KnitKnot
You are reading the agent-optimized layer of this page: the literal markdown we serve to AI crawlers and assistants, shipped in the page source of every visit. KnitKnot finds the claims AI repeats about a company, traces them to their sources, and helps the company correct the record.

# Read your report

What the AI Presence Score means, how to read wins and losses, and how to drill from a headline number to the exact AI response behind it.

After a run completes, your report is the summary of everything the four engines said. Every number in it drills down to the underlying answers : nothing is a black box.

## The AI Presence Score

The headline is a 0–100 composite of how favorably AI represents you in its answers to buyer questions: how often you show up, how you fare head-to-head, whether what AI says about you is accurate, and how you are framed. It is designed to be tracked over time; the trend across runs matters more than any single reading.

Two supporting numbers to know:

  • - **Visibility** : the share of answers where you were mentioned at all (excluding questions that name you directly, which would inflate it).
  • - **Win rate** : of the head-to-head comparisons where the AI picked a vendor, how often it picked you.

## What AI tells buyers

This section is the qualitative core: the recurring claims AI makes about your company, with the captured quotes and engines behind them. Claim conclusions use available company facts, receipts, and validation gates; missing evidence remains unverifiable rather than being treated as false.

## Competitive results

Head-to-head outcomes are broken down per competitor and per capability: where you win, where you lose, and what the AI said when it picked the other vendor. A loss row links straight to the responses behind it, so you can read exactly how the AI justified the pick.

One nuance: if AI recommends one of your own products over another, that's a win for your brand, not a loss : the scoring knows your product family.

## Sources

When a captured response exposes a reliable citation signal, KnitKnot binds the claim to that source. The sources view shows the owned, competitor, and third-party pages present in the captured citations. When no reliable signal can be bound to a claim, attribution remains empty.

## From report to action

Repeated gaps in the report can become **Issues**: persistent records such as a factual problem, missed capability, losing comparison, coverage gap, or access failure. Issues feed **Playbooks**: legacy evidence-grounded briefs or technical fix plans. After publication, a later full benchmark compares observed answer, citation, score, and issue changes without treating the intervention as the only explanation.

## Sharing

Your report has a public, shareable version at your workspace's `knitknot.io` address. **Brief** presents the decision-ready narrative; **Evidence** contains the answer, competitor, source, topic, and methodology drill-downs. Both preserve the report and subject in the URL, so refresh, browser history, and copied deep links return to the same context.

Raw mirror of this content: https://knitknot.ai/docs/read-your-report.md. Site-wide summary: /llms.txt · full content: /llms-full.txt

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Read your report

What the AI Presence Score means, how to read wins and losses, and how to drill from a headline number to the exact AI response behind it.

Updated

After a run completes, your report is the summary of everything the four engines said. Every number in it drills down to the underlying answers : nothing is a black box.

The AI Presence Score

The headline is a 0–100 composite of how favorably AI represents you in its answers to buyer questions: how often you show up, how you fare head-to-head, whether what AI says about you is accurate, and how you are framed. It is designed to be tracked over time; the trend across runs matters more than any single reading.

Two supporting numbers to know:

  • Visibility : the share of answers where you were mentioned at all (excluding questions that name you directly, which would inflate it).
  • Win rate : of the head-to-head comparisons where the AI picked a vendor, how often it picked you.

What AI tells buyers

This section is the qualitative core: the recurring claims AI makes about your company, with the captured quotes and engines behind them. Claim conclusions use available company facts, receipts, and validation gates; missing evidence remains unverifiable rather than being treated as false.

Competitive results

Head-to-head outcomes are broken down per competitor and per capability: where you win, where you lose, and what the AI said when it picked the other vendor. A loss row links straight to the responses behind it, so you can read exactly how the AI justified the pick.

One nuance: if AI recommends one of your own products over another, that’s a win for your brand, not a loss : the scoring knows your product family.

Sources

When a captured response exposes a reliable citation signal, KnitKnot binds the claim to that source. The sources view shows the owned, competitor, and third-party pages present in the captured citations. When no reliable signal can be bound to a claim, attribution remains empty.

From report to action

Repeated gaps in the report can become Issues: persistent records such as a factual problem, missed capability, losing comparison, coverage gap, or access failure. Issues feed Playbooks: legacy evidence-grounded briefs or technical fix plans. After publication, a later full benchmark compares observed answer, citation, score, and issue changes without treating the intervention as the only explanation.

Sharing

Your report has a public, shareable version at your workspace’s knitknot.io address. Brief presents the decision-ready narrative; Evidence contains the answer, competitor, source, topic, and methodology drill-downs. Both preserve the report and subject in the URL, so refresh, browser history, and copied deep links return to the same context.