Humans Direct AI-driven Automation for PDF Accessibility

laptop with automation running

Artificial Intelligence (AI)-driven solutions are making a splash in many organizations. They are removing many layers of manual operations, shortening the time to complete repetitive tasks, and solving for more and more complex functionality across many industries. AI can complete repetitive tasks without tiring or deviating from the programmed procedures. AI-driven automation starts with humans. 

PDF accessibility often involves thousands of similar documents across many departments within organizations. Financial, healthcare, and insurance companies produce thousands of PDF statements, reports, and other repetitive, templated documents. These and other organizations need to remediate more PDFs than they have time and staff to complete. The frequency at which the PDFs are generated compounds the need for timely and efficient remediation. Ensuring the accurate remediation and full compliance of every document causes difficulties due to the sheer volume of documents. 

As humans use AI-driven, automated solutions to complete increasing numbers of time-consuming, and repetitive tasks, many questions arise. Many organizations are concerned about the accuracy, security, and viability of AI-driven automated solutions, particularly in the area of accessibility. Recognizing that manual reviews are necessary to resolve or validate many accessibility issues, organizations must address these concerns. And that starts with the data scientists.  

AI-driven automation begins with humans

Humans inform AI functionality. The responsibility for programming AI models and testing them to ensure accurate and complete performance of assigned tasks lies with human data scientists. Digital elements like PDF tags produced by AI are as valid as the rules and assumptions programmed by humans. Data scientists can obtain incredibly accurate and useful results using AI. But to do so, they must apply a full understanding of the end goal and how to define that goal to a computer. To ensure successful, accurate results, data scientists must collaborate with PDF accessibility experts. 

Tagging PDFs automatically is solved

With a large enough sample size, AI components such as machine learning and computer vision can combine to recognize and tag elements within a PDF. Because data scientists need many similarly-formatted samples with which to train the AI model, this technology is applicable only to high volumes of templated documents. 

With a complete understanding of a correctly tagged PDF, data scientists program AI to complete remediation tasks and tag PDFs to make them accessible. Such a solution is flexible enough to adapt to calculated variations among the documents as well. With this functionality, AI-driven automation converts high volumes of templated PDFs to accessible PDFs without human intervention. 


During the programming process, data scientists must rigorously test and validate automated PDF accessibility solutions, thereby eliminating the requirement for manual validation across thousands of documents after tagging. The resulting accessible PDFs will pass not only automated checkers, which can only verify 20-30% of accessibility issues, but also manual checks with screen readers. Validation is a crucial step in ensuring full compliance with accessibility standards.


Organizations can easily address security using one of several architectures to host AI-driven automation for PDF accessibility. Options include:

  • Single-tenant cloud-based environments
  • On-premises models in which data stays within an internal customer network
  • Service-based configurations where the automation is completed by a vendor who hosts the data on their secure network

For all of these configurations, either batch processing or on-demand functionality can be implemented. Each of these solutions can rely on existing digital security protocols to protect sensitive data. 


With accuracy and privacy assured, automated PDF remediation solutions for high-volume, templated PDFs are cost-effective and efficient. AI-driven models drastically reduce time and costs, with minimal staffing requirements. The required PDFs are uploaded, remediated automatically, and accessible PDFs are provided back to them, or directly to their end users.  

Equidox AI automates PDF remediation

Equidox AI is a fully automated, AI-powered PDF accessibility solution for high-volume, templated documents that is cost-effective and accurate. Once programmed by Equidox data scientists in collaboration with Equidox accessibility experts, it produces fully accessible and compliant PDFs with no human intervention.  Mitigating the risk of digital accessibility lawsuits and including everyone has never been easier.


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Tammy Albee

Tammy Albee | Director of Marketing | Equidox Tammy joined Equidox after four years of experience working at the National Federation of the Blind. She firmly maintains that accessibility is about reaching everyone, regardless of ability, and boosting your market share in the process. "Nobody should be barred from accessing information. It's what drives our modern society."