The Recommendation
Readiness Hypothesis
Recommendation Readiness measures how easy it is for an AI assistant to justify recommending your business.
Not find. Not rank. Not crawl. Justify. That changes everything, and the whole product is built around it.
AI assistants don't pick the best company. They pick the one they can most confidently recommend.
When an AI assistant recommends a supplier, it isn't choosing the “best” company. It chooses the company it can build the most confident, evidence-backed recommendation for.
The product doesn't measure quality. It measures recommendability.
Recommendation Readiness is the probability that an AI assistant can confidently recommend your company when a buyer asks a relevant question.
Every AI recommendation comes from three things working together.
Discoverability
Can the AI find evidence about you?
If not, you don't exist.
Answerability
Can the AI answer a buyer's questions from the evidence it found?
If not, it lacks confidence.
Verifiability
Can the AI justify recommending you?
If not, it recommends someone else.
Every score in your audit rolls up to these three principles.
Every recommendation follows the same path.
Each score in the report shows where this chain breaks for your company.
Companies don't disappear because they're bad.
They disappear because one of four things failed. This is the diagnostic behind every recommendation we make.
Evidence has four dimensions.
Did we find it?
Can AI retrieve it?
Does it answer buyer questions?
Would an AI rely on it?
Every piece of evidence in your audit is scored on these four dimensions. Any recommendation you receive can be traced back to specific evidence that passed or failed on one of them.
Every audit tests four assumptions.
A buyer asks a realistic question.
The AI retrieves publicly available evidence.
The AI synthesises an answer using that evidence.
The company recommended is the one supported by the strongest accessible evidence.
From truth to recommendation.
Break any link and the recommendation weakens. The audit shows which link failed and how many Recommendation Readiness points you'd gain by fixing it.
The likelihood of an AI recommending a company is proportional to the quantity, quality, accessibility and consistency of independently verifiable evidence available to answer buyer intent.
That's a testable hypothesis. Our software is the apparatus for testing it, one company and one buyer question at a time.