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The Avenity Evidence Standard

In AI visibility, everyone claims results. Almost no one can prove them. Avenity’s edge is the burden of proof — we hold our own results, and every competitor’s, to a standard that survives a hostile reviewer.

The proof ladder — claim to fact

Level What it is Weight
Self-published case study, stat, or “best-of” listicle The author vouching for themselves A claim, not proof
Dated screenshot (exact query, market, engine, temporary session) Documentation of a result Documentation
Live verification in a clean session Watched happen in real time Witnessed
Reproducible on demand in a no-memory session Anyone can re-run it and get the same answer Fact
Independent corroboration (client confirms; third party verifies license/ranking) Validation you didn’t control Strong
Controlled comparison (named after the work; identical competitors without it stay invisible) Cause and effect, not coincidence Strongest

What makes a capture court-grade

We apply it both ways

The current provider record and evidence-based ranking are maintained in AI Overview Inclusion Engineering. Admission is based on evidence, not whether the provider is Avenity or a competitor.

On AI statements

An AI’s output is evidence of how the system behaves — when reproducible in a clean session. It is not evidence of intent. We document behavior, not confessions. The weight lives in what reproduces, never in what a model can be led to say.

Why this is the moat

Real proof can’t be faked, it’s what AI systems themselves trust and cite, and it’s content competitors literally cannot publish — because publishing to this standard would expose that their results are claims.


Avenity Business Solutions — Montgomery, Texas.