Overview

With the introduction of Azure AI Foundry, integrating various AI services to streamline AI solution development and deployment of Agentic AI Workflow solutions like multi-modal, multi-model, dynamic & interactive Agents etc. has become more efficient. The platform offers a range of AI services, including Document Intelligence for extracting data from documents, natural language processing and robust machine learning capabilities, and more. Microsoft Fabric further enhances this ecosystem by providing robust data storage, analytics, and data science tools, enabling seamless data management and analysis. Additionally, Copilot and GitHub Copilot assist developers by offering AI-powered code suggestions and automating repetitive coding tasks, significantly boosting productivity and efficiency.

Objectives

In this use case, we will use monthly electricity bills from the utilities' website for a year and analyze them using Azure AI services within Azure AI Foundry. The electricity bills is simply an easy start but we could apply it to any other format really. Like say, W-2, I-9, 1099, ISO, EHR etc.

By leveraging the Foundry's workflow capabilities, we will streamline the development stages step by step. Initially, we will use Document Intelligence to extract key data such as usage in kilowatts (KW), billed consumption, and other necessary information from each PDF file. This data will then be stored in Microsoft Fabric, where we will utilize its analytics and data science capabilities to process and analyze the information. We will also include a bit of processing steps to include Azure Functions to utilize GitHub Copilot in VS Code. Finally, we will create a Power BI dashboard in Fabric to visually display the analysis, providing insights into electricity usage trends and billing patterns over the year.

Utility Invoice sample

 

 

Building the solution

Depicted in the picture are the key Azure and Copilot Services we will use to build the solution.

 

 

 

Set up Azure AI Foundry

Create a new project in Azure AI Foundry. Add Document Intelligence to your project. You can do this directly within the Foundry portal.

 

 

Extract documents through Doc Intel

Download the PDF files of the power bills and upload them to Azure Blob storage. I used Document Intelligence Studio to create a new project and Train custom models using the files from the Blob storage. Next, in your Azure AI Foundry project, add the Document Intelligence resource by providing the Endpoint URL and Keys.

 

 

Data Extraction

Use Azure Document Intelligence to extract required information from the PDF files. From the resource page in the Doc Intel service in the portal, copy the Endpoint URL and Keys. We will need these to connect the application to the Document Intelligence API.

Next, let’s integrate doc intel with the project. In the Azure AI Foundry project, add the Document Intelligence resource by providing the Endpoint URL and Keys. Configure the settings as needed to start using doc intel for extracting data from the PDF documents. We can stay within the Azure AI Foundry portal for most of these steps, but for more advanced configurations, we might need to use the Document Intelligence Studio.

GitHub Copilot in VS Code for Azure Functions

For processing portions of the output from Doc Intel, what better way to create the Azure Function than in VS Code, especially with the help of GitHub Copilot. Let’s start by installing the Azure Functions extension in VS Code, then create a new function project. GitHub Copilot can assist in writing the code to process the JSON received. Additionally, we can get Copilot to help generate unit tests to ensure the function works correctly. We could use Copilot to explain the code and the tests it generates. Finally, we seamlessly integrate the generated code and unit tests into the Functions app code file, all within VS Code.

Notice how we can prompt GitHub Copilot from step 1 of Creating the Workspace to inserting the generated code into the Python file for the Azure Function to testing it and all the way to deploying the Function.

 

 

Store and Analyze information in Fabric

There are many options for storing and analyzing JSON data in Fabric. Lakehouse, Data Warehouse, SQL Database, Power BI Datamart. As our dataset is small, let’s choose either SQL DB or PBI Datamart. PBI Datamart is great for smaller datasets and direct integration with PBI for dashboarding while SQL DB is good for moderate data volumes and supports transactional & analytical workloads.

To insert the JSON values derived in the Azure Functions App either called from Logic Apps or directly from the AI Foundry through the API calls into Fabric, let’s explore two approaches. Using REST API and the other Using Functions with Azure SQL DB.

Using REST API – Fabric provides APIs that we can call directly from our Function to insert records using HTTP client in the Function’s Python code to send POST requests to the Fabric API endpoints with our JSON data.

 

 

Using Functions with Azure SQL DB – we can connect it directly from our Function using the SQL client in the Function to execute SQL INSERT statements to add records to the database.

