Solution Overview

The automation performs the following tasks:

• Monitors a shared mailbox for incoming emails.
• Extracts invoice data from PDF attachments using AI.
• Stores the extracted data in a structured Dataverse table.

This eliminates manual data entry and ensures that invoice records are consistently and reliably captured.

Technologies Used

• Power Automate – Workflow automation
• AI Builder (Invoice Processing) – Intelligent document processing
• Dataverse – Centralized data storage
• Outlook Connector – Email monitoring

AI Model Training

The Invoice Processing model in AI Builder was trained using sample invoices tailored to the organization’s format. This improved the model’s ability to accurately extract fields such as:

• Invoice ID
• Vendor Name
• Invoice Date
• Total Amount

Training involved uploading labeled documents and validating the model’s predictions to ensure consistent accuracy.

Click on the AI Models

Click on prebuilt invoice processing model and train it

In left hand side we have 5 steps and here you have to choose document which is invoices and then extract information from the pdf and after that add collections of items which you have to train and then tag each components in pdf as per the tag ,it has certain fields to get using few tables by default you can use your own custom fields here.

 Cloudflow Steps

The Power Automate flow follows a structured sequence:

1. Email Trigger

The flow starts when a new email arrives in a shared mailbox.

2. Attachment Loop

Each attachment in the email is processed individually.

3. Get Attachment

The PDF file is retrieved from the email.

4. AI Prediction

The file is sent to AI Builder’s Invoice Processing model, which extracts structured data.

5. Field Extraction

Specific fields such as ID and Name are isolated using Compose actions.

6. Dataverse Entry

The extracted data is inserted into a Dataverse table as a new record.

Dataverse Table Schema

Flow Diagram

Below is the visual representation of the Power Automate flow:

This diagram outlines the automation steps from email monitoring to data storage.

Key Advantages

• AI-Driven Accuracy: Reduces errors in data extraction.
• Automated Cloudflow: Eliminates manual intervention.
• Centralized Storage: Dataverse provides secure and scalable data management.
• Adaptable Design: Can be extended to support additional document types or fields.

Conclusion

This solution demonstrates how Power Platform components can be combined to automate invoice handling efficiently. By integrating AI Builder, Power Automate, and Dataverse, invoice data is captured reliably and stored in a format ready for analysis, reporting, or integration with other systems.