About the Agent

ZBrain Order Entry Management Agent automates handling purchase orders received via email, streamlining their integration into the ERP system. By leveraging A Large Language Model (LLM), the agent efficiently identifies purchase orders, extracts essential information, and either creates new orders or updates existing ones in the ERP system, minimizing manual intervention and the associated risk of errors.

Challenges the Order Entry Management Agent Addresses:

The traditional process of managing purchase orders is cumbersome and error-prone. Staff members must monitor inboxes, identify relevant emails, open attachments to extract order details, and manually enter this information into the ERP system. This process not only leads to potential delays and data entry errors but also requires additional communications for any discrepancies or missing information, further increasing processing time. Such inefficiencies can compromise order fulfillment and overall business operations.

ZBrain Order Entry Management Agent leverages an LLM to transform the purchase order management process by automating the reception and processing of POs received via email. It filters emails, identifies those with purchase orders, and extracts essential details. Once extracted, the agent automatically maps this data in the ERP system, creating or updating orders as needed. This automation reduces the likelihood of errors, speeds up order processing, and ensures more accurate order fulfillment. The agent enhances operational efficiency and scalability, supporting business growth and freeing staff to focus on strategic tasks instead of manual data entry.

How the Agent works?

The Order Entry Management Agent is designed to automate and streamline the entire order entry management workflow. The agent is activated when a new email arrives in a designated inbox, prompting it to initiate a series of well-defined, automated steps. Leveraging an advanced Large Language Model (LLM), the agent makes real-time decisions at each stage of the process. It intelligently analyzes and processes the incoming information, executing the necessary actions to ensure that every phase of the order entry process is handled with precision and efficiency. Below is a detailed breakdown of how the agent works at each step of the process:

Step 1: Email Content Analysis and Classification

Upon receiving a new email, the agent analyzes the content to determine its type—whether it is a purchase order (PO) or a non-PO communication. The LLM evaluates the structure, keywords, and context of the email to classify it correctly. The process involves several tasks:

Key tasks:

• PO Classification: The agent uses the LLM to classify the email as either a PO or non-PO. Key indicators such as PO numbers, order terms, and reference codes are identified.
• Attachment Processing: If attachments (e.g., PDFs, Word documents) are included, the agent employs OCR (Optical Character Recognition) and multimodal models to extract textual data, including order details such as item names, quantities, and prices.
• Information Extraction: The agent extracts critical fields like order number, items, quantities, and pricing details from both the email body and attachments, ensuring all relevant information is captured.

Outcome:

• PO Identification: The agent successfully classifies the email as either a PO or non-PO and extracts relevant data for further processing. If it is a PO, the necessary order details are ready for the next stage.

Step 2: ERP Order Matching and Validation

Once the order details are extracted, the agent cross-references the information with the company’s ERP system to ensure accurate matching with existing records.

Key Tasks:

• ERP Database Search: The LLM queries the ERP system using the extracted PO number to locate matching records.
• PO Matching: The agent compares the incoming PO number against existing records in the ERP database to verify its accuracy.
• Data Comparison: The agent validates the extracted details, including items, quantities, and prices, against the records in the ERP database to ensure consistency.

Outcome:

• Match Found: If a match is found but additional items or quantities are identified, the agent generates a sub-order to reflect the updated details.
• Duplicate Order: If the order already exists in the system, the agent identifies it as a duplicate and rejects the new entry to to avoid redundancy.
• No Match: If no match is found, the agent proceeds with the creation of a new order in the ERP system.

Step 3: Order Creation and Finalization

Once the order is validated or confirmed as a new entry, the agent creates or updates the order in the ERP system.

Key Tasks:

• Order Classification: The LLM determines whether the order is a new order or a sub-order, depending on the match status from Step 2.
• Mapping & Integration: The agent maps the extracted fields to the corresponding fields in the ERP system, ensuring that the order is processed with consistency and accuracy.
• Order Finalization: The agent completes the order creation process in the ERP system, generating a final response summary that provides detailed information about the newly created or updated order.

Outcome:

• Order Confirmation: The order is successfully processed and finalized, either as a new order or a suborder and the system is notified with a confirmation of the successful order creation.

Step 4: Continuous Improvement Through Human Feedback

After the order is processed, the agent integrates human feedback to refine its decision-making abilities and adapt to new order criteria, ensuring ongoing improvement in the automation process.

Key Tasks:

• Feedback Processing: Users provide feedback on the accuracy and efficiency of the order processing. The agent analyzes this feedback to identify areas for improvement.
• Error Correction: The feedback highlights any issues with the extracted data or the decisioning process, which the agent then uses to adjust its rules and algorithms.
• Knowledge Base Update: The agent updates its internal knowledge base to reflect new insights, ensuring it can handle similar orders with increased accuracy moving forward.

Outcome:

• Continuous Improvement: The agent evolves with each processed order, becoming more accurate and efficient over time. This adaptive learning ensures that the agent can better handle complex scenarios and improve its decision-making capabilities.

Why use the Order Entry Management Agent?

• Automated Processing: Automates the entire order management workflow, reducing manual intervention and errors.
• Multimodal and OCR Analysis: Uses ZBrain’s capabilities to read and interpret documents in various formats.
• Human Feedback Loop: Incorporates human feedback to continually refine the agent’s performance, ensuring high accuracy and adaptability.
• ERP Integration: Direct integration allows for real-time updates and centralized tracking.
• Scalable: Handles large volumes of emails and orders, supporting business growth.
• LLM-Powered Accuracy: Ensures precise extraction and matching of data fields for order creation, with built-in steps to validate and deduplicate data.