Could you tell these receipts from real ones. These are actual examples of AI generated receipts that have been submitted by employees at work filing expenses and they're almost impossible to distinguish from legitimate ones. This is a massive problem for systems like Concur and Appzen who help many organizations with their expense processing.And you don't need Photoshop. You don't even need a printer. And if you look very closely at these examples, you'll see the details that are there.Wrinkles in the pape ...

Key Functionality & Customization - Llama Index provides a customizable extraction review template for specific data like receipts and invoices [1] - The template allows users to focus on schema development rather than building scaffolding [2] - Users can update front-end schemas to reflect changes in the data extraction process [3] - The system flags fields with low confidence, enabling efficient review and validation [4] - Users can deploy custom extraction agents with validation on Llama Cloud [6] Deployment & Data Handling - The deployed app can have a distinct production dataset compared to the local environment [5] - Users can re-upload data to improve extraction results and achieve valid states [4][6] Application & Validation - The system is designed for messy and hard-to-read documents with multiple line items and taxes [1] - The custom extraction agent includes validation to ensure accuracy [6]

Product Overview - Llama Index introduces a customizable extraction review template for specific data types, focusing on receipts and invoices [1] - The template aims to streamline the extraction process from messy and varied line items, including taxes [1] - The solution includes a validation feature to ensure data accuracy [6] - The custom extraction agent can be deployed on Llama Cloud [6] Schema Development & Customization - Schema development is a key aspect, and the template helps users focus on this task [2] - The system allows for updating front-end schemas and re-uploading data to reflect changes [3][4] - Users can build custom UI around receipts data for better context [5] Deployment & Validation - The deployed app allows for distinct production data sets [5] - The system highlights fields with low confidence, enabling efficient review [4] - Validation ensures the extracted data is accurate [6]