Nanonets

What is Nanonets?

Nanonets is an API-driven, AI-powered platform engineered for intelligent document processing (IDP). From a technical standpoint, it provides a scalable infrastructure to extract structured data from unstructured or semi-structured documents like invoices, purchase orders, and receipts. Rather than relying on rigid templates, Nanonets leverages machine learning models to understand document layouts and context, making it a resilient solution for automating data-centric workflows. It functions as a critical middleware component, transforming variable inputs into predictable, machine-readable JSON outputs that can be consumed by other business systems. For development teams, it offers a way to offload the complex, resource-intensive task of building and maintaining an in-house document parsing engine.

Key Features and How It Works

The architecture of Nanonets is built around a logical flow of ingestion, processing, and exportation, all accessible via a comprehensive API.

• Flexible Data Ingestion: The platform supports multiple ingestion channels, including a REST API for direct uploads, monitored email inboxes, and connections to cloud storage providers like Google Drive, Dropbox, and SharePoint. This flexibility allows developers to easily integrate Nanonets into existing data pipelines without significant re-architecting.
• Advanced AI Engine: At its core, Nanonets utilizes a sophisticated AI that combines optical character recognition (OCR) with deep learning models for entity recognition. It comes with pre-trained models for common document types, but its key differentiator is the ability to train custom models. Developers can programmatically upload sample documents and label the required fields, enabling the system to learn and adapt to unique or proprietary document formats with high accuracy.
• Programmable Workflows: Nanonets isn’t just an extraction tool; it’s a workflow automation engine. Developers can define post-processing rules, validation logic, and approval steps. This can be configured to, for example, flag invoices where the total amount doesn’t match the sum of line items or route documents to different endpoints based on extracted data, minimizing the need for custom backend validation code.
• API-Centric Export and Integration: Once data is extracted and validated, it can be seamlessly pushed to other systems. Nanonets provides webhook support to notify downstream services in real-time. It also features direct integrations with various CRMs, ERPs, and databases, but the primary method for custom solutions is retrieving structured data (typically JSON) via its API for use in any application.

Pros and Cons

Pros

• API-First Design: The platform is built with developers in mind, offering a well-documented and extensive API that allows for deep integration and control over the entire document processing lifecycle.
• High Accuracy and Customization: The ability to train custom models on specific datasets allows for accuracy rates exceeding 95%, even on complex and non-standard documents where template-based systems would fail.
• Scalable Infrastructure: As a cloud-native platform, Nanonets is architected to handle high-volume, asynchronous processing, making it suitable for enterprise-grade applications without concerns about managing server capacity.
• Reduced Development Overhead: It abstracts away the complexity of OCR, machine learning, and data parsing, allowing engineering teams to focus on core application logic rather than building an IDP solution from scratch.

Cons

• Integration Complexity: While powerful, fully leveraging the API and webhooks for a custom, end-to-end automated workflow requires dedicated developer resources and can have a notable implementation timeline.
• Cloud-Only Architecture: The lack of an on-premise deployment option can be a significant constraint for organizations with stringent data residency requirements or policies against third-party cloud data processing.
• Potential for High Cost at Scale: The pricing model, based on document volume and features, can become a considerable operational expense for businesses processing millions of documents, requiring a clear cost-benefit analysis.

Who Should Consider Nanonets?

Nanonets is an ideal solution for technical teams and enterprises looking to build robust, automated document processing into their software stack. It is particularly well-suited for:

• Software Development Teams: Engineers building applications in fintech, logistics, or healthcare that require reliable data extraction from user-uploaded documents like ID cards, loan applications, or bills of lading.
• Enterprise IT Departments: Teams tasked with automating internal processes such as accounts payable, employee expense reporting, or supply chain management by integrating a powerful IDP component into their existing ERP and financial systems.
• Systems Integrators: Professionals who design and implement custom automation solutions for clients and need a reliable, scalable, and API-driven platform for document data extraction.
• Product Managers: Individuals looking to add advanced document processing features to their software products without investing in building an in-house AI/ML team.

Pricing and Plans

Nanonets operates on a paid subscription model with plans designed to scale with usage. While a free trial is available to test the platform’s capabilities, ongoing use requires a paid plan.

• Pricing Model: Paid
• Starting Price: $499 per month
• Available Plans: Nanonets offers a Starter plan beginning at approximately $499 per month, which is suitable for small to medium-sized businesses with moderate document processing needs. For larger organizations with high-volume requirements, custom workflows, and advanced security needs, an Enterprise plan is available with pricing tailored to specific use cases. Full details are provided upon booking a demo.

What makes Nanonets great?

Struggling to build reliable data extraction pipelines that don’t break every time a document format changes? This is the core problem Nanonets solves with its architectural approach. Its greatness lies in its departure from brittle, template-based OCR. Instead, it employs a machine learning model that understands documents contextually, recognizing fields like ‘Invoice Number’ or ‘Total Amount’ regardless of their position on the page. This AI-driven resilience makes it a far more robust and scalable solution for real-world scenarios where document layouts are inconsistent. Furthermore, the platform incorporates a crucial feedback loop; through its UI or API, users can correct extractions, and this data is used to retrain and improve the model over time. This continuous learning capability ensures the system becomes more accurate and efficient with use, a hallmark of a well-designed AI product.

Frequently Asked Questions

How does Nanonets handle custom or unique document types?

Nanonets allows users to train custom AI models. Through the API or the user interface, you can upload a set of your specific documents and label the data fields you need to extract. The platform uses this training data to create a model tailored to your document structure.

What is the API rate limit and how does it scale?

The API is designed for high-throughput applications. Specific rate limits vary by subscription plan, with Enterprise plans offering the highest limits and dedicated processing queues. The cloud infrastructure is built to scale, ensuring consistent performance even during high-volume periods.

Can Nanonets be deployed on-premise?

No, Nanonets is a cloud-based SaaS platform. It does not offer an on-premise deployment option. To address security concerns, the company maintains compliance with standards like SOC 2, HIPAA, and GDPR, ensuring data is handled securely within its cloud environment.

How does Nanonets differ from standard OCR libraries?

Standard OCR (Optical Character Recognition) libraries convert images of text into machine-readable text strings. Nanonets is an Intelligent Document Processing (IDP) platform that goes further. After performing OCR, its AI models apply contextual understanding to identify and extract specific data points (e.g., names, dates, line items) and return them as structured data like JSON, ready for programmatic use.