Accessibility at Scale:
Redesigning a Legacy PDF Tool
Modernizing a legacy batch-processing tool into a scalable, accessible workflow for enterprise users.
Summary
Enterprise users relied on a legacy PDF accessibility tool to process large batches of documents, but fragmented workflows, poor system feedback, and accessibility gaps made the experience inefficient and difficult to use.
This project focused on redesigning the tool into a structured, accessible workflow.- improving clarity, reducing errors, and enabling users to process files at scale with greater confidence and efficiency.
The Problem
The legacy tool had evolved over time without a cohesive UX strategy, resulting in:
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Complex, fragmented workflows for batch processing
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Poor visibility into file status and errors
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Limited keyboard navigation and inconsistent focus states
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Accessibility gaps for screen readers and assistive technologies
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High cognitive load for both new and experienced users
These issues led to slow task completion, frequent errors, and reliance on workarounds or support.
Role: Lead UX Designer
Owned end-to-end design, including workflow redesign, accessibility strategy, prototyping, and collaboration with engineering. Leveraged and extended the design system to support complex workflows, ensuring consistency, accessibility, and scalability across the experience.
Before:
Users & Context
1
Compliance Teams
Compliance teams preparing accessible documents at scale
Needs: Clarity and feedback (file status, errors)
2
Enterprise Users
Enterprise users processing large batches of files
Needs: Speed and efficiency (batch workflows)
3
Assistive Tech Users
Users with accessibility needs relying on keyboard navigation and screen readers
Needs: Predictable, accessible interactions
Constraints
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Maintain feature parity with the legacy system
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Support high-volume batch processing workflows
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Align with accessibility standards (WCAG)
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Transition toward a cloud-based architecture
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Minimize disruption for existing users
My Approach
AI-Assisted Audit & Workflow Mapping
I used OpenCode with GitHubCopilot's Claude AI models to accelerate:
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UI audits (identifying gaps in navigation, structure, and interaction patterns)
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Edge case generation for features
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User story creation
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Error state discovery
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Initial task flow mapping
This allowed for rapid identification of problem areas across a complex system.
I used AI to rapidly generate accessibility audit scenarios and edge cases across complex batch workflows. I validated all findings through real user testing and assistive technologies to ensure accuracy and usability.
Technical Collaboration
I worked closely with engineering to ensure:
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Interaction patterns aligned with front-end architecture
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Accessibility requirements were implemented using semantic HTML and ARIA roles
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Designs were production-ready and scalable
My understanding of front-end code allowed me to validate feasibility early and reduce iteration cycles during development.
Before & After
After:
Unified workflow with clear stages (import - process - validate - export)
Real-time feedback on file status and progress
Structured Batch Workflow
Before:
Disconnected steps
No clear progress tracking
After:
Inline error messaging
Clear indication of which files failed and why
Actionable next steps
Error Handling & Feedback
Before:
Errors surfaced late or unclearly
Voice and tone not user friendly
Before:
Dated UI and layout
Design not scalable for large data sets
Repetitive actions and hidden features
After:
Table-based layouts optimized for batch processing.
Filtering and sorting for large datasets
Bulk actions to reduce repetitive work
Scalable UI for High-Volume Tasks
Before:
Limited and difficult to use
Non intuitive UI
Disconnected flow
After:
Improved and modernized UI
Added scalability for large batch files and components
Cleaner interface
Improved Flow Diagrams
The Design
Mid-fidelity Prototype
I used OpenCode with GitHubCopilot's Claude AI models and instructed the AI to base decisions on our user interview data along with our design system guidelines to create mid-fidelity wireframes that we iterated on; user stories for feature creation and usability test plans.
We used our prototypes to test the viability and feasibility of our solution. When we presented the prototype to key stakeholders, we were able to see whether the solution provided a viable business model and whether it was technically feasible to implement. These crucial factors determined our long-term success.
Our usability testing identified a few areas of confusion around the 2 step process of tag identification, as well as some visual clutter we could reduce by hiding some elements until they are actually needed by the user, based on what we knew from their work flows.
Hi-fidelity Figma Prototype
Final dev hand off was done in Figma, to pixel perfection including error states, accessibility guidelines and edge cases.
Final Designs
Outcomes
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Reduced user errors during batch processing
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Improved task completion confidence in usability testing
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Faster identification and resolution of failed files
Achievements
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Reduced friction in the user flow by 85%
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Redesigned the interface, reducing visual clutter by 78%
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Brought the platform into the Cloud with 80% feature parity
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Increased user satisfaction in the tool by 30%
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Reduced churn rate by 68%
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Utilize the design system for 80% of the components
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Improve self-serve success rate by 30%
Key Takeaways
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Accessibility improvements benefit all users by increasing clarity and predictability
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AI can accelerate discovery - but real user validation is critical for accuracy
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Modernizing legacy systems requires balancing familiarity with meaningful change
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Designing for scale means optimizing both interaction and system feedback
Reflection
This project demonstrates how modernizing legacy systems - when grounded in accessibility and real user needs - can significantly improve both usability and scalability.