Build and Run an Intelligent Document Processing (IDP) System in the Cloud

Build and Run Your Own AI Agent in the Cloud

Build and deploy an agent on AWS with Strands and AgentCore The post Build and Run Your Own AI Agent in the Cloud appeared first on Towards Data Science .

AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers

arXiv:2601. 10161v3 Announce Type: replace-cross Abstract: Named Entity Recognition (NER) and Personally Identifiable Information (PII) anonymization are critical tasks in Natural Language Processing (NLP) for information extraction and privacy preservation.

Introducing OpenAI Privacy Filter

OpenAI Privacy Filter is an open-weight model for detecting and redacting personally identifiable information (PII) in text with state-of-the-art accuracy

Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload

Enterprise Document Intelligence [Vol. 1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine.

One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries

The article discusses a case study in enterprise document intelligence where a single document type contains a million files. It outlines a workflow that takes about an hour with two people to extract six to ten structured fields, emphasizing the importance of identifying two key signals that distinguish a valid column from one that could break a filter later. The focus is on converting unstructured documents into a structured SQL table for Retrieval-Augmented Generation (RAG) queries.

ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction

arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.

Accelerating Document AI

IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations

arXiv:2607. 26075v1 Announce Type: cross Abstract: We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines.

AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research

arXiv:2512. 16455v4 Announce Type: replace-cross Abstract: The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique requirements of modern and Open Science, particularly regarding the FAIR (Findable, Accessible, Interoperable, and Reusable) principles.

Introducing Mistral OCR 4

Mistral OCR 4 delivers enterprise document AI with 170-language support, bounding boxes, and self-hosted deployment.

Fast And Accurate Text Content File Type Identification

The paper introduces a neural network model that identifies text content file types, especially source code, with higher accuracy and speed than existing tools. Experiments on open-source files show the model is more accurate on average, runs about four times faster than Magika, and is 28% smaller in size.

A Multi-Agent AI System for Automated High School Transcript Processing: Collaborative Document Analysis at Scale

arXiv:2606. 13916v1 Announce Type: new Abstract: Each year, college admissions offices face an overwhelming challenge: processing millions of high school transcripts, each with unique formats, grading systems, and layouts.