How Do I Remediate Thousands of PDFs at Scale?

You have just completed an accessibility audit. The numbers are back, and they are not encouraging: 23,000 PDFs across your institution's web properties, departmental portals, and learning management system. Roughly 80% of them fail basic WCAG 2.1 compliance checks. The April 2027 ADA Title II deadline (extended from 2026 via the IFR) is approaching, and your team consists of two accessibility coordinators who handle everything from training faculty to reviewing alt text.

This is not a unique situation. It is the reality facing most universities right now.

The math on manual remediation is brutal. At 30 to 60 minutes per document — adding tags, writing alt text, fixing reading order, repairing table structures — a single person working full-time could remediate roughly 15 to 20 documents per day. At that pace, clearing a backlog of 20,000 documents would take four to five years. The deadline is months away.

The answer is not to hire 50 contractors. It is to build a structured remediation pipeline that combines intelligent triage, automation, and targeted human review. Here is how to do it.

Step 1: Audit and Understand What You Actually Have

Before remediating a single document, you need a complete inventory. This means scanning every PDF across every property your institution controls — your main website, department sites, Canvas or Blackboard courses, digital repositories, and shared drives.

A good audit produces more than a pass/fail count. You need to know what is wrong with each document. A PDF missing a single alt text tag is a very different problem from a scanned image PDF with zero structural markup. The type and severity of issues determine how each document flows through your pipeline.

Categorize documents by issue complexity:

• Light fixes — missing alt text on a few images, minor tag issues, missing document language
• Moderate fixes — broken reading order, untagged tables, missing headings structure
• Heavy fixes — scanned image PDFs requiring OCR, complex multi-column layouts, forms without labels
• Rebuild required — corrupted files, documents so poorly structured that remediation costs more than recreation

This categorization directly feeds your automation strategy. Light and moderate fixes are prime candidates for AI-powered remediation. Heavy fixes need AI plus human review. Rebuilds need a different conversation entirely.

Step 2: Prioritize Ruthlessly

You cannot fix everything at once. A clear prioritization framework lets you demonstrate compliance progress while addressing the highest-risk documents first.

Tier 1 — Public-facing documents. Anything on your main website, admissions pages, financial aid forms, or program catalogs. These are the most visible and the most likely to trigger complaints or OCR investigations.

Tier 2 — Student-facing documents. Course syllabi, assignment sheets, lecture slides converted to PDF, and readings uploaded to your LMS. Students with disabilities encounter these daily. This is also where your legal exposure is highest.

Tier 3 — High-traffic internal documents. HR policies, benefits guides, faculty handbooks, committee reports that circulate widely.

Tier 4 — Archived and low-traffic documents. Historical records, old meeting minutes, superseded policies. These still need remediation, but they can wait until Tiers 1 through 3 are cleared.

Work through the tiers in order. Report progress by tier so administration can see that the most critical documents are being addressed first.

Step 3: Use Automation as a Force Multiplier

Automation does not replace human judgment. It replaces human labor on the repetitive, predictable portions of remediation — which happen to account for 60 to 80% of the total work.

A well-configured AI remediation system can handle tagging, heading structure, reading order correction, table markup, and basic alt text generation across thousands of documents in hours rather than months. The key is understanding where automation is reliable and where it is not.

This is where confidence scoring becomes essential.

Step 4: Let Confidence Scores Route Your Workflow

Not every AI-generated fix deserves the same level of scrutiny. A confidence scoring system evaluates how certain the AI is about each remediation it performs and routes documents accordingly:

• High confidence (above 90%) — Auto-approve. The fix is straightforward and the AI's output matches established patterns. Examples: adding document language, tagging simple paragraphs, generating alt text for common chart types.
• Medium confidence (70 to 90%) — Light review. A human spot-checks the AI's work but does not redo it from scratch. Examples: complex table structures, reading order in multi-column layouts.
• Low confidence (below 70%) — Full human review. The AI flags what it found but a trained remediator makes the final decisions. Examples: decorative vs. informative image classification, ambiguous reading order, mathematical notation.

This routing model means your accessibility coordinators spend their time where it matters most — reviewing genuinely difficult cases — instead of manually tagging thousands of simple paragraphs. It is the difference between reviewing 3,000 flagged documents and manually remediating 20,000.

Step 5: Validate at Scale

Remediation without validation is guesswork. Every document that passes through your pipeline — whether auto-approved or human-reviewed — needs automated validation against WCAG 2.1 success criteria before it is published.

Batch validation should check tag structure, alt text presence, reading order logic, color contrast in embedded images, table header associations, and form field labels. Documents that fail validation loop back into the review queue.

The real cost of skipping validation is rework. A document published with broken tags will eventually generate a complaint, and you will remediate it again — this time under pressure.

Step 6: Establish Go-Forward Policies

Clearing your backlog is only half the battle. Without go-forward policies, you will rebuild that backlog within two semesters.

Effective go-forward policies include:

• Intake gates — Every new PDF uploaded to your website or LMS passes through automated accessibility checking. Non-compliant documents are flagged before publication.
• Faculty tooling — Give content creators the ability to check their own documents before uploading. Reduce friction by integrating checks into the tools they already use.
• Departmental accountability — Assign accessibility compliance to department-level reporting. Track new document compliance rates alongside backlog progress.
• Semester reviews — Run a full-property scan at the start of each semester to catch documents that slipped through.

The goal is to shift from a reactive remediation project to a proactive compliance culture. The backlog is a one-time problem. The pipeline is permanent.

Step 7: Track and Report Progress

Administration needs to see measurable progress. Build a reporting dashboard that tracks:

• Documents scanned vs. documents remediated vs. documents validated
• Compliance rate by tier (public-facing, student-facing, internal, archived)
• Average remediation time per document (this number should drop as automation handles more)
• Documents flagged for human review vs. auto-approved
• New documents added vs. new documents compliant at upload

These metrics tell a clear story: the backlog is shrinking, the pipeline is working, and new content is being created accessibly from the start.

The Bottom Line

Remediating thousands of PDFs is not a staffing problem. It is a systems problem. The institutions that will meet the 2026 deadline are not the ones that hired the most contractors — they are the ones that built intelligent pipelines combining automation, confidence-based routing, human expertise where it counts, and validation at every step.

The backlog is finite. With the right approach, it is solvable.

Aelira is built for institutional-scale remediation — batch processing, confidence scoring, and dual validation across your entire document library. Start a free pilot.