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

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

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.

[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.

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

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.

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.

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

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"

Predictive maintenance and quality control system for manufacturing. Uses sensor data and computer vision to predict equipment failures, optimize production lines, and detect product defects in real-time.

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

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.