Nanonets
An AI-powered intelligent document processing (IDP) platform that automates data extraction from unstructured documents like invoices, receipts, and forms.
Price: Premium
Description
Nanonets uses AI and machine learning to automatically extract structured data from various unstructured and semi-structured documents. It's designed to eliminate manual data entry, reduce errors, and accelerate business processes for finance, operations, and logistics teams. Unlike traditional OCR, Nanonets' AI models learn from document layouts and content, allowing it to accurately extract information even from varied or complex document types without needing fixed templates. This makes it ideal for businesses processing large volumes of invoices, purchase orders, identity documents, and other critical paperwork, significantly improving efficiency, reducing costs, and enhancing compliance.
How to Use
1.Sign up for a Nanonets account and choose a pre-trained model or create a custom one.
2.Upload your document type (e.g., invoices, receipts) to the platform.
3.If creating a custom model, train it by tagging key data fields on a few sample documents.
4.Nanonets' AI learns to identify and extract these fields automatically from similar documents.
5.Upload new documents (individually or in bulk) for automatic data extraction.
6.Review extracted data, make any necessary corrections, and export it to your desired systems via integrations or API.
Use Cases
Automating invoice processingExtracting data from receiptsDigitizing KYC (Know Your Customer) documentsProcessing purchase ordersAutomating loan application reviewManaging freight documentsExtracting data from tax formsAutomating claims processing
Pros & Cons
Pros
- High accuracy in data extraction from complex, unstructured documents.
- Reduces manual data entry and processing time significantly.
- AI models adapt and learn from new document variations over time.
- Strong integration capabilities with existing business systems (ERPs, accounting software).
- Improves compliance and reduces human error in data handling.
Cons
- Initial setup and custom model training can require some effort and sample data.
- Cost can increase significantly with higher document volumes.
- Less effective on extremely poor-quality, heavily distorted, or entirely handwritten documents without specific, extensive training.
Pricing
To check the pricing of Nanonets click here
FAQs
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