PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation

OpenSSA: Small Specialist Agents based on Domain-Aware Neurosymbolic Agent (DANA) architecture for industrial problem-solving

roadmap to applied ai

AI SDK Tutorials helps you to get familiar with AI Software Development Kit (AI SDK) through a set of end-to-end tutorials, how-to jupyter notebooks, and how-to guides.

Ontology, and Knowledge graph based RAG that uses local LLM.

[CVPRW'25] Official Code For "SK-RD4AD: Skip-Connected Reverse Distillation for One-Class Anomaly Detection"

Industrial AI Agents using LLMs

Async-first, production-grade AI agent framework with workflow, RAG, observability & multi-agent teams（Python 智能体框架）

Build an enterprise-level AI agent operating system enabling cross-departmental and cross-system intelligent collaboration.

Real-time Industrial Anomaly Defect Inference Detection implemented by cpp（实时工业缺陷检测cpp）

This article presents a reference architecture to enhance the compatibility of Siemens Industrial Artificial Intelligence (Industrial AI) products with Microsoft Azure.

The Compositional Agentic Architecture (CAA): A blueprint for building reliable, deterministic, and safe industrial AI agents.

Zero and few-shot industrial image anomaly detection framework comparing AnomalyDINO & MuSc models across MVTec AD, BTAD, and ViSA datasets with MLflow tracking and flexible configuration.

A visual editor to convert Chemical P&IDs into Neo4j Knowledge Graphs for Industrial AI/RAG. (Build by Expert + AI)

Automatically identify whether the sounds produced by industrial machines are normal or anomalous (faulty machines). This is crucial for ensuring efficient and safe operations in the context of AI-based factory automation.

This repository provides code for the paper "Vipul Bansal, Yong Chen, Shiyu Zhou, Component-Wise Markov Decision Process for Solving Condition Based Maintenance of Large Multi-Component Systems with Economic Dependence"

Advanced Condition Monitoring and Remaining Useful Life Prediction Framework using Deep Learning for Industrial Equipment Prognosis and Predictive Maintenance

The prototype, CemGenie, is a Generative AI–powered platform designed to autonomously optimize cement plant operations. It integrates real-time process data from raw material handling, grinding, clinkerization, and utilities into a unified AI control layer.

An end-to-end deep learning system for automated PCB defect detection that combines computer vision with domain expertise. This project demonstrates the practical application of AI in industrial quality control, achieving 91.2% F1-score on multi-label defect classification.

Build an enterprise-level AI agent operating system enabling cross-departmental and cross-system intelligent collaboration.