Purchase Order

Challenges in Extracting Data from Purchase Orders:

• Data Variability: Purchase orders can vary in format and structure, depending on the organization and industry. Extracting data accurately from diverse PO formats can be challenging.
• Data Inconsistencies: Incomplete or inconsistent data within purchase orders can complicate data extraction. This may include variations in product descriptions, unit of measure, or pricing formats.
• Data Accuracy: Ensuring that data extracted from purchase orders matches the information on the invoice is crucial for accuracy in invoice processing. Any discrepancies can lead to payment errors.
• Handling Attachments: Purchase orders often include attachments, such as product specifications or terms and conditions. Extracting relevant data from these attachments can be time-consuming.

How Infrrd for Invoice Can Solve These Challenges:

Infrrd for Invoice is a valuable tool in simplifying the extraction of data from purchase orders and addressing these challenges effectively:

• Data Variability: Infrrd for Invoice uses machine learning algorithms and Natural Language Processing (NLP) to adapt to varying formats and layouts of purchase orders. It recognizes key data fields, such as product descriptions and quantities, and extracts information accurately, regardless of document variability.
• Data Inconsistencies: Infrrd for Invoice can validate data against predefined templates or rules to identify inconsistencies or missing information. It can also learn from previous purchase orders to improve accuracy over time.
• Data Accuracy: Infrrd for Invoice significantly reduces the risk of human errors associated with manual data entry by extracting data with a high degree of accuracy. This ensures that the information extracted from the purchase order matches the invoice.
• Handling Attachments: Infrrd for Invoice can be configured to process attachments, extract relevant data, and associate it with the corresponding purchase order, simplifying the handling of additional documents.