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How good are your docs, really?

Answer a few questions about your documentation. Our models read it the way an LLM, an agent, and a first-time developer would, then score it across six dimensions and show you where it wins, where it leaks, and what's missing.

Scored on 6 weighted dimensions Benchmarked vs competitors Read by the same models developers use
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01 · the basics

The name developers would type into ChatGPT when they're evaluating you.

02 · where they live

The URL a developer lands on. We'll read what's publicly reachable there.

03 · what you sell

So we benchmark you against the right neighbours.

04 · the audience

One line. Backend engineers integrating an API reads very differently from data scientists.

05 · your worry

Optional. Pick any that worry you. It won't bias the score, it just sharpens the read.

06 · where to send it

You'll see the score on screen now. We email the full findings + evidence log.

Scanning your docs

Reading every page we can reach.

starting

Scored on

  • Coverage · 25%
  • AI readiness · 20%
  • Navigation · 20%
  • Freshness · 15%
  • Findability · 10%
  • Consistency · 10%

Scan stopped

We could not read enough to score it.

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        Docs usually live at

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          You've seen the quick score

          Now get the audit that fixes it.

          This score is a fast, directional read. A full paperkraft audit goes page-by-page across your whole docs set, with evidence for every finding and a prioritized fix plan we can ship with you.

          Content & code formatInformation architectureNavigation depth Per-page freshnessWorking, tested examplesAI-readiness & llms.txtPrioritized fix plan
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