Our Experience with Accessup.ai
Accessup.ai is a PDF remediation platform designed to help organizations bring their documents and e-books into compliance with the Americans with Disabilities Act (ADA) and the upcoming European Accessibility Act (EAA).
The platform integrates into high-volume PDF generation environments, allowing accessibility remediation to happen at scale.
Our Approach
We partnered with Accessup.ai to support the discovery phase and develop the alpha version of their product. With the EAA coming into effect in June 2025, Accessup.ai aimed to build a tool that could help automate document remediation with accuracy, scalability and compliance with existing accessibility standards.
Results
A clear understanding of legal and technical requirements for document accessibility
An analysis of current market offerings to identify gaps and opportunities
A validated approach for addressing two key use cases: PDFs and e-books
A functional alpha prototype ready for further development and testing
Implementation
We worked closely with Accessup.ai to define and prototype the initial version of their product. This included:
Understanding the regulatory and technical landscape — including ADA, EAA, and accessibility guidelines such as PDF/UA and WCAG
Market analysis — assessing existing tools and identifying key areas of differentiation
Discovery research — mapping workflows and needs for two core target formats: PDF documents and ebooks
Design and prototyping — turning ideas into functional prototypes to test core features
The Solution
The alpha version of Accessup.ai focused on a structured workflow that allows users to upload a PDF, apply remediation actions, and receive an updated version of the file along with a final accessibility report. The process combined established PDF processing tools with newer machine learning techniques.
1. Initial Report Generation
Each uploaded document is first validated using VeraPDF, which checks for compliance against an accessibility profile (e.g. PDF/UA-1). The output is an initial report highlighting areas that need attention.
2. Basic Accessibility Setup
Before applying remediation actions, the system checks and updates the document’s metadata and structure. This includes:
Tagging the document
Adding or correcting language information
Ensuring images have basic metadata
Including descriptions where possible
3. Remediation Actions
The platform supports four key remediation actions:
Language Detection
An LLM reviews the document text and identifies the appropriate language, updating the metadata to reflect this.
Alternative Text Generation
All images are identified and tagged. Each image is then processed by an image-to-text model to generate a description, which is added to the document metadata.
Optical Character Recognition (OCR)
For scanned PDFs, OCR is applied to extract text from image-based content and insert it in a structured format with appropriate tagging.
PDF Layout Analysis (optional)
This tool provides a structural overview of the document to help verify logical reading order and layout consistency.
4. Final Report
After all selected actions have been completed, a final accessibility report is generated. This report reflects the updated state of the document, based on the remediations applied. See Accessup.ai in action with a hands-on preview here.
Technologies we used
Cloud Infrastructure — AWS
Programming Languages — Python
Tooling — Jupyter Notebooks, VeraPDF, OCR libraries
Machine Learning — PyTorch, large language models, image-to-text models