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Artificial intelligence (AI) is revolutionizing numerous industries, and one of the sectors enjoying immense benefits from its adoption is document management. Document sorting, a process once solely relegated to the realm of human labor, has been remarkably transformed by AI. This transformation has significantly boosted efficiency, accuracy, and scalability, allowing businesses to handle large volumes of data in a shorter time, while reducing manual errors.

The process of sorting documents is not simply about categorizing files. It involves analyzing, understanding, and recognizing the content of each document to ensure appropriate classification. Traditional methods of document sorting can be time-consuming, prone to errors, and lack the dynamism needed to adapt to changing information structures. This is where AI comes into play, providing automated, reliable, and responsive solutions for document sorting.

AI-based document sorting employs machine learning, natural language processing (NLP), and optical character recognition (OCR) to intelligently classify documents. Machine learning algorithms help the system to learn from data patterns and make accurate predictions, NLP enables the system to comprehend the context and semantics of the document content, while OCR facilitates the conversion of different types of documents into machine-readable text. Together, these technologies empower AI systems to sort documents efficiently, providing businesses with a reliable and highly scalable solution.

Whether it's sorting emails in an inbox, classifying patient records in a hospital, or organizing legal documents in a law firm, AI-based document sorting is streamlining processes and making document management significantly more efficient. The future of document sorting lies in the integration of AI, and this blog aims to explore that future, examining how AI can transform document sorting, the underlying technologies, its benefits, and its potential for future growth.

Examples of Document Sorting Workflows

Invoice Processing in Finance Department:
The finance department of a large corporation often receives hundreds of invoices daily in PDF format. Using Nanonets' document sorting solution, these invoices can be automatically sorted based on parameters like vendor name, date, and amount. The AI extracts data from the PDFs, classifies them appropriately, and routes them to the right department or person for processing. This not only improves efficiency and accuracy but also speeds up the payment process.

Insurance Claim Processing:
Insurance companies receive a large volume of claims in different formats like accident reports, medical bills, repair invoices, etc. Using Nanonets, these documents can be sorted according to claim ID, type of claim, or claimant details, streamlining the claims process. This results in faster, more accurate claims processing and better customer service.

Healthcare Patient Records Management:
Hospitals deal with a multitude of patient records daily, including lab reports, prescriptions, diagnostic images, etc. Nanonets can automatically categorize these documents based on patient ID, type of report, date, etc. This sorted data can then be stored digitally in a patient's health record, ensuring easy access for doctors and improving the quality of patient care.

Legal Document Management:
Law firms handle numerous documents, including case briefs, contracts, and legal notices. With Nanonets, these documents can be sorted based on case number, client ID, or type of legal document, allowing lawyers to access the required documents promptly and improving the overall productivity of the firm.

HR Document Management:
HR departments handle documents like resumes, employment contracts, performance reviews, etc. With Nanonets, these documents can be automatically sorted according to employee ID, type of document, or date, making HR processes more efficient and freeing up staff to focus on more strategic tasks.

Academic Document Sorting in Universities:
Universities deal with a variety of documents like admission forms, examination papers, and student records. Nanonets can sort these documents based on student ID, department, or type of document, making it easier for university staff to manage records and provide more effective services to students.

How to Sort Documents using Nanonets?

You can create your document sorting workflow using Nanonets within minutes by following the below steps -

  • Choose a pretrained model based on your document type / create your own document extractor within minutes.
  • Verify the data extracted by Nanonets. Your data extraction model is ready now.
  • Once you have created your model, go to the workflow section of your model.
  • Go to the export tab and select "Export files to Google Drive".
  • Connect your Google Drive account.
  • You can now specify the folder based on the data extracted by Nanonets. For example, I have used the invoice model in this workflow. I am going to sort invoices by the seller_name field automatically extracted by your Nanonets model.
  • You can also rename the sorted PDF files by using the extracted data. Specify a renaming format for your files based on the data extracted by Nanonets. I have specified a format here to rename files based on invoice date, seller name, and invoice amount as follows - {invoice_date}_{seller_name}_{invoice_amount}.pdf
  • Choose your export trigger and test using a file.
  • Click on "Add Integration" and you are good to go.

Nanonets will now automatically extract data from incoming files, sort them using predefined conditions, rename them based on the specified naming convention using the extracted data, and then send the renamed PDFs to the correct Google Drive folder based on your sorting rule!

Nanonets for Intelligent Document Sorting

As we embrace the future, the immense potential of artificial intelligence in transforming our everyday tasks becomes more evident. In the realm of document management, Nanonets' intelligent document sorting offers a new frontier in efficiency, scalability, and accuracy. Its ability to automatically extract data from PDFs and categorize documents based on this data is a boon for businesses across various sectors.

In essence, the Nanonets AI-based document sorting solution is more than just a convenience—it's a strategic enabler. From streamlining invoice processing in finance departments and managing patient records in healthcare institutions to facilitating efficient legal document management and simplifying academic document sorting in universities, Nanonets' AI-driven solution proves invaluable.

Furthermore, it improves accuracy, as the machine-learning models employed are trained to learn and adapt continuously, minimizing the risk of human error. This heightened accuracy in document categorization, coupled with improved efficiency, inevitably leads to a significant boost in productivity. Businesses can also scale their operations seamlessly, as the Nanonets solution can handle high volumes of documents with ease.

The integration of AI into document management also provides the added benefit of saving valuable time, which employees can redirect towards strategic, value-add tasks. This, in turn, cultivates a more innovative, productive work environment.

As businesses seek to optimize their operations and thrive in the digital age, adopting advanced tools like Nanonets for document sorting is no longer a luxury—it's a necessity. AI is reshaping how we handle and interpret information, and Nanonets stands at the forefront of this transformation. As we move forward, the question for businesses is no longer whether they should embrace AI in document sorting but how quickly they can adopt it to stay competitive. With Nanonets, the future of document sorting is here, and it's intelligent.