Nanonets

Nanonets is an AI platform that turns unstructured documents into structured, actionable data in minutes. The homepage positions it as “Intelligent Document Processing built for business users,” emphasizing zero-code workflow creation. Users upload sample files— invoices, receipts, passports, forms, emails or any PDF/image— and the auto-training engine instantly learns the layout, extracts the desired fields, and exports them to Excel, QuickBooks, Salesforce, SAP or custom APIs. No pre-built templates or lengthy annotation are required; the few-shot learning core adapts to new formats on the fly.

Key features highlighted on the site include: (1) Pre-trained models for common documents such as invoices, receipts and ID cards that work out-of-the-box. (2) A drag-and-drop Flow Builder where non-technical staff can add approval steps, conditional logic and human-in-the-loop review without writing code. (3) 95 %+ extraction accuracy powered by continual learning: every user correction is fed back to retrain the private model, so accuracy improves automatically. (4) Enterprise-grade security— SOC-2, GDPR, HIPAA compliant, hosted on AWS with VPC isolation and single-tenant options. (5) Infinite scalability through cloud workers that process millions of pages per day while keeping sub-second response times. (6) Ready connectors to 5 000 + apps via Zapier, REST & webhooks, plus on-premise deployment for air-gapped environments.

The vendor stresses measurable ROI: customers report 90 % cost reduction, 10 × faster turnaround and 80 % decrease in manual errors within the first quarter. A free starter tier allows 100 pages per month; paid plans add team collaboration, advanced analytics and unlimited models. The site displays case studies from Xerox, Toyota, Canon and other Fortune 500 companies that replaced manual data entry in finance, logistics and healthcare departments. Overall, Nanonets markets itself as the fastest path to autonomous document understanding for operations teams that need accuracy, security and speed without hiring data scientists or ML engineers.