KnitKnot

# What does the AI Presence Score tell you?

The AI Presence Score is a benchmark summary, not an outcome promise. Read it with the captured answers, claim evidence, journey metrics, and reporting floor.

## What is the AI Presence Score?

The AI Presence Score is a composite summary of how the benchmark represented the company. It combines several structured signals from the captured answers, including coverage, competitive outcomes, claim accuracy, sentiment, source evidence, and confidence markers.

It is written at scoring time from the same records used by the report drill-downs. The score is not a separate model opinion layered on top.

## What should you read under the score?

Start with the captured answers. Then inspect material claims, competitive outcomes, misrepresentations, available sources, and the journeys where the pattern appears. That decomposition explains which part of the representation needs attention.

A topline score can move while one important journey gets worse. The journey record is the useful operating grain for the managed cycle.

## Is a later score change proof of the content result?

No. The default statement is changed, then measured movement. Other pages, competitors, model updates, retrieval changes, and normal answer variation may also be present.

A qualifying holdout can permit a narrower causal statement for the contrast it supports. Headline workspace and journey rates remain association-grade.

## What happens when the sample is thin?

KnitKnot applies a reporting floor before printing a delta. A journey or cycle below that floor is labeled insufficient data. The state is shown even when a directional result would make a better story.

## How does the score fit competitive defense?

The score is the overview. The benchmark underneath it keeps visibility, head-to-head outcomes, material claims, and source evidence separate. KnitKnot preserves that before-state and connects the team's response to the next comparable run.

Read the full [measurement methodology](/methodology/).

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Guide

What does the AI Presence Score tell you?

The AI Presence Score is a benchmark summary, not an outcome promise. Read it with the captured answers, claim evidence, journey metrics, and reporting floor.

A frozen baseline and later observation arranged as a measured evaluation window

What is the AI Presence Score?

The AI Presence Score is a composite summary of how the benchmark represented the company. It combines several structured signals from the captured answers, including coverage, competitive outcomes, claim accuracy, sentiment, source evidence, and confidence markers.

It is written at scoring time from the same records used by the report drill-downs. The score is not a separate model opinion layered on top.

What should you read under the score?

Start with the captured answers. Then inspect material claims, competitive outcomes, misrepresentations, available sources, and the journeys where the pattern appears. That decomposition explains which part of the representation needs attention.

A topline score can move while one important journey gets worse. The journey record is the useful operating grain for the managed cycle.

Is a later score change proof of the content result?

No. The default statement is changed, then measured movement. Other pages, competitors, model updates, retrieval changes, and normal answer variation may also be present.

A qualifying holdout can permit a narrower causal statement for the contrast it supports. Headline workspace and journey rates remain association-grade.

What happens when the sample is thin?

KnitKnot applies a reporting floor before printing a delta. A journey or cycle below that floor is labeled insufficient data. The state is shown even when a directional result would make a better story.

How does the score fit competitive defense?

The score is the overview. The benchmark underneath it keeps visibility, head-to-head outcomes, material claims, and source evidence separate. KnitKnot preserves that before-state and connects the team’s response to the next comparable run.

Read the full measurement methodology.