Problem Statement & Goals
Problem:
Enterprise teams managing complex technical and compliance documentation faced several critical challenges:
Manual authoring workflows were time-consuming and error-prone.
Translations required external tools, adding cost and breaking workflow continuity.
Inconsistent keyword tagging and metadata management made search unreliable.
Large content repositories led to frustration when finding the right information quickly.
Goal - Redesign the home page to:
Streamline authoring workflows by integrating AI-assisted drafting and editing.
Reduce translation time and cost with built-in AI-powered translation tools.
Improve content discoverability through automated keyword extraction and consistent tagging.
Enhance user experience by designing intuitive, seamless AI features that blend into existing workflows.
Build user trust in AI by ensuring transparency and maintaining editorial control.
My Role:
As the Core UI/UX Designer, I was responsible for:
Led UX research to identify user pain points in content authoring and search.
Designed intuitive AI interaction flows for drafting, translation, and keyword tagging.
Created prototypes and conducted usability testing with technical writers and content managers.
Collaborated closely with product managers and AI engineers to balance functionality and usability.
Design Process:
To guide design decisions for Quarky Copilot AI, we used a mix of qualitative and quantitative research methods:
Qualitative methods:
User interviews: Talked to technical writers, content managers, and compliance leads to understand daily workflows and frustrations.
Stakeholder workshops: Gathered feedback on early AI concepts to align business goals with user needs.
Usability testing: Ran moderated sessions on low-fidelity wireframes to validate design clarity and trust in AI suggestions.
Quantitative methods:
Surveys: Collected data on how frequently users faced issues like manual tagging, translation delays, and document search failures.
Task completion timing: Measured average time spent on manual vs. AI-assisted tasks to evaluate impact.
Feature adoption metrics: Planned to track how often new AI features are used in production to inform future iterations.
Ideation & Wireframes:
To bring Quarky Copilot AI to life, we followed an iterative design process:
Research & Discovery
Conducted stakeholder interviews to understand user pain points.
Mapped existing workflows to identify friction points and duplication.
Ideation Workshops
Brainstormed AI-powered features that could directly address these problems.
Explored concepts like AI-assisted drafting, one-click translation, automated tagging, and semantic search
Low-Fidelity Wireframes
Sketched rough layouts to visualize feature integration without adding UI complexity.
Focused on maintaining user control and transparency when presenting AI suggestions.
Feedback & Refinement
Shared wireframes with internal teams and target users.
Collected insights to refine layouts, simplify interactions, and clarify AI touchpoints.
Design Decision and Iteration:
Design Decision | Iteration & Feedback |
|---|---|
Added an AI suggestions panel instead of auto-inserting text | Early testers preferred suggestions over direct edits → increased trust and perceived control |
One-click translation button inside the authoring canvas | Users reported significantly faster workflow and reduced context switching |
Automated keyword extraction visible for review | Users wanted an editable list of AI-generated tags before final publishing; kept human oversight |
Semantic search bar with summary cards | Feedback supported minimalist design; reduced cognitive load and visual noise |
Impact & Results:
After launch, teams using Quarky Copilot AI reported:
30–40% faster document creation.
Significant cost savings on translation.
Improved content discoverability, reducing search time by up to 50%.
Higher confidence in documentation accuracy and compliance.
Designing for AI transparency helping users understand and trust AI suggestions.
Balancing automation with human control.
Creating seamless workflows without adding cognitive load.
Outcome and Learnings:
The Quarky Copilot AI became a key differentiator for QPP NextGen, positioning the product as a modern, AI-driven platform for enterprise content lifecycle management.
