Microsoft Fabrics x Agent Lucid AI
PRODUCT DESIGN
WEB APP
AI
Role
UX research, Workflow design,
UI Design, User testing
Impact
82% faster processing,
207% higher approvals, and 40%
fewer loops
Team
1 Designer, 1 PM, 2 ML Engineers,
2 Developers
Overview
What is Lucid Data Hub & AI Agents?
Lucid Data Hub unifies fragmented enterprise data and makes it accessible through AI-powered automation. Its built-in AI Agents analyze data, answer questions, and trigger actions using natural language—reducing manual work and enabling faster, smarter decisions.
Why Integrate with Microsoft Fabric?
By embedding Lucid Data Hub and AI Agents into Microsoft Fabric, businesses unlock the power of real-time, secure, and scalable analytics within the tools they already use. Integration with Fabric’s data lakes, warehouses, and Power BI eliminates data silos and removes the need for extra ETL processes. This unified environment streamlines workflows, accelerates insights, and enables teams to move from data to decisions with unprecedented speed and efficiency.
Problem Context
Traditional sales and data operations face several challenges that hinder efficiency and data insights.
• Siloed Data: Enterprise info is fragmented across tools limiting insight.
• Manual Sales Processes: Data prep and outreach require time-consuming human effort.
• Usability Gaps: Most data/AI tools are hard for non-technical users.
Integrated Solution Vision
Embedding AgentLucid into Microsoft Fabric eliminates data silos, streamlines setup, and gives every authorized user instant access to actionable insights and AI-powered automation.
Outcome
Before
After
User
Satisfaction
Setup Time
User
Confidence
Time to
Insights
• User Statisfaction increased significantly after integrating AI agents and guided workflows.
• Setup Time reduced by 75%, enabling faster onboarding and agent deployment.
• User Confidence boosted by 80%, with non-technical users finding the interface intuitive and transparent.
• Time to Insights improved by 60%, allowing quicker decision-making compared to manual workflows.
Fabric - Integrated User Journey
Key UX Challenges
Problem:
Lucid’s original experience was unified and tightly integrated. But Microsoft Fabric’s architecture required it to be broken into three standalone apps—Agent, Copilot, and Builder each appearing as a separate item in the Fabric ecosystem.
Solution:
You introduced a Context Hub to act as a common anchor, allowing users to associate all agent/chat activities to a central object solving the feeling of disconnection.This also supported with :
Shared UI
Deep linking between tools
Role-based separation
Modular deployment compatibility with Fabric
3 Core Features
Context Hub - Understand and explains your company's data.
AI Agents - Lets you create smart helpers to watch data and do work automatically
Lucid Chatbot - Data Assistant that answers questions in plain language
Context Hub : Setting the stage for smart Data Use
The Context Hub serves as the foundational intelligence layer of the platform. It connects to enterprise data sources and builds a data dictionary that describes the meaning of each table and field in human-understandable terms.
Why it's Important
This ensures that every AI action, automation, or insight is grounded in shared, accurate understanding of the data. It avoids misinterpretation by downstream tools like agents and chatbots, and removes ambiguity for both technical and non-technical users interacting with business-critical data.
How it works
Users give Context Hub a name, industry, and region to tailor how data is interpreted.
Users begin by connecting a secure data workspace (like a lakehouse).
The system performs an AI-driven scan of the data structure identifying columns, field types, and naming conventions.
Once connected to a data source, the system scans and extracts fields; AI then suggests definitions through a guided review interface to build the data dictionary.
AI Agent Builder : Creating your helpers
A no-code interface that enables users to create intelligent agents that monitor data, evaluate conditions, and trigger automated workflows like alerts, emails, and report generation.
Why it's Important
It empowers non-technical users like operations managers, analysts, or admins to independently automate workflows that would otherwise require developer support. The visual structure helps users reason about the logic, while tight integration with the Context Hub ensures the agents are always referencing well-defined, trustworthy data.
How it works
Users begin by selecting an existing or create a new Context Hub or select Industry or domain name.
Users choose from pre-built templates relevant to their selected context.
Users construct agents by dragging and linking modular logic blocks. These blocks can be connected to create conditional workflows.
The agents run continuously or on schedule, acting like background co-workers who monitor KPIs and respond to business rules automatically.
Chatbot : Your Data Assistant
An AI-powered chatbot that allows users to ask natural language questions or issue commands and get live answers from their data without navigating dashboards or writing queries.
Why it's Important
It democratizes access to insights by removing the technical barrier of BI tools or SQL. Users can self-serve information or activate automations via a simple chat interface, improving productivity and reducing dependency on technical teams.
How it works
Users begin by selecting an existing or create a new Context Hub.
Chat Interface to ask natural language questions or issue commands.
Case Example
Smart Inventory Management at a Retail Store
A retail store that stocks hundreds of products. Traditionally, the store manager spends hours checking inventory levels, sales reports, and manually contacting suppliers to reorder stock. This can cause delays or stock shortages.How AgentLucid Helps :
1. Context Hub: The store’s data is stored in their Microsoft Fabric workspace and lakehouse. The manager sets this up in Context Hub by explaining that this data covers “Product Inventory” and “Sales Records.” The system scans all the tables and builds a detailed dictionary explaining what each column means, such as “Product ID,” “Quantity in Stock,” and “Reorder Threshold.”
2. AI Agent Builder: Using the drag-and-drop tool, the manager builds a smart helper: “Check inventory levels daily, and if any product is below the reorder threshold, send a notification to the purchasing department to reorder.” This agent works automatically without constant supervision.
3. Chatbot: At any time, the store manager can ask the chatbot inside Microsoft Fabric: “Which products are running low today?” The chatbot replies with a clear list, including notes on which orders are already placed, thanks to the AI agent’s work.
Outcome
The store avoids running out of popular products, reduces manual workload, and speeds up its restocking process all with simple tools anyone can use.
Testing & Validation
Made with Love, Framer,
and a lot of Ctrl+Z