Building with Agentic Document Extraction APIs
For developers building document-intensive applications, agentic document extraction APIs provide the intelligence layer that transforms unstructured documents into structured, actionable data. This guide covers the key concepts, integration patterns, and best practices for working with agentic extraction APIs.
API Architecture Overview
Modern agentic extraction APIs follow a consistent architecture. Documents are submitted via HTTP POST to an extraction endpoint — either as file uploads or as URLs pointing to document storage. The API returns either synchronous results for simple documents or a job ID for asynchronous processing of complex or large documents. Webhooks notify your application when async extraction jobs complete.
Core API Endpoints
A typical agentic extraction API provides endpoints for document submission (POST /extract), job status retrieval (GET /jobs/{id}), result retrieval (GET /results/{id}), feedback submission for continuous learning (POST /feedback), and schema definition for custom extraction configurations (POST /schemas).
Handling Extraction Results
API responses return structured JSON with extracted fields, their values, and confidence scores. Implement confidence thresholds — routing high-confidence extractions automatically and flagging low-confidence results for human review. The response also includes bounding box coordinates and source page references for each extracted field.
Getting Started with Papirus AI API
Papirus AI provides a RESTful API with comprehensive documentation, SDK support for Python, Node.js, and Java, and a Postman collection for rapid prototyping. Developers can integrate agentic document extraction into any application with minimal boilerplate code and achieve production-ready extraction accuracy from day one.
