Streamlines order fulfillment by automatically extracting purchase order details, verifying accuracy, creating records in your ERP, and alerting relevant departments.
This workflow is designed for sales operations and finance teams who want to eliminate manual data entry and accelerate the order fulfillment process after a sale is closed. By automating the extraction and processing of purchase orders, teams can reduce errors and significantly speed up order processing.
When a customer submits a purchase order document, the system first uses OCR to digitize the document, then leverages an LLM to intelligently extract critical information like product details, quantities, pricing, and delivery requirements—regardless of varying PO formats from different customers. This AI-powered approach handles the variability and complexity of real-world purchase orders much more effectively than rule-based parsing. The extracted data is then validated against the CRM opportunity to ensure accuracy before being automatically entered into the ERP or billing system, triggering the fulfillment process.
By implementing this automation, organizations typically reduce order processing time by 70-80% while minimizing human error and freeing staff to focus on exception handling and customer service. The LLM's ability to understand context and handle inconsistent document formats dramatically reduces the need for manual review, while automated notifications ensure all relevant departments are immediately aware of new orders, creating a seamless handoff between sales, operations, and finance teams.
You care about improving the entire lifecycle of buyer and customer journeys. And you want inspiration for areas where Ai may help.
You want inspiration for real-world ways to help people as you're building your Ai product.
You're focused on the Decision stage in your daily job and want to find ways for Ai to help you do more with less.
Does this AI agent workflow rely too much on AI and not enough on human know-how? Or the reverse? Is it missing steps or tools?
Note that this Ai workflow is presented as inspiration for what's possible. Adjust the amount, type and quality of the data inputs. Adjust how much or how little your human team mates (or you), AI and fully autonomous agents contribute.
And test it! Learn what works and what doesn't.
Don't forget! In the end, it's not just about efficiency. It's about delivering great experiences for your customers and customers-to-be.
Extract the following information from this purchase order document:
1. PO Number
2. Order Date
3. Company Name
4. Billing Address
5. Shipping Address
6. Contact Person and Information
7. For each line item: Product/Service description, Quantity, Unit Price, Total Price
8. Subtotal
9. Tax (if any)
10. Total Order Amount
11. Payment Terms
12. Requested Delivery Date
13. Special Instructions
The OCR-processed text of the purchase order is as follows:
{poDocumentText}
Return the information in a structured JSON format with fields corresponding to each of the requested items. For missing information, use null values. If you're uncertain about any extracted information, include a confidence score (0-100) for that field.
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