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Automate unstructured data extraction with LLM-native Intelligent Document Processing.

Docyard is a high-performance Intelligent Document Processing (IDP) platform engineered for the 2026 automation landscape. It distinguishes itself by utilizing a multi-model orchestration layer that combines traditional LayoutLM architectures with state-of-the-art Large Language Models (LLMs) like GPT-4o and Claude 3.5 Sonnet. This hybrid approach allows for the extraction of highly nested data from semi-structured and unstructured documents that traditional OCR tools fail to process, such as complex medical records, hand-written legal notes, and multi-page commercial contracts. The technical architecture is built on a microservices framework, enabling horizontal scaling for enterprise-grade throughput. It features a robust 'Human-in-the-Loop' (HITL) interface for validation, ensuring 99.9% data accuracy for high-stakes financial and healthcare workflows. As of 2026, Docyard has introduced native support for multimodal inputs, allowing it to process video-based document scanning and real-time mobile captures with sub-second latency, positioning it as a leader in the autonomous back-office revolution.
Docyard is a high-performance Intelligent Document Processing (IDP) platform engineered for the 2026 automation landscape.
Explore all tools that specialize in automate document workflows. This domain focus ensures Docyard delivers optimized results for this specific requirement.
Explore all tools that specialize in kyc document verification. This domain focus ensures Docyard delivers optimized results for this specific requirement.
Uses semantic understanding to extract fields without needing pre-labeled training data.
Combines visual coordinates with text tokens to maintain structural integrity of complex tables.
Processes low-resolution images and mobile photos with edge-enhancement AI.
Assigns a 0-1 probability score to every extracted field based on cross-model consensus.
Automatically identifies and masks sensitive data during the extraction phase.
Compares two documents to find meaningful differences in clauses rather than just text changes.
Dynamically routes documents to smaller, cheaper models or larger LLMs based on complexity.
Register for a Docyard account and verify organizational domain.
Access the 'Workspace' dashboard and create a new Extraction Project.
Upload a training set of 5-10 sample documents for schema identification.
Use the 'Auto-Labeler' to define key-value pairs and tables to extract.
Configure the LLM orchestration layer (select between Speed or Accuracy models).
Set up validation rules (RegEx or logical checks) for extracted fields.
Generate a unique API key from the developer settings menu.
Test the extraction pipeline via the built-in Postman-style sandbox.
Integrate the Webhook URL to receive structured JSON payloads in real-time.
Deploy the Human-in-the-Loop (HITL) queue for low-confidence scores.
All Set
Ready to go
Verified feedback from other users.
"Users praise the platform's ability to handle highly complex tables and its 'zero-training' extraction features, though some find the enterprise pricing steep."
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