Verizon’s AIM reporting dashboard

Thousands of AI conversations.
One dashboard.

Designed to reveal where chatbot conversations fail, uncover patterns, and help teams continuously improve the customer experience.

 

User Workflow

Admin → Developer → Conversationalist → Lead Conversationalist → Q&A → Production

My focus was designing for the Developer, enabling teams to quickly detect chatbot fallouts and uncover opportunities to improve AI responses.

 

Design System & Layout

UI Framework

Built with Google Material Design, the interface uses familiar components for tables, filters, charts, and navigation. The design system provides a consistent, scalable foundation while keeping complex data clear and approachable.

Layout

Designed specifically for desktop, the dashboard uses a structured grid to organize filters, analytics, and conversation data into a clear visual hierarchy. The layout helps teams quickly move from high-level trends to detailed investigation without losing context.

 

Information Hierarchy

Designed for quick decision-making, the interface surfaces the most important information first while keeping supporting actions within easy reach.

Global Navigation • Supporting Controls • Traffic & Fallout • Summary Metrics

Filters

Filters help teams quickly isolate chatbot trends and pinpoint where issues occur.

  • Quick View

  • Conversation Type

  • Channel

  • Entry Point

  • Logged-in State

Data Visualization & Analysis

Designed to surface insights at every level—from overall chatbot performance to individual conversations.

  • Timeline — Reveals traffic and fallout patterns.

  • Conversation List — Displays detailed chat records.

  • Layered Insights — Connects trends to specific interactions.

 

Prototype

Experience the dashboard in action. For the best experience, view on desktop.

View Dashboard Prototype