Integrations

Supercharge Microsoft Power BI with Relevance AI

Microsoft Power BI is a powerful tool for data visualization and business intelligence, allowing users to manage datasets and generate insights. With Relevance AI, you can elevate your Power BI experience by leveraging AI Agents for real-time data updates and automated reporting.

Give your AI Agents Microsoft Power BI Superpowers

Microsoft Power BI empowers users with advanced data visualization and business intelligence capabilities. Relevance AI amplifies this by enabling intelligent AI Agents to automate data management and reporting processes.

Real-time Data Orchestration

The AI agent dynamically coordinates and synchronizes multiple data streams for instant visualization and analysis.

Predictive Insight Generation

Leverages historical patterns and trends to automatically forecast future business metrics and outcomes.

Intelligent Anomaly Detection

Proactively identifies and alerts users to data patterns that deviate from expected trends.

Tools

Equip AI Agents with the Microsoft Power BI Tools they need

Relevance AI seamlessly integrates with Microsoft Power BI to enhance your data visualization workflows.

Microsoft Power BI - Add Rows to Dataset Table
Adds new data rows to an existing Power BI dataset table, enabling dynamic updates to Push Datasets that were created through the API
Microsoft Power BI - Refresh Dataset
Triggers a refresh operation for a specified Power BI dataset to update its data with the latest information from the source
Microsoft Power BI - Create Dataset
Creates a new dataset in Power BI with specified tables and schema definitions, allowing for custom data structure implementation
Microsoft Power BI - Cancel Dataset Refresh
Terminates an ongoing dataset refresh operation in Power BI, providing control over long-running refresh processes
Microsoft Power BI - Get Dataset Refresh
Retrieves the refresh history and status information for a specified Power BI dataset, including details about recent refresh operations
Name
Microsoft Power BI API Call
Description
Make an authorized request to a Microsoft Power BI API
Parameters
["OAuth account authentication", "HTTP method selection (GET, POST, PUT, DELETE, PATCH)", "Custom request headers", "Request body configuration", "API endpoint path customization"]
Use Case
A data analytics team uses this integration to automatically refresh their Power BI dashboards whenever new data arrives in their data warehouse, ensuring executives always see the latest KPIs without manual intervention.
Quick Start

Connect Microsoft Power BI to Relevance AI in minutes

Security & Reliability

The integration enables secure OAuth authentication, ensuring that only authorized workflows can access your Microsoft Power BI data. Relevance AI manages API operations (such as creating datasets, adding data, refreshing datasets, and checking refresh status) in the background—so you don’t have to worry about errors, formatting, or limits.

Built-in validation and type conversion ensure your workflows run smoothly, even when data formats vary, allowing for automated dataset management and real-time data updates.

No training on your data

Your data remains private and is never utilized for model training purposes.

Security first

We never store anything we don’t need to. The inputs or outputs of your tools are never stored.

Get Started

Best Practices for Non-Technical Users

To get the most out of the Microsoft Power BI + Relevance AI integration without writing code:
  • Start with a well-structured dataset: Ensure your dataset schema is clear and follows best practices for data types and naming conventions.
  • Utilize pre-built workflows: Relevance AI provides templates for common operations like dataset creation, data updates, and refreshes—perfect for quick implementation.
  • Connect with care: Double-check that you are linking to the correct Power BI workspace and dataset during the setup process.
  • Test before scaling: Conduct tests with sample data to validate your automations and avoid potential issues in production.
  • Monitor refresh operations: Keep an eye on dataset refresh statuses and implement appropriate polling intervals to manage performance effectively.