not promise a result. It records what happened under stated conditions and turns it into decisions you can act on. Evidence before opinion.