Nanonets

The platform uses deep learning models trained on specific document typesinvoices, receipts, purchase orders, contracts, insurance claims, customs forms, and moreto extract structured data from the unstructured content that flows through business operations, eliminating the manual data entry that consumes significant human resources in finance, procurement, logistics, and operations departments.

Custom model training is a core differentiator for Nanonets. Unlike off-the-shelf OCR tools that apply generic text recognition to any document, Nanonets allows users to train their own extraction models on their specific document layouts within secondswithout machine learning expertise.

This customization produces significantly higher accuracy than generic models on the particular document types that matter most to each organization, since the model learns the exact fields, formats, and variations present in that company's actual paperwork.

The automation layer extends beyond extraction into complete workflow orchestration.

Once data is extracted from documents, Nanonets applies LLM-powered transformations and validations, performs lookups against external databases, routes documents through approval flows, and pushes structured data into downstream systems via one-click integrations.

This end-to-end automation can reduce manual document processing effort by up to 90%, according to the company, with corresponding reductions in processing time and error rates.

AI Agents represent Nanonets' evolution beyond passive document processing into active intelligent automation.