How Automatic Purchase Orders processing improves the experience in a Law Firm

Enhances law firms by automating delivery note processing with AI, saving time, reducing errors, and improving overall efficiency.

How Automatic Purchase Orders processing improves the experience in a Law Firm

In a law firm, document management is a critical task. The extraction of delivery notes is one of the most important processes, since it allows lawyers and their teams to have fast and accurate access to relevant information about the services and products purchased by their clients. However, extracting delivery notes is a manual, tedious, and error-prone process. That is why the extraction of delivery notes by artificial intelligence has become a valuable tool for law firms that need to process large amounts of documents in a short time.

What is data extraction through OCR powered by Artificial Intelligence?

Faced with the loss of time in quite tedious and repetitive manual processes, the intelligence of OCR without templates presents a fast, efficient and error-free solution. With Procys OCR, documents are processed 6 times faster than manual processes. Plus, our self-learning engine gets smarter with every document processed.

Procys allows the extraction of delivery notes by artificial intelligence, reads and understands the data of the delivery notes, such as customer name, date, invoice number, services provided and other important details. The automation of processes allows law firms to process large quantities of delivery notes in a short time.

How does delivery note extraction work in Procys?

1. Extract: In the first step, the image of the packing slip is captured using a camera or scanner. The artificial intelligence system is capable of capturing high-quality images even if the delivery note is damaged or deteriorated.

2. Understand: Once the image has been captured, the artificial intelligence software begins to analyze it. Using advanced optical character recognition (OCR) techniques, the software is capable of interpreting the information on the packing slip, including the customer's name, date, invoice number, services provided, and other important details.

3. Export: Once the information on the packing slip has been interpreted, it can be extracted and processed for further use. The information can be sent directly to the law firm's database or integrated with other business applications and platforms. This allows law firms to more accurately track their records and more efficiently manage their document management processes.

Within the law firm, the management of delivery notes, and manual processes is tedious and consume a lot of costs and time. Likewise, the manual creation, review and processing of delivery notes can delay the daily work of lawyers and their administrative team. However, automating delivery note processes through the use of artificial intelligence can help improve the overall experience in a law firm.

Automating delivery note processes with artificial intelligence involves the use of advanced technology to streamline the creation and review of delivery notes. Thanks to Procys, lawyers can automatically generate delivery notes for their services and send them directly to their clients without having to worry about the tedious task of manual creation. In addition, the software can also review packing slips for errors and make sure all the necessary information is included before sending them to customers.

Automating invoice processes with artificial intelligence can also help lawyers keep accurate records of their time and services. Automating packing slip processes can also save administrative teams time. Instead of spending hours manually reviewing and processing packing slips, they can rely on software to do much of the work. This allows administrative teams to focus on other important tasks instead of spending valuable time on administrative tasks. Go to Procys today and improve the experience in your business.

How Automatic Purchase Orders processing improves the experience in a Law Firm