Invoice Processing (Use Case)

This article is about the current version of Grooper. Note that some content may still need to be updated.

2025

You may download the ZIP files below for use in your own Grooper environment (version 2025). These are Project ZIP files. 2025 Project – AI Invoice Processing (Scanned) 2025 Project – AI Invoice Processing (File Import) 2025 Project – AI Invoice Processing (Emailed) This is a Batch with example email scenarios: 2025 Batch – Example Email Scenarios This is a normal ZIP file containing multiple image based invoice examples: Sample Invoices – Image-Based PDFs

Introduction

Invoice Processing showcases how Grooper can automate the capture, understanding, validation, and organization of invoice documents using a combination of DI OCR, data extraction, review workflows, and AI-enabled capabilities. This article demonstrates a realistic business use case that reflects how organizations process accounts payable documents in production environments.

The intention of this article is to move beyond isolated feature demonstrations and show how Grooper’s technologies work together as part of a complete invoice processing solution. Rather than focusing on a single Activity or configuration object, this guide illustrates how invoices move through a coordinated workflow—from document ingestion and recognition to structured data extraction, validation, review, and downstream use.

This use case highlights several core Grooper concepts, AI Extract, Azure DI OCR, and more. This is a one-size-fits-all approach to invoice processing.

By the end of this guide, readers will have a foundational understanding of how Grooper can be used to build an end-to-end invoice processing solution and how the platform’s modular architecture supports scalable, production-ready document automation workflows.

Setup for AI Extract

This portion of the article focuses on configuring Grooper’s AI Extract capability so documents can be analyzed by a Large Language Model (LLM) and mapped into a Data Model. It involves setting up an LLM Connector within the Grooper Repository and selecting an appropriate model through the Data Model’s Fill Methods.

The goal of this configuration is to enable Grooper to interpret document content and populate generic fields—such as document identifiers, dates, and party information—without relying on rigid, template-based extraction. This setup establishes the connection between Grooper and the external LLM provider, ensuring AI Extract can execute during Batch Processing.

1. Select the Root node, then click the ellipsis button for the Options property to open the Options editor.
2. Add an LLM Connector, then be sure to properly configure it.
   • The most important configuration is choosing a service provider for the Service Provider property, and properly configuring it.
3. Expand the Node Tree and select the Data Model from the provided "AI Invoice Processing (File Import)" Project, then click the ellipsis button for the Fill Methods property to open the "Fill Methods" editor.
4. Expand the Generator sub-properties and be sure to select a desired model for the Model property.

For More Information

• AI Extract
• Activity Processing
• Azure DI OCR
• Batch
• Batch Folder
• Batch Page
• Batch Process
• Batch Process Step
• Behaviors
• Content Model
• Data Model
• Extract
• Fill Method
• Import Watcher
• LLM Connector
• Machine
• Node Tree
• OCR Profile
• Project
• Recognize
• Repository
• Review
• Root
• Split Pages