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.

# Glossary

Quick definitions of the core KnitKnot terms: subject, question, run, answer, issue, playbook, and the rest, each linked to a fuller explanation.

Short definitions of the terms used throughout KnitKnot and this documentation. Each links to a fuller explanation where one exists.

## Core objects

**Subject** : the thing a question benchmarks: either your brand or one of its products. One company has one brand subject and zero or more product subjects. The canonical way everything is scoped per-product. See [Subjects](/docs/subjects/).

**Brand** : your company as a whole; the top-level subject. A recommendation of any of your products counts as a brand win.

**Product** : a subject under your brand, with its own questions, competitors, and report slice. See [Benchmark products separately](/docs/benchmark-products-separately/).

**Question** : one buyer-style question sent to an AI engine during a benchmark. Questions live in a persistent set that stays stable across runs, so your score stays comparable. See [Benchmark questions](/docs/benchmark-questions/) and [Manage benchmark questions](/docs/manage-questions/).

**Run** : one full benchmark: your active question library executed across the selected engines and scored. Runs are the unit of measurement over time. See [Runs and scoring](/docs/runs-and-scoring/).

**Answer** : one scored AI response: a single question run against a single engine. The atom every metric is built from.

**Engine** : one of the four AI systems a question runs through: ChatGPT, Claude, Perplexity, Gemini. See [Engines](/docs/engines/).

**Competitor** : a vendor AI compares you against; head-to-head results are scoped to your competitor set. See [Manage competitors](/docs/manage-competitors/).

## Metrics

**AI Presence Score** : the 0–100 composite headline number. See [Metrics reference](/docs/metrics-reference/).

**Coverage** : the per-response label for how prominently you appear: primary, substantial, peripheral, incidental, or absent.

**Visibility rate** : the share of scored responses where you appear at all, computed organic-only (excluding questions that name you).

**Win rate (W-L-T)** : of decided head-to-head comparisons, the share you won; ties count against you.

**Sentiment** : how favorably you're framed when you appear, on a 0–100 scale.

Full definitions: [Metrics reference](/docs/metrics-reference/).

## The fix loop

**Issue** : a tracked gap a benchmark surfaced (factual error, outdated claim, missed capability, losing comparison, visibility gap), with its evidence attached. See [Issues and playbooks](/docs/issues-and-playbooks/).

**Playbook** : an evidence-grounded create, revise, or repair brief linked to one or more issues and a measurable hypothesis.

**Source** : a web page present in a captured AI response's citations. A claim is attributed to that page only when a reliable response-native signal can be bound to the claim.

**Head-to-head (H2H)** : a comparison question that names you against a competitor. Excluded from organic visibility, counted in win rate.

## Access

**Workspace** : your company's shared data in KnitKnot. Teammates on the same email domain join the same account. See [Getting started](/docs/quickstart/).

**Public report** : a published, read-only report at `{workspace}.knitknot.io/reports/{slug}` that anyone with the link can open. See [Share your report](/docs/share-your-report/).

**MCP** : the Model Context Protocol connection that lets AI assistants query and act on your workspace. See [Connect to your AI tools](/docs/connect-to-ai-tools/).

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

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Docs › Reference

Glossary

Quick definitions of the core KnitKnot terms: subject, question, run, answer, issue, playbook, and the rest, each linked to a fuller explanation.

Updated

Short definitions of the terms used throughout KnitKnot and this documentation. Each links to a fuller explanation where one exists.

Core objects

Subject : the thing a question benchmarks: either your brand or one of its products. One company has one brand subject and zero or more product subjects. The canonical way everything is scoped per-product. See Subjects.

Brand : your company as a whole; the top-level subject. A recommendation of any of your products counts as a brand win.

Product : a subject under your brand, with its own questions, competitors, and report slice. See Benchmark products separately.

Question : one buyer-style question sent to an AI engine during a benchmark. Questions live in a persistent set that stays stable across runs, so your score stays comparable. See Benchmark questions and Manage benchmark questions.

Run : one full benchmark: your active question library executed across the selected engines and scored. Runs are the unit of measurement over time. See Runs and scoring.

Answer : one scored AI response: a single question run against a single engine. The atom every metric is built from.

Engine : one of the four AI systems a question runs through: ChatGPT, Claude, Perplexity, Gemini. See Engines.

Competitor : a vendor AI compares you against; head-to-head results are scoped to your competitor set. See Manage competitors.

Metrics

AI Presence Score : the 0–100 composite headline number. See Metrics reference.

Coverage : the per-response label for how prominently you appear: primary, substantial, peripheral, incidental, or absent.

Visibility rate : the share of scored responses where you appear at all, computed organic-only (excluding questions that name you).

Win rate (W-L-T) : of decided head-to-head comparisons, the share you won; ties count against you.

Sentiment : how favorably you’re framed when you appear, on a 0–100 scale.

Full definitions: Metrics reference.

The fix loop

Issue : a tracked gap a benchmark surfaced (factual error, outdated claim, missed capability, losing comparison, visibility gap), with its evidence attached. See Issues and playbooks.

Playbook : an evidence-grounded create, revise, or repair brief linked to one or more issues and a measurable hypothesis.

Source : a web page present in a captured AI response’s citations. A claim is attributed to that page only when a reliable response-native signal can be bound to the claim.

Head-to-head (H2H) : a comparison question that names you against a competitor. Excluded from organic visibility, counted in win rate.

Access

Workspace : your company’s shared data in KnitKnot. Teammates on the same email domain join the same account. See Getting started.

Public report : a published, read-only report at {workspace}.knitknot.io/reports/{slug} that anyone with the link can open. See Share your report.

MCP : the Model Context Protocol connection that lets AI assistants query and act on your workspace. See Connect to your AI tools.