 

 

While we are at it, we could even get GitHub Copilot to write up the Unit Tests. Here’s a sample:

 

 

Visualization in Fabric Power BI

Let's start with creating visualizations in Fabric using the web version of Power BI for our report, UtilitiesBillAnalysisDashboard. You could use the PBI Desktop version too. Open the PBI Service and navigate to the workspace where you want to create your report. Click on "New" and select "Dataset" to add a new data source. Choose "SQL Server" from the list of data sources and enter "UtilityBillsServer" as the server name and "UtilityBillsDB" as the DB name to establish the connection. Once connected, navigate to the Navigator pane where we can select the table "tblElectricity" and the columns. I’ve shown these in the pictures below. For a clustered column (or bar) chart, let us choose the columns that contain our categorical data (e.g., month, year) and numerical data (e.g., kWh usage, billed amounts). After loading the data into PBI, drag the desired fields into the Values and Axis areas of the clustered column chart visualization. Customize the chart by adjusting the formatting options to enhance readability and insights. We now visualize our data in PBI within Fabric.

 

 

We may need to do custom sort of the Month column. Let’s do this in the Data view. Select the table and create a new column with the following formula. This will create a custom sort column that we will use as ‘Sum of MonthNumber’ in ascending order.

 

 

 

 

 

 

 

Other visualizations possibilities:

 

 

 

 

 

 

Other Possibilities

Agents with Custom Copilot Studio

Next, you could leverage a custom Copilot to provide personalized energy usage recommendations based on historical data. Start by integrating the Copilot with your existing data pipeline in Azure AI Foundry. The Copilot can analyze electricity consumption patterns stored in your Fabric SQL DB and use ML models to identify optimization opportunities. For instance, it could suggest energy-efficient appliances, optimal usage times, or tips to reduce consumption. These recommendations can be visualized in PBI where users can track progress over time.

To implement this, you would need to set up an API endpoint for the Copilot to access the data, train the ML models using Python in VS Code (let GitHub Copilot help you here… you will love it), and deploy the models to Azure using CLI / PowerShell / Bicep / Terraform / ARM or the Azure portal. Finally, connect the Copilot to PBI to visualize the personalized recommendations.

Additionally, you could explore using Azure AI Agents for automated anomaly detection and alerts. This agent could monitor electricity bill data for unusual patterns and send notifications when anomalies are detected.

Yet another idea would be to implement predictive maintenance for electrical systems, where an AI agent uses predictive analytics to forecast maintenance needs based on the data collected, helping to reduce downtime and improve system reliability.

Summary

We have built a solution that leveraged the seamless integration of pioneering AI technologies with Microsoft’s end-to-end platform. By leveraging Azure AI Foundry, we have developed a solution that uses Document Intelligence to scan electricity bills, stores the data in Fabric SQL DB, and processes it with Python in Azure Functions in VS Code, assisted by GitHub Copilot. The resulting insights are visualized in Power BI within Fabric. Additionally, we explored potential enhancements using Azure AI Agents and Custom Copilots, showcasing the ease of implementation and the transformative possibilities.

Finally, speaking of possibilities – With Gen AI, the only limit is our imagination!

Additional resources

• Explore Azure AI Foundry
• Start using the Azure AI Foundry SDK
• Review the Azure AI Foundry documentation and Call Azure Logic Apps as functions using Azure OpenAI Assistants
• Take the Azure AI Learn courses
• Learn more about Azure AI Services Document Intelligence: Azure AI Doc Intel
• GitHub Copilot examples: What can GitHub Copilot do – Examples
• Explore Microsoft Fabric: Microsoft Fabric Documentation
• See what you can connect with Azure Logic Apps: Azure Logic Apps Connectors

About the Author

Pradyumna (Prad) Harish is a Technology leader in the GSI Partner Organization at Microsoft. He has 26 years of experience in Product Engineering, Partner Development, Presales, and Delivery. Responsible for revenue growth through Cloud, AI, Cognitive Services, ML, Data & Analytics, Integration, DevOps, Open Source Software, Enterprise Architecture, IoT, Digital strategies and other innovative areas for business generation and transformation; achieving revenue targets via extensive experience in managing global functions, global accounts, products, and solution architects across over 26 countries.