The Invoice Validation Agent automates the validation of invoices against purchase orders. It enables the precise and efficient processing of invoice data, significantly reducing manual intervention and enhancing overall financial operations.

The manual process of validating invoices is resource-intensive and error-prone. Teams must monitor their inboxes, manually open invoice attachments to extract key details, and then tediously compare these against purchase order information within the ERP system. This can lead to potential errors and duplications and also delays payment processing, adversely affecting cash flow and overall efficiency.
‍

The Invoice Validation Agent streamlines invoice processing by automatically extracting key invoice details and cross-referencing this data with corresponding PO details in the ERP system. Upon finding discrepancies, the agent flags them for manual review or automatically updates the ERP system if the data matches, thereby reducing errors and speeding up the processing. This efficient automation enhances accuracy, accelerates payments, and improves overall financial operations.

The Invoice Validation Agent is designed to fully automate and optimize the invoice validation workflow, ensuring both accuracy and efficiency. Upon receiving a new email in a designated inbox, the agent seamlessly triggers a series of predefined automated steps. Leveraging the power of an advanced Large Language Model (LLM), the agent conducts real-time analysis and verification at each stage, intelligently processing and validating incoming invoice data. It meticulously cross-references the invoice details with the corresponding purchase order, ensuring compliance and accuracy. The agent’s operations are fine-tuned for maximum precision, eliminating manual errors and reducing processing time.

The agent initiates by receiving emails and analyzing their content to identify invoice-related communications, eliminating irrelevant messages for an optimized workflow.

‍
Key Tasks:Email Content Classification: Using a large language model (LLM), the agent examines email structure, keywords, and contextual clues to classify each email as either invoice-related or non-invoice content.Relevance Filtering: Emails unrelated to invoices are filtered out, and an automated response is sent: "Not related to invoice," reducing the clutter and ensuring only relevant emails proceed for further processing.

‍
Outcome:Relevant Email Processed: Emails containing invoice-related content move forward to attachment processing and data extraction, streamlining subsequent steps.Non-Invoice Email Discarded: Unnecessary emails are discarded promptly, minimizing manual intervention and improving operational efficiency.

The agent inspects filtered emails for attachments and leverages advanced OCR and multimodal capabilities to extract key invoice data with high precision and accuracy.
‍
Key Tasks:Attachment Verification: The agent identifies whether the email contains attachments, focusing on documents with invoice-related data for processing.OCR for Scanned Documents: ZBrain’s OCR capabilities process scanned or image-based documents, converting them into searchable text to ensure no critical data is missed.Multimodal Analysis: The agent uses multimodal technology to interpret complex layouts, extracting structured data, including tables and multi-page information, which are essential for handling non-standard invoices.Key Data Extraction: Essential invoice details, such as invoice number, PO number, item names, quantities, prices, and total amounts, are captured from both attachments and the email body to ensure comprehensive data retrieval.
‍
Outcome:Key invoice data is successfully retrieved and ready for validation and ERP integration, minimizing errors caused by missing or incorrect data.

Once invoice data is extracted, the agent matches it with purchase orders in the ERP system, performs validation, and updates the system to complete the transaction lifecycle.

‍
Key Tasks:ERP Search for PO: The agent queries the ERP system using the extracted PO number to locate corresponding purchase order records.PO Matching: The agent cross-verifies the invoice details (such as items, quantities, and prices) against the matching PO record to ensure consistency.Data Validation Rules: Customized validation rules enforce company standards, ensuring that only accurate and verified invoices proceed to the final stage.Data Comparison and Error Handling: The agent compares all extracted data against the ERP system, identifying discrepancies such as incorrect prices or missing line items.

‍
Outcome:No Matching PO Found: An automated response is generated: "The purchase order is not found," notifying relevant stakeholders for further action.Matching PO with Correct Data: The validated invoice is formatted into a human-readable report and automatically updated in the ERP system.Discrepancies Detected: The agent generates a detailed report highlighting mismatches, suggesting corrective actions for manual review to ensure financial accuracy and compliance.