AI Infrastructure Engineer at a consultancy with 11-50 employees
Real User
Top 5
Jun 25, 2026
My main use case for Nanonets is an OCR data extraction workflow. I use Nanonets for OCR detection, which helps me extract OCR data from GST bills and medical claim bills, as well as extracting tables from them.
Lead Engineer at a tech vendor with 10,001+ employees
Real User
Top 10
Jun 12, 2026
I use Nanonets for OCR purposes, specifically for invoice conciliation. For invoice conciliation, I used Nanonets to fetch fields from invoices and match them with third-party database data. If the data matched, it passed, and if it did not match, I sent it for user review. In my use case, since there were third-party comparisons involved, I integrated Nanonets with the database.
Senior Rga Developer at a tech vendor with 10,001+ employees
MSP
Top 20
Jun 4, 2026
Nanonets is primarily used for table extraction from native and scanned PDFs. I have implemented Nanonets for sales order automation, as we work with different manufacturing units across various countries worldwide, and the sales orders received by each unit or factory are in different formats. The PDFs contain complex multiple tables within them, and we need to extract the sales order number, items listed in the sales order, the date, item quantities, item names, and prices. These details must be extracted from sales orders that contain multiple tables on the same page, which we need to identify and extract accurately. Nanonets has helped us capture these details into a structured format that we can integrate into our SAP ERP system to update that data. We have another use case involving the extraction of another table as well, but it is not as complex; it is a simple table where we are utilizing Nanonets.
Intelligent Document Processing (IDP) transforms unstructured data from documents into structured formats, streamlining data extraction processes with advanced technologies.This technology combines AI, machine learning, and natural language processing to automate document handling tasks previously done manually. It can handle large volumes of data quickly and accurately, significantly reducing human error while improving operational efficiency.What are the critical features of Intelligent...
My main use case for Nanonets is an OCR data extraction workflow. I use Nanonets for OCR detection, which helps me extract OCR data from GST bills and medical claim bills, as well as extracting tables from them.
I use Nanonets for OCR purposes, specifically for invoice conciliation. For invoice conciliation, I used Nanonets to fetch fields from invoices and match them with third-party database data. If the data matched, it passed, and if it did not match, I sent it for user review. In my use case, since there were third-party comparisons involved, I integrated Nanonets with the database.
Nanonets is primarily used for table extraction from native and scanned PDFs. I have implemented Nanonets for sales order automation, as we work with different manufacturing units across various countries worldwide, and the sales orders received by each unit or factory are in different formats. The PDFs contain complex multiple tables within them, and we need to extract the sales order number, items listed in the sales order, the date, item quantities, item names, and prices. These details must be extracted from sales orders that contain multiple tables on the same page, which we need to identify and extract accurately. Nanonets has helped us capture these details into a structured format that we can integrate into our SAP ERP system to update that data. We have another use case involving the extraction of another table as well, but it is not as complex; it is a simple table where we are utilizing Nanonets.