Nanonets

Nanonets is a document AI platform that combines optical character recognition with machine learning to extract structured fields from a wide variety of document types, including invoices, bank statements, receipts, passports, contracts, and custom business formats. Users train extraction models by uploading sample documents through a visual interface, labeling the fields they want to capture, and allowing the system to generalize from those examples. Trained models then process incoming documents automatically, returning structured data in exportable formats or passing results through API and Zapier integrations to connected tools and systems. The platform supports documents in more than 100 languages. Nanonets is used by accounts payable teams automating invoice processing, HR departments extracting fields from onboarding and compliance documents, procurement teams parsing supplier contracts, and compliance workflows where identity document data needs to be captured systematically. Integration with accounting platforms, ERP systems, and approval tools allows extracted data to move downstream without manual re-entry. Find related tools. Teams processing high volumes of recurring document types benefit most, where the upfront model training effort pays off across repeated processing runs at scale. Nanonets uses a usage-based pricing model with no fixed platform fees. New accounts receive $200 in processing credits at signup, which can be applied across any document workflow. Costs accrue per block execution within a workflow, so teams pay only for processing they actually use rather than a fixed monthly seat. Volume discounts are available for higher usage levels, and further savings apply to annual usage commitments, both of which can be arranged through the sales team. A key limitation is that training reliable custom models for unusual document layouts or predominantly handwritten content requires a larger and more representative set of sample documents, which may take additional time to assemble Try this alternative.