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Inclusive Language Audit

AI
Gendered language + outdated terminology + stereotyping flagged with fixes.
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What you'll get: a bias audit covering gendered language, outdated terminology, stereotyping, and accessibility-framing — plus concrete edit suggestions. For another use, try AI · Proofread (grammar + spelling).

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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 Inclusive Language Audit

Inclusive Language Audit flags gendered language (mankind, manpower, chairman), outdated terminology (master/slave, blacklist/whitelist), and stereotyping language with concrete suggested fixes. Heuristic only — the audit catches obvious patterns, not all bias. Useful as a pre-publication pass for inclusive-language standards.

Pre-publication content audit
Articles, marketing copy, internal docs audited for inclusive language standards before publication. Catches the obvious gendered defaults (“chairman”, “manpower”) that have neutral alternatives (“chair”, “workforce”).
Legacy doc modernization
Older docs (style guides, internal handbooks, technical specs) frequently use outdated terminology. The audit surfaces what to update during a refresh pass.
Hiring / job-description review
Job descriptions are a known site for biased language (“rockstar”, “ninja”, “competitive” vs “collaborative” signals). Audit before posting to widen candidate pool.
Educational content review
Course materials, textbooks, training docs audited for inclusive language — especially important for content that reaches diverse student populations.
Brand-voice inclusion check
When evolving a brand voice, the audit verifies updates didn’t miss the harder-to-spot patterns (gendered metaphors, ability-based metaphors like “crippled” / “blind to”).

How Inclusive Language Audit 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 on any prose — articles, JDs, course materials, internal docs, marketing copy.
  2. STEP 2
    We scan + classify
    Server-side extraction, then a multi-pattern scan: gendered language (he-default, gendered role nouns), outdated terminology (problematic metaphors, slurs in older texts), stereotyping (gendered profession defaults), accessibility-aware language. Each flag classified by category and severity.
  3. STEP 3
    Get a structured fix list
    Markdown table: page / quote / issue category / suggested fix. Each fix has rationale (“'manpower' → 'workforce' or 'staff' — gender-neutral alternative is widely used”). Sorted by page for linear review.

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 this comprehensive?
No. The audit catches obvious patterns (lexical / template-based). It does NOT catch subtle bias (rhetorical framing, what’s included vs excluded, who’s named vs anonymized). For comprehensive inclusion review, treat the audit as a baseline catch and supplement with human reviewer.
Will it work in Indian-English context?
Yes — Indian-English specific patterns recognized (caste-coded language, region-coded stereotyping, English-with-Indian-conventions). Note: Indian-language content (Hindi, Tamil, etc.) has its own bias-language patterns we don’t audit yet — English content only for now.
What categories of issues does it cover?
Gendered language (he-default, role-noun gendering), outdated technical terminology (master/slave, blacklist/whitelist, sanity-check — all flagged with suggested replacements that have industry adoption), ability-based metaphors, age-coded language. Full taxonomy on the roadmap to publish.
Should I accept every fix?
No. Some flags are false positives (e.g. “mankind” in a quote vs in your own writing — the audit may not distinguish). Some suggestions don’t fit the doc’s register. Treat the table as a list of things to consider, not a list of things to apply.
What’s the credit cost?
3 credits per doc. Cost is fixed regardless of doc size.
Privacy?
Doc sent to inference provider for the scan, not stored. For confidential drafts, redact author names and project codenames via Redact PDF first if needed.

Want to fix the writing too?

AI Improve Writing tightens prose for clarity and concision — useful as the next pass after the inclusive-language audit, for an overall polish.

Try AI Improve Writing

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