Business question
Why did we disappear after follow-up questions?
Because an AI evaluator builds its recommendation in stages. You can sit in the opening set for a broad category question and then be removed the moment the buyer adds a requirement your evidence does not clearly satisfy. The removal is usually specific and repeatable: one requirement, at one step, dropping the vendors that do not visibly meet it.
The problem
Most companies only see the final answer, not the sequence that produced it. So a loss looks like bad luck rather than a precise elimination at a requirement they could name and address.
Why it matters
If you cannot see the requirement that removed you, you cannot fix it. Budget goes to broad visibility work when the real problem is a single requirement where the evaluator is not confident you qualify.
What the research studies
The components behind this question.
The evidence so far
What we have run, and what we have seen.
- E-045Video conferencing software: When the field survives and keeps its names
- E-041CRM software: When the recommendation set survives but its members do not
- E-043Accounting software: When the whole field is wiped out and replaced
- E-044Accounting software: When the same kill repeats on a second evaluator
- E-034Clinical trial management software: When the recommendation set collapses
- E-040Project management software: When the recommendation set holds
- E-002Healthcare CDP and the HIPAA flip: the program's one causally verified gate
- Observation · Candidate · 2026-07-20Survival depended on the category, in at least four ways
- Observation · Candidate · 2026-07-20Survival appeared in two modes, and their cause is confounded
- Observation · Candidate · 2026-07-20The same elimination appeared on two different evaluators
What we can conclude
Across five categories we have observed the field narrowing as requirements are added, and the vendor leading the opening list is frequently the first removed at a requirement it cannot satisfy. In one category the entire named field was eliminated at a single requirement and the recommendation relocated to a higher market tier.
What remains unknown
We cannot yet say which property of a category predicts who survives, and most of these runs used a single evaluator, so the exact drop-off point should be measured for your category rather than assumed.
How Upstream Zero helps
From the evidence to your answer.
Upstream Zero runs your category through the same kind of requirement sequence a buyer would, identifies the exact requirement where you drop out, and diagnoses the evidence gap behind it. That diagnosis is measurement, not a promise about rankings.