Autonomous AI workflows to capture and automate data from any unstructured document in seconds.
Nanonets is a market-leading Intelligent Document Processing (IDP) platform that leverages proprietary deep learning models and Large Language Models (LLMs) to transform unstructured data into structured, actionable insights. By 2026, Nanonets has transitioned from a standard OCR provider to an 'Autonomous Workflow' engine, where its AI agents not only extract data but also perform multi-step validation, cross-referencing with external databases, and automated decision-making. The technical architecture is built on a few-shot learning framework, allowing enterprises to train highly accurate custom models with as few as 10 documents. This significantly reduces the time-to-value compared to legacy systems. Its 2026 market positioning focuses on 'Straight-Through Processing' (STP), aiming to eliminate human-in-the-loop requirements for 95% of document types. The platform is robust, API-first, and integrates deeply into ERP and CRM ecosystems, making it a critical component of the modern enterprise's hyper-automation stack. It supports a vast array of document types including invoices, receipts, ID cards, and complex legal contracts, providing high-fidelity JSON outputs optimized for downstream automation.
Proprietary algorithm that allows model convergence with minimal data samples using transfer learning.
AI-powered platform for end-to-end document intelligence and business process orchestration.
Enterprise-grade Intelligent Document Processing (IDP) powered by Generative AI.
Enterprise-grade multimodal document intelligence and semantic extraction engine.
Automate unstructured data extraction with LLM-native Intelligent Document Processing.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses GPT-4o and Claude 3.5 integrations to verify context within extracted data.
Conditional routing logic based on extracted data values (e.g., if total > $5000, send to CFO).
Hybrid approach combining anchor-based zonal extraction and global semantic understanding.
Advanced grid-detection algorithms that reconstruct multi-page tables into structured JSON.
Automated identification and masking of sensitive information (SSN, credit card numbers) before storage.
Support for 200+ languages including RTL languages like Arabic and character-based languages like Chinese.
Manual entry of thousands of invoices leading to 4% error rates and high labor costs.
Registry Updated:2/7/2026
Slow verification of ID documents causing high drop-off rates in fintech apps.
Banks struggling to process hundreds of pages of bank statements and tax returns.