BrightHive
Democratizing Advanced Data Analysis with AI Agents
BrightHive sought to democratize their data platform by integrating AI agents that make complex analytics accessible to non-expert users. We defined comprehensive user flows and workflows for different personas, designed an innovative AI agent interaction model, and created the UX/UI that brings it all together.
The result is a platform where specialized AI agents collaborate through conversational interfaces, handling complex data tasks while maintaining transparency. This approach transforms advanced data analysis from an expert-only capability into something accessible to anyone through natural language interactions.
Orchestrating AI Agents
A team of AI Agents at your service!
The centerpiece of our solution was a hierarchical AI system where specialized agents work together under "The Queen" coordinator. This architecture handles complex tasks behind a simple interface, making advanced data operations accessible to non-technical users.
The right side displays the chat interface with an activity panel showing AI processes in real-time. Users get both conversation and transparency, building trust through visibility into the AI's work.
User Flow Mapping
Collaborative sessions with the BrightHive team to map essential user flows. Each colored sticky represents different user actions or system responses, creating a visual journey map for non-technical users navigating the platform with AI assistance.
Detailed Workflows
Screen-by-screen mapping of critical processes, showing exactly how users progress from question to insight with BrightBot's help. This detailed approach ensured we maintained technical requirements while simplifying the experience for non-data experts.
Chat Interface Evolution
Our iterative design process explored multiple interface solutions for the AI chat experience. Through these variations, we tested different patterns—from drawers to panels to step navigators—to find the right balance of simplicity and functionality that would serve both technical and non-technical users.
Design Evolution
Early digital mockups exploring different approaches to the AI chat experience. These designs tested various concepts for displaying collaborative AI agents, integrating data visualizations within conversations, and maintaining context as users move through complex analysis workflows.
The design evolved through multiple iterations, from drawer-based navigation to panels and step navigators, each refining the experience for different user types and workflows.
Anatomy of a new UI
The final interface design includes:
Transparent AI
BrightBot creates detailed plans using specialized agents when handling complex requests. This dual-view interface reveals the AI's process, building trust by showing users both the planned approach (left) and real-time execution (right).
This transparency serves two key purposes: it lets users understand and modify the process at any point, while educating them about data operations that would typically require technical expertise.
Talk to Your Data Like a Human
One of BrightBot's most powerful features is letting you ask questions about your data in plain English. Simply type "Show me last month's sales by region" or "Which customers bought the most?" and get instant answers.
The system translates natural language into technical queries behind the scenes, enabling anyone to explore data through conversation while maintaining accuracy for technical teams. This approach removes barriers between business users and their data insights.
Flexible Technical Depth
Our dual-mode interface adapts to each user's technical comfort level. The "Preview Data" view (left) presents insights in business-friendly formats with explanatory text, while the "See Code" toggle (right) reveals the technical implementation for those who want to verify or learn from the process.
This approach allows the platform to serve both technical and non-technical users without compromise.
The Impact
Since launching the redesigned BrightBot experience, we've seen positive user engagement and adoption. The conversational AI interface has made data analysis more accessible across teams, allowing users to get insights through natural dialogue.