Upstream Zero

For companies

Find out why AI is not recommending you.

Deals are being decided in evaluations you never see, before your team is in the room. If an AI system cannot confirm what you do, it does not recommend you, and you never find out why. We measure what is actually happening and explain what caused it.

See the research

What a project answers

Four questions, answered with evidence.

  1. 01

    How you are evaluated today

    How AI systems currently describe, compare, recommend, and rule out your company.

  2. 02

    Why that result happened

    The buyer requirements you are judged against, the proof you are missing, and the ways competitors differ that shaped the result.

  3. 03

    What would have to become true

    The single most important change to test if you want to become a more obvious choice.

  4. 04

    Whether anything moved

    Repeated measurement to see whether the list AI recommends changed, and whether that change held up the next time.

The rule behind all of it never moves. We measure and diagnose. We never promise to change what an AI system does. Our projects deliver evidence and analysis, never a promised result, and the site is built so that a promised outcome has nowhere it could even be written down.

What we can measure today

Every capability is still experimental, and we label it.

The site itself refuses to call a capability ready until it comes from published method. That is not a promise, it is a build error.

What a project produces

Evidence you can inspect before you buy.

ENG-1active

Evaluation Audit

Organizations that want to know how AI evaluators currently assess, recommend, validate, or eliminate them

ENG-2provisional

Requirement and Evidence Gap Analysis (provisional)

Organizations whose capabilities are real but unverifiable by a machine reader

ENG-3provisional

Evaluation Stability Measurement (provisional)

Organizations making decisions based on single-shot AI evaluation results

ENG-4active

Selection Tracking

Organizations that need to know when evaluator behavior about them changes

ENG-5provisional

Machine Representation Advisory (provisional)

Organizations building the structured, verifiable account of themselves that machine evaluators consume

ENG-6provisional

Research Partnership (provisional)

Organizations willing to contribute anonymized evaluation data to the research program in exchange for early access to findings

ENG-7active

Category Report

Organizations that want to understand how AI systems define and evaluate their category

What each project delivers is public before you buy: Evaluation Observation Report (specification draft).

Open founder decision
Project names, scope, and pricing are still provisional until the founder confirms them. Nothing can be booked until that decision is made.

Measured outcomes

There are no testimonials on this site.

N = 0

No measured outcomes yet. Client results will be published here as evidence, labeled by how sure we are, with the client’s consent, as they are measured. This is what replaces a wall of logos. It is slower, and it is worth more.

Start with your category, not a pitch.

A first conversation covers your situation, what we can measure now, and whether we should work together yet. Sometimes the honest answer is not yet.