Nanonets
AI-powered intelligent document processing platform that extracts structured data from invoices, receipts, and custom documents using adaptive OCR models.
Nanonets is an AI-powered intelligent document processing platform that trains custom OCR models to extract structured data from virtually any document type - invoices, purchase orders, receipts, ID documents, bank statements, and contracts. Finance, logistics, and operations teams use Nanonets to eliminate manual data entry by building extraction workflows that capture fields, validate outputs, and push data into downstream systems like ERP, accounting software, and databases. The platform is self-learning, improving accuracy over time as users correct extractions and expand the training set. Founded in 2016 as a Y Combinator S17 company, Nanonets has processed hundreds of millions of documents for customers ranging from mid-market businesses to enterprise accounts. Pricing starts at $99 per month with a free tier covering 500 pages monthly.
Key Features
- Custom OCR model training - upload sample documents and train a field extractor specific to your document layout in minutes
- Multi-document type support covering invoices, POs, receipts, ID cards, bank statements, and any custom document with structured fields
- Validation rules that flag low-confidence extractions and route them for human review before data reaches downstream systems
- Workflow builder connecting extraction outputs directly to Google Sheets, QuickBooks, SAP, Salesforce, and 50+ integrations via Zapier
- Approval workflows with review screens where teams can correct extractions and simultaneously improve the underlying AI model
- Batch processing API for uploading and processing thousands of documents programmatically with JSON output for each document
Use Cases
- Finance teams automating accounts payable by extracting invoice line items, vendor names, and amounts directly into accounting software
- Logistics companies digitizing paper bills of lading, packing lists, and customs documents at port without manual transcription
- HR departments extracting candidate data from resumes and application forms into ATS records without recruiter manual entry
- Healthcare back-office teams processing insurance claim forms and referral documents to reduce administrative processing time
Pros
- Self-learning model that improves extraction accuracy with every human correction - unlike static OCR tools that never get smarter
- Free tier with 500 pages per month lets small teams validate ROI before committing to a paid plan
- API-first architecture makes it straightforward for developers to integrate document extraction into existing operational workflows
Cons
- Initial model accuracy on uncommon document layouts requires multiple training rounds and a meaningful volume of annotated samples
- Paid plans at $99/month cover only limited page volumes - high-volume document processing escalates costs quickly toward enterprise pricing
- Validation and correction UI is functional but lacks the polish of purpose-built data review tools for teams doing high-volume document work
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