Supercharge Power Automate Desktop with AI Builder!

Explore how AI Builder enables machine-learning driven decisions with classification and regression, and learn how input maps to analysis and output within Power Automate Desktop.

Discover how to analyze social media reviews for sentiment using AI Builder in Power Automate, pulling data from Google Sheets and labeling entries as positive, negative, or neutral.

Learn to automatically extract contact details from business cards and create new CRM records using Power Automate Desktop with AI Builder, leveraging prebuilt flows and templates.

Learn to extract invoice data using the pre-built invoice reader model, configure Google Drive and Sheets, and automatically insert extracted fields into a sheet for expense tracking.

Automate expense tracking by processing receipts from a Google Drive folder and extracting vendor, date, total, and taxes into the Google Sheets Receipts sheet via Power Automate.

Load and structure your data in data verse by creating a prediction table, defining column data types, and importing from Excel to map fields for AI Builder models.

Train a real estate price prediction model in Power Otomi by selecting key features such as bathrooms, bedrooms, date sold, region, suburb, and property type.

Create a manual flow in Power Automate Desktop using AI Builder to predict home prices with a trained model, feeding date, region, suburb, bedrooms, bathrooms, and property type.

Develop a hotel review classification model to group feedback into complaints and compliments, using AI Builder with Power Automate Desktop, available to run anytime, aiming for 60 percent accuracy.

Create a hotel reviews text classification model from loaded data, select text and tags with separators, train in English, publish, and integrate into a flow.

Finish integrating the hotel reviews classification model into a Power Automate flow, test with sample text, and explore data sources and connectors, achieving 81% confidence that result is a compliment.

Build and evaluate an AI Builder entity extraction model to extract sale price, city, bedrooms, and bathrooms from real estate listings, using data loading, training, and testing workflows.

Prepare and clean spreadsheet data, create an entity extraction table with a listing text column, load and map data, and verify loading success.

Create a real estate listings flow using a custom entity extraction model in a cloud float, then test and review extracted address, bedrooms, bathrooms, and money.

Harness ai builder to extract lease application data and auto-enter it into a web-based tracking system, as part of a two-part Power Automate Desktop project.

Demonstrate a three-part AI Builder workflow for lease automation by analyzing lease documents and passing extracted data to Power Automate Desktop for automated data entry.

Build a form processing model by tagging fields in lease applications, training five documents, and testing on a sixth to automatically extract name, address, date, occupation, occupants, vehicle, and notes.

Learn to connect your machine to Power Automate Desktop, so you can trigger desktop flows from cloud flows by adding and registering a machine and selecting it in cloud flows.

Define input variables in a desktop flow, pass name, address, date, occupation, occupants, vehicles, and notes from the cloud flow, publish the model, and trigger the desktop flow from cloud.

Automate a desktop flow by recording a web login, filling lease forms, and importing PDF data with AI Builder; then run and verify the cloud and desktop flows integrate seamlessly.

Conclude by exploring next steps in power automate desktop, ai builder, and cloud flows, with process advisor insights, and consider applying forms and predictions in power apps.