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Readability Score

AI
Flesch-Kincaid grade + complex-sentence callouts + jargon flags + fixes.
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What you'll get: a Flesch-Kincaid grade level + complex-sentence callouts + jargon flags + 3–5 concrete edit suggestions. For another use, try AI · Rewrite (to actually apply the fixes).

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Private & secure
AI tools process your PDF in memory, send only the extracted text to the model, and discard everything immediately. No file is persisted on our servers.
No watermarks
Clean output, no branding, no daily limits.
Pay only for what you use
Purchased credits never expire. Cancel anytime.

Why people use Readability Score

Most readers can handle text that scores around 8–10 on the Flesch-Kincaid grade level — that’s 8th-grade reading level, the recommended target for general-audience content. Readability Score measures the doc’s grade level, flags the complex sentences and jargon making it harder than it needs to be, and suggests fixes.

Marketing / customer-facing copy QA
Run customer-facing copy through readability before publishing. Saas pages aimed at general audience but written by engineers often score 14–16 (graduate-level); the report flags which sentences are dragging the average up.
Patient-facing medical content
Healthcare apps and medical content services have explicit readability targets (often 6–8 grade level for accessibility). The score plus jargon flags surface what to simplify before publication.
Educational content alignment
EdTech building age-appropriate content runs the score to verify alignment. A 5th-grade textbook scoring at 9th-grade level is too hard; the flagged sentences are where to revise.
Technical doc clarity audit
Engineering-written docs aimed at non-technical buyers (sales decks, pricing pages, FAQ sections) often have register mismatch. The score quantifies the mismatch; the fixes section suggests how to bring it down.
Legal / policy doc accessibility
GDPR, terms of service, privacy policies have legal requirement to be “understandable.” A grade-12+ score signals risk. The report surfaces specific sentences and substitution suggestions.

How Readability Score works

Three steps. Sign in to run, zero retention — files never persisted on our servers.

  1. STEP 1
    Drop the doc
    PDF up to 100 MB. Works best on prose; heavily structured content (tables, code blocks, math notation) skews the score.
  2. STEP 2
    We score + flag
    Server-side extraction, then Flesch-Kincaid grade calculation, complex-sentence detection (high syllable-per-word ratios, long sentences with multiple clauses), jargon flagging (domain-specific terms with lay alternatives available).
  3. STEP 3
    Get a structured report
    Output: overall grade level / sentence-by-sentence flag list / jargon list with suggested replacements / overall recommendations (e.g. “split sentences over 25 words”). Page citations on every flag.

What makes pdfcraft ai different

  • Citations on every claim
    Every AI summary, answer, and translation links back to the page it came from. You can verify the source before relying on the output. Hallucination is bounded, not blind.
  • Pay only for what you use
    No subscription. Purchased credits never expire. Cancel anytime. Cost shown before you run — exact credit amount appears after upload.
  • Multi-provider routing, no vendor lock-in
    We balance multiple AI providers per call to optimise cost and quality. If one has an outage, the next call automatically tries another. You're not bound to a single vendor's pricing or downtime.
  • Zero retention by design
    Your PDF lives in memory only as long as the AI call needs it, then it's discarded — nothing is persisted to our servers. We don't fine-tune models on your data. Verifiable in your dashboard's audit log.
  • Honest about model limits
    When a doc exceeds the model's context window we surface that the output was truncated (we render result.wasTruncated in every AI tool's UI). When a number can't be verified from the source we flag it. No silent failures.

Frequently asked questions

Is Flesch-Kincaid the only metric?
Primary score is Flesch-Kincaid grade level (most widely used for English content). We also surface average sentence length and average syllable-per-word for context. Other metrics (Gunning Fog, SMOG, Coleman-Liau) are correlated and don’t add much signal.
Will it work for non-English content?
Flesch-Kincaid is English-specific. For Indian-language content (Hindi, Tamil, Bengali), the syllable-counting heuristic doesn’t translate accurately. Use the score on English source content only; for Indic readability, rely on human review.
What’s a good target score?
Depends on audience. General-audience web content: 6–9. Patient-facing health content: 5–7. Technical docs for technical readers: 10–13. Legal docs aiming at consumer accessibility: 8–10. Above 13 is graduate-level and should generally be intentional.
Can it auto-rewrite the flagged sentences?
No — it surfaces the issues. For automatic rewriting at lower readability, AI Improve Writing rewrites for clarity + concision (~20-30% shorter); AI Rewrite with the “simple” register option rewrites at lower complexity. Score-then-rewrite is a 2-tool workflow today.
What’s the credit cost?
3 credits per doc. Cost is fixed regardless of doc size.
What if the score feels wrong?
Flesch-Kincaid is heuristic — it doesn’t understand context, only word/sentence shape. A doc with simple language but specialized terms can score low (good readability) while still being inaccessible to laypeople. Use the score with the jargon-flag list, not in isolation.

Want to actually fix the writing?

AI Improve Writing rewrites for clarity + concision while preserving every claim and number — the natural next step after the readability audit identifies what to fix.

Try AI Improve Writing

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