IDP Overview
MuleSoft Intelligent Document Processing (IDP) enables you to read invoices, purchase orders, and other unstructured or semi-structured documents and then analyze and refine the extracted content using AI capabilities to create a structured response.
With the simple IDP interface, you can create and publish document actions as APIs to use for further integration with RPA, Mule applications, and other systems, without subscribing to external services.
A document action is a multi-step process that uses multiple AI engines to scan a document, filter out fields, and return a structured response as a JSON object. Each document action defines the types of documents it expects as input, the fields to extract, and the fields to filter out from the response. You can hide fields, mark fields as required, configure the minimum confidence score accepted for each field to extract, and configure Prompts to enhance and refine the data-extraction process by asking questions using natural language.
The confidence score represents the probability that IDP has properly extracted the value from a document. For example, a confidence score of 100% means that IDP extracted the value with total accuracy. However, a confidence score of 75% means that there’s a 25% chance that the extracted value is not correct.
Each processed document shows a confidence score for each extracted field. When this value is lower than the defined threshold, IDP sends the document for review by a human to verify the accuracy of the extracted values. You can add single reviewers or teams to each document action.
For an introduction to IDP, see our Trailhead badge, MuleSoft IDP Basics (login required). Sign up if you don’t have a Trailhead account.
Enhance Data Extraction With Einstein
When you add prompts to your document actions, you can select wether to use the default or Einstein’s response. Einstein can answer complex questions that require further analysis of the document instead of just searching and extracting a field. For example, you can ask Einstein what’s the total amount due in an invoice after deducting taxes and other values from the document.
Use Einstein to analyze documents that don’t use a standard format or are difficult to read without performing a complex analysis of the extracted data, such as a driver’s license or a certificate of medical leave.
For document analysis in IDP, Einstein uses OpenAI’s GPT4Omni_05_13 LLM Gateway model accessed through the Salesforce Einstein Trust layer, which is part of the Salesforce Einstein platform.