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"PDFs are a proverbial tool of Satan and not to use them. But we have thousands of PDF course materials already created."
— IT Director, r/highereducation (31 upvotes)
Manual PDF remediation takes 10+ hours per document. Here's how to speed it up 10x with AI automation.
10-15 hours
Manual remediation per PDF (typical)
~30 seconds
Fully automated, no manual review needed
Many PDFs are just scanned images. Text isn't selectable, searchable, or accessible to screen readers.
Manual fix: Retype all text, or run OCR and manually correct errors (5-8 hours per document)
PDFs lack semantic headings (H1, H2, H3). Screen readers can't navigate the document.
Manual fix: Manually tag every heading, paragraph, list, table (3-4 hours per document)
Images, charts, and diagrams have no descriptions. Blind students can't understand visual content.
Manual fix: Write descriptive alt text for every image (2-3 hours per document)
Data tables need header rows/columns tagged for screen reader navigation.
Manual fix: Tag table headers, scope attributes (1-2 hours per document)
OCR + error correction: 5-8 hours
Structure tagging: 3-4 hours
Alt text for images: 2-3 hours
Table structure: 1-2 hours
11-17 hours per PDF (average)
× 100 PDFs per course = 1,100-1,700 hours of work
Tesseract 5 at 300 DPI extracts all text from scanned PDFs automatically.
Time saved: 5-8 hours reduced to 5 seconds
Dual-strategy reading order fix (heuristic for standard layouts, AI vision for complex ones) plus full structure tagging — headings, paragraphs, lists.
Time saved: 3-4 hours reduced to 30 seconds
Auto-detects tables, adds THead/TBody/TH/TD with Scope attributes. AI vision generates context-aware alt text for images and diagrams.
Time saved: 3-5 hours reduced to 1-2 minutes
Every fix is scored. Rule-based fixes (~0.95) apply automatically. AI fixes (~0.55-0.60) are flagged for your review. You only check what needs human judgment.
Review time: 5-10 minutes instead of hours
After remediation, Aelira validates the output against published standards — not just “we tried,” but verifiable proof of compliance.
Matterhorn Protocol
15 machine-checkable PDF/UA conditions (structure tree, language, alt text, headings, tables, role mappings)
veraPDF (optional)
108 rules for comprehensive PDF/UA-1 and PDF/UA-2 compliance. Results merged into a single report.
Free and demo accounts use Google Gemini for AI features. Department plans can use custom or open-source models via Ollama. Self-hosted open-source deployments run any model you choose — no vendor lock-in.
Upload + automated processing: ~30 seconds per PDF
OCR, structure, tables, reading order, alt text: fully automated
Human review: 5-10 minutes for flagged items only
Validation: 120+ checks, automated
~30 seconds per PDF + brief review
× 100 PDFs per course = under 1 hour total
Save 1,000+ hours per course
Misses April 2026 deadline by 10 months.
Meets April 2026 deadline with months to spare.
Time Saved
10,193 hours
Money Saved
$1.05M
Faster Completion
14 months
Automate 90% of PDF remediation work with Aelira AI.
30-day free trial · Process up to 10 PDFs free
Watch Aelira process a 50-page scanned PDF in under 5 seconds. Join 500+ universities preparing for April 2026.