Understanding the Challenge
Power BI semantic models represent significant organizational investment. Analysts spend months defining measures, building relationships, and encoding business logic. These models become the authoritative source of truth for metrics like revenue, customer lifetime value, or operational efficiency.
Yet accessing this intelligence requires Power BI expertise. Users need to know which dataset to connect to, how to navigate complex measure hierarchies, and how to write DAX queries. The sophisticated analytics locked in these models remain accessible only to Power BI experts.
Connecting AI to Your Semantic Models
Querex integrates directly with Power BI datasets and SSAS tabular models. Your AI assistant gains full access to your existing business intelligence infrastructure:
Model Discovery
The system automatically discovers all Power BI workspaces and datasets you have access to, including Power BI Desktop instances running on your machine.
- List all available workspaces and datasets
- Connect to Power BI Service, Azure Analysis Services, or SQL Server Analysis Services
- Discover tables, measures, and relationships within each model
- Access both cloud-hosted and on-premises models
Measure Intelligence
Your AI assistant understands the business logic already encoded in your DAX measures. When you ask for "total revenue" or "customer churn rate," it knows exactly which measure to use.
- Query any measure in your semantic model
- Understand measure definitions and dependencies
- Slice and filter using model dimensions
- Respect row-level security (RLS) settings
Natural Language to DAX
Complex DAX queries that normally require expertise are generated automatically from your natural language questions.
Example questions:
- "Show me total sales by product category for this quarter"
- "What's our year-over-year revenue growth by region?"
- "Which customers account for 80% of our revenue?"
- "Calculate average order value by sales channel and month"
What You Can Do
Querying Business Metrics
A finance director needs to review quarterly performance. She asks: "Show me revenue, gross margin, and operating expenses by business unit for Q3, compared to budget."
The AI assistant queries your Power BI model, pulling the exact measures your finance team has defined—including all the business logic for revenue recognition, cost allocation, and budget variance calculations.
Model Documentation
New analysts joining your organization need to understand your data model. Instead of reading outdated documentation, they can ask: "What measures are available in the Sales model? Show me how Customer Lifetime Value is calculated."
The system explains your model structure, shows measure definitions, and describes relationships between tables—providing live, always-current documentation.
Cross-Model Analysis
An executive wants insights that span multiple models. She asks: "Compare our sales performance from the Sales model with marketing spend from the Marketing model, calculating ROI by campaign."
The AI assistant queries both models, combines the results, and calculates derived metrics—analysis that would normally require custom Power BI reports or Excel manipulation.
Technical Capabilities
Connection Support
- Power BI Service (cloud workspaces)
- Power BI Desktop (local instances)
- Azure Analysis Services
- SQL Server Analysis Services (Tabular)
- Premium capacity and shared workspaces
Query Capabilities
- DAX query generation and execution
- Measure evaluation with filters
- Table and column queries
- Calculated columns and tables
- Time intelligence functions
- Complex filtering and slicing
Model Intelligence
- Enumerate all tables and columns
- List measures with definitions
- Discover relationships and cardinality
- View hierarchies and drill paths
- Access model metadata
- Understand role-based security
Advanced Features
- Usage metrics and activity tracking
- Refresh history and status
- Dataset size and performance info
- User access patterns analysis
- Model comparison across workspaces
- DMV (Dynamic Management Views) queries
Real-World Impact
Organizations build Power BI models to centralize business logic and ensure consistent metrics. But the value of these models is limited by accessibility. If only Power BI experts can use them effectively, then most of the organization still relies on ad-hoc Excel calculations and inconsistent definitions.
When your AI assistant can query Power BI models directly, those carefully defined metrics become accessible to everyone. The sales manager, the operations director, the marketing analyst—they all get answers based on the same authoritative definitions, without needing to become Power BI experts.
This doesn't replace Power BI. Your analysts still build and maintain models in Power BI Desktop. Your executives still view dashboards in the Power BI Service. But now, anyone who needs a quick answer to a data question can get it through conversation, powered by the same business logic your organization has already invested in defining.
Why This Matters for Analytics Teams
Analytics teams spend significant time answering ad-hoc questions: "What was revenue last month?" "How many new customers did we acquire?" "What's our top product by margin?"
These questions are straightforward but time-consuming. Each one interrupts focused work. With AI access to semantic models, these routine questions get answered instantly, freeing analytics teams to focus on complex analysis and strategic insights.
See Your Power BI Models in Action
Connect your AI assistant to your actual Power BI workspaces. We'll show you real queries on your semantic models.
Request a Demo