Conversation Clusters
I redesigned the interface architecture and configuration experience for an enterprise machine learning feature that clusters social data into semantic network graphs. I transformed the tool into a unified workspace and designed a responsive widget system that could function both as a standalone exploration module and as an embedded component within multi-tenant dashboards and reports.
the challenge
The original product combined slow performance, fragmented workflows and dense visualizations, creating friction across both setup and analysis.
Performance Bottleneck: Auto-loading the last active session created a 1+ minute delay whenever users entered the product.
Workflow Limitations: Users were constrained by single-language cluster creation, manual-only timeframes, and a rigid 20-cluster query limit.
Data Readability Issues: Dense 20-item charts with long labels made patterns harder to interpret, while cramped side panels and unexplained “Top Keywords” obscured important insights.
I redesigned the experience across performance, workflow flexibility, data readability, and cross-product integration.
Workspace Landing Table: Replaced automatic session loading with a landing dashboard, allowing users to instantly open, duplicate, or create clusters without waiting.
Multi-Language Workflows: Designed a flexible setup modal that enabled users to generate cross-language clusters within a single flow, eliminating redundant query-building steps.
Scalable Chart Readability: Reduced visualization clutter by introducing top 5, 10, and 15 result filters, predefined timeframes, and expanded query-depth limits, doubling cluster capacity.
Contextual AI Summaries: Prioritized the user's most valuable data with an instant show/hide control for AI summaries directly in the side-panel header.
Keyword Traceability & Drill-Down: Transformed “Top Keywords” into actionable insights through network-chart highlights and an interactive drill-down widget that surfaces each highlighted keyword within its original article.
Optimized Interaction Patterns: Improved setup visibility and moved high-friction editing tasks from the cramped side panel into dedicated, spacious modals.
conclusion
The Conversation Clusters redesign transformed a technically complex machine learning feature into a more accessible and scalable analytical workspace. By simplifying setup, improving visualization readability, and creating a portable widget architecture, the experience connected complex data exploration with the broader enterprise analytics workflow.