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. Making sure AI reads the right facts about a company is literally what KnitKnot does.

# Getting started with KnitKnot

What KnitKnot does, how the benchmark → report → fix loop works, and what to expect in your first week.

KnitKnot measures how AI models — ChatGPT, Claude, Perplexity, and Gemini — represent your company when buyers ask them to compare vendors. It runs real buyer-style evaluation prompts through each engine, scores every response at the claim level, and turns the gaps into a prioritized playbook of content fixes.

Everything in the product hangs off one loop:

  1. 1. **Benchmark** — run your prompt library across the four engines.
  2. 2. **Report** — see your AI Presence Score, where you win and lose head-to-head, and which sources the AIs are citing.
  3. 3. **Fix** — work through the issues and playbooks KnitKnot generates from the gaps.
  4. 4. **Re-run** — benchmark again and watch the score move as your fixes get picked up.

## Sign in

Go to [app.knitknot.ai](https://app.knitknot.ai/signin) and sign in with your work email. Accounts are organized by company domain — teammates with the same email domain join the same account, and your company's data lives in a shared workspace.

If your company doesn't have a workspace yet, [request a benchmark](https://knitknot.ai/) and we'll set one up — the first benchmark is free.

## What's already set up for you

By the time you sign in, your workspace usually already has:

  • - **A researched company profile** — your features, positioning, and products, each backed by a source link.
  • - **A competitor set** — the vendors AI actually compares you against, each with the same depth of profile.
  • - **A prompt library** — evaluation prompts grounded in real Google search queries with monthly volume data, not synthetic templates. Prompts persist across runs, so every benchmark measures the same questions and results stay comparable over time.

You can review and edit all three from the console — see [Run your first benchmark](/docs/run-your-first-benchmark/) for where each lives.

## Your first week

  • - **Day 1** — read your report ([how to read it](/docs/read-your-report/)), skim the evaluations behind the headline numbers, and sanity-check the competitor set.
  • - **Day 1–2** — review the open issues: factual errors, missed features, and losing comparisons, ranked by severity.
  • - **Week 1** — pick one playbook and ship it. Each one maps linked evidence to a create, revise, or repair brief.
  • - **Ongoing** — benchmarks re-run on a schedule; the score trend on your home page shows whether the fixes are landing.

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

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Docs › Getting started

Getting started with KnitKnot

What KnitKnot does, how the benchmark → report → fix loop works, and what to expect in your first week.

Updated

KnitKnot measures how AI models — ChatGPT, Claude, Perplexity, and Gemini — represent your company when buyers ask them to compare vendors. It runs real buyer-style evaluation prompts through each engine, scores every response at the claim level, and turns the gaps into a prioritized playbook of content fixes.

Everything in the product hangs off one loop:

  1. Benchmark — run your prompt library across the four engines.
  2. Report — see your AI Presence Score, where you win and lose head-to-head, and which sources the AIs are citing.
  3. Fix — work through the issues and playbooks KnitKnot generates from the gaps.
  4. Re-run — benchmark again and watch the score move as your fixes get picked up.

Sign in

Go to app.knitknot.ai and sign in with your work email. Accounts are organized by company domain — teammates with the same email domain join the same account, and your company’s data lives in a shared workspace.

If your company doesn’t have a workspace yet, request a benchmark and we’ll set one up — the first benchmark is free.

What’s already set up for you

By the time you sign in, your workspace usually already has:

  • A researched company profile — your features, positioning, and products, each backed by a source link.
  • A competitor set — the vendors AI actually compares you against, each with the same depth of profile.
  • A prompt library — evaluation prompts grounded in real Google search queries with monthly volume data, not synthetic templates. Prompts persist across runs, so every benchmark measures the same questions and results stay comparable over time.

You can review and edit all three from the console — see Run your first benchmark for where each lives.

Your first week

  • Day 1 — read your report (how to read it), skim the evaluations behind the headline numbers, and sanity-check the competitor set.
  • Day 1–2 — review the open issues: factual errors, missed features, and losing comparisons, ranked by severity.
  • Week 1 — pick one playbook and ship it. Each one maps linked evidence to a create, revise, or repair brief.
  • Ongoing — benchmarks re-run on a schedule; the score trend on your home page shows whether the fixes are landing.