:zap: Cloud-native, AI-powered, document processing pipelines on AWS.

Complex data extraction and orchestration framework designed for processing unstructured documents. It integrates AI-powered document pipelines (GenAI, LLM, VLLM) into your applications, supporting various tasks such as document cleanup, optical character recognition (OCR), classification, splitting, named entity recognition, and form processing

Python-based Durable Functions accelerator for building intelligent document processing pipelines with Azure AI Services on Azure Container Apps

AI-powered invoice processing system using Google Document AI - Automated AP workflows with CI/CD pipeline for enterprise finance operations

Intelligent Document Processing pipeline powered by Azure Durable Functions — 6-step orchestration with dual-model extraction, human-in-the-loop review, and multi-provider LLM support (Azure OpenAI, Claude, Azure AI Models).

This sample will show you how to use a Fabric Pipeline template to process your document, video, image or audio content with Azure AI Content Understanding and save your results in a Fabric LakeHouse

An intelligent system combining a sophisticated data pipeline with an AI-powered chatbot. The system features document processing capabilities, multi-modal AI integration, and a scalable architecture.

MCP server for intelligent document ingestion using Docling. Convert PDFs, DOCX, images, audio & more to clean Markdown for AI/RAG pipelines. Mac M2 optimized with MLX acceleration, VLM processing & queue management.

Python-based Durable Functions accelerator for building intelligent document processing pipelines with Azure AI Services on Azure Container Apps

A library for parsing, formatting, and processing documents that can be used to build AI-powered document processing pipelines.

A fully automated PRD (Product Requirement Document) processing pipeline using AI/ML and NLP for task assignment, workload balancing, and epic generation.

This project is a serverless MLOps pipeline for AI-driven document processing, automating text extraction, refinement, and analysis. It leverages AWS Textract, SageMaker, and OpenSearch, along with AWS CDK for infra provisioning.

This Document Analysis pipeline is a comprehensive document analysis system, designed to automate the processing and analysis of documents from acquisition to consumption. It integrates advanced machine learning & AI models like RAG (Retrieval Augmented Generation) & Mistral LLM to efficiently extract, match, enrich, process document

The data pipeline to process pdf and other documents to extract and clean the text database, for the Energy Moonshot AI

This project is an Enterprise Multilingual AI Document Intelligence Platform designed for comprehensive document analysis and fraud verification. Its processing pipeline combines various ML models, including EasyOCR for text extraction, LayoutLMv3 for field extraction, ViT for document classification, and Claude LLM for intelligent text correction.

Developed an intelligent AI chatbot utilizing the DeepSeek LLM, designed for efficient interaction with large documents such as textbooks and study materials. Integrated Docling for parsing and processing large files, and implemented a Retrieval-Augmented Generation (RAG) pipeline using FAISS and Sentence Transformers to optimize context retrieval

An intelligent, AI-powered pipeline for extracting structured data from complex documents. Using multimodal LLM for content-aware routing, sself-corrects errors, and processes pages in parallel with async design. Handles text, tables and charts with high accuracy

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AI-powered document processing pipeline with Claude, FastAPI, and AWS

AI document processing toolkit: PDF → Markdown → RAG pipeline with Ollama/OpenAI support

AI-powered smart api for document processing with RAG pipeline for insurance decisions

AI receipt analysis pipeline for Google Drive, OCR, routing, and tax-ready document processing.

An AI-powered document processing pipeline that automates the extraction, analysis, and storage of unstructured data from documents.

A serverless, event-driven document processing pipeline designed to help small businesses eliminate manual data entry using AI.

This project consists of two interconnected n8n workflows that create a complete RAG (Retrieval-Augmented Generation) pipeline for document analysis and intelligent querying: Document Ingestion Pipeline - Automated document processing and vector storage AI Chat Interface - Interactive conversational AI with document retrieval capabilities

AI-powered legal analysis system for UK family court judgments. Features XML processing, ML training pipelines, and explainable AI for legal document analysis.

A RAG pipeline for processing health content from MedlinePlus. Downloads, converts HTML to Markdown, and chunks documents for AI applications.

BlockData decomposes documents into blocks, applies user-defined schemas with parallel AI processing, and exports structured outputs for downstream pipelines.

CAR-T Pathology Intelligence: Snowflake Document AI pipeline for processing pathology reports and determining CD19-targeted CAR-T cell therapy eligibility

This configuration implements a fully automated, end-to-end document processing pipeline for Paperless-NGX, using AI to improve OCR recognition.