Viewpoint eDiscovery

Project Overview

Viewpoint is a legal and compliance analytics platform used to manage document review for litigation, investigations, and regulatory matters. The product suite includes a modern web-based platform and two legacy on-premise applications: Review and Processing. The long-term goal is to consolidate these tools into a single, browser-based experience under Viewpoint Web.

Challenge

As our team prepared to integrate AI-assisted review features into the platform, it became clear that the existing interface and user workflows needed to be re-evaluated. The legacy-inspired design introduced usability barriers and inefficiencies that risked interrupting the experience for current users once the new features were deployed.

My Role

I was responsible for redesigning the Viewpoint Web interface and resolving key UX pain points ahead of the feature rollout. My focus was on modernizing the UI, improving consistency across the platform, and ensuring that updates could be introduced with minimal disruption to ongoing legal workflows.

Process

To establish a scalable design foundation, I began by auditing the existing interface and any prior style documentation. From there, I developed an expanded UI guideline system and created a comprehensive component library to support future development and maintain design consistency. These standards are documented in the LCA UI Guidelines and reflected throughout the new Viewpoint Web experience.

Problems to be Solved

During my initial audit of the web interface, several usability and design issues emerged that were negatively impacting user efficiency, accessibility, and overall experience. The examples of the old UI below highlight key areas of concern:

Outdated interface design that no longer reflects modern usability standards.
Non-compliance with Conduent’s branding guidelines, resulting in inconsistent visual identity.
Disorganized navigation and tool structure, making it difficult for users to locate key actions.
Over-reliance on right-click actions, hiding essential tools from less experienced users.

Accessibility issues, with multiple elements failing to meet WCAG / ADA compliance standards.
Inconsistent styling, terminology, and interaction patterns across different screens.
Inefficient use of space, leading to visual clutter and reduced clarity.
Confusing organization and workflow, creating friction in completing review tasks efficiently.

Actions Taken

To address the identified usability and design issues, I led a structured redesign process focused on consistency, accessibility, and efficiency:

Conducted comprehensive design audits to identify inconsistencies, accessibility failures, and areas for improvement.
Mapped user flows and created wireframes to resolve workflow friction and optimize screen layouts.
Reorganized and simplified navigation and action menus to establish clear hierarchy and reduce cognitive load.

Prioritized high-frequency actions by improving visibility and emphasizing them through visual hierarchy.
Developed and presented interactive prototypes to product owners, stakeholders, and lead developers for iterative feedback and alignment.
Documented all design updates within epics and supporting materials to ensure smooth handoff to development.
Performed a post-QA design audit to verify implementation accuracy and maintain design integrity across the final build.

Enhanced Review Feature

As part of the Viewpoint eDiscovery redesign, I contributed to the development of an AI-powered tagging component aimed at improving the speed and accuracy of legal document review. The feature supports reviewers by automatically identifying and suggesting tags based on contextual patterns, key phrases, and case-specific criteria. The goal was to enhance reviewer efficiency, reduce manual tagging errors, and streamline the review process while maintaining transparency and user control over AI-generated suggestions.

Overview: Brief description of the tagging protocol and its purpose within the review process (e.g., relevance, privilege, confidentiality).
Dates: Defined time ranges relevant to the case or matter that inform contextual tagging.
Keywords / Phrases: Lists of important terms, entities, or language patterns used to train or guide the AI’s tagging logic.

Criteria: Detailed tagging rules or logic statements (e.g., “Documents containing both [keyword A] and [date range B] are flagged for review”).
Notes: Space for reviewer or administrator commentary, rationale, or clarifications regarding tag behavior and decision-making.

Once AI-assisted tagging is complete, users can search, filter, and organize documents based on the generated Enhanced Review (ER) tags. This functionality allows reviewers to quickly isolate documents by relevance, topic, or other defined criteria, improving efficiency during large-scale investigations and compliance reviews. The filtering system supports both AI-generated and manually applied tags, ensuring transparency and user control throughout the review process.