I’m proud to share the Product of my Agentic Predictive Maintenance Copilot for Industrial Assets. 🚀 What started as an engineering problem evolved into a working AI-powered maintenance system: 🔌 Live Industrial Telemetry — connected machine monitoring 🧠 ML-Based Prediction — machine health & failure risk 📡 Anomaly Detection — identify abnormal conditions 🤖 AI Diagnosis — evidence-based fault analysis 💬 Maintenance Copilot — actionable recommendations 🛠️ Work Orders — turning insights into maintenance action The core idea is simple: Predict before failure. Act before downtime. This project brings together Mechanical Engineering + Industrial AI + Automation into one practical solution. ⚙️🤖 Dr Ramanth Polu Sathyaram Sannasi Lori Figueiredo Jay Harish Jethva Ahmed Raoofuddin Sadiya Kauser Ahmad Burt Reynolds 🙏 A special thank you to the judges and mentors who took the time to review, evaluate and provide valuable feedback throughout the project. Especially grateful to Mohammad Arshad, Decoding Data Science, DDS Business Circle and everyone who contributed to this journey.

Watching Kindred evolve from an idea into something tangible has been an incredible journey of building, learning, feedback, and continuous improvement. I’m truly grateful to Decoding Data Science (DDS), the mentors, @Mohammad Ahmad and this amazing community for the guidance, encouragement, and opportunity to keep pushing ourselves. Wishing every fellow contestant the very best as we head into the final day—regardless of the outcome, everyone who dared to build, learn, and finish this journey is already a winner! #Kindred #DecodingDataScience #AgenticAI #AIInnovation #BuildInPublic #LearningByDoing

Day 7 — ProDiag AI | From Local Prototype to Production Architecture 🚀 Today was less about adding another UI feature—and more about making the system deployable, reliable, and production-oriented. I containerized the ProDiag AI V2 backend with Docker and validated the complete communication architecture: React Frontend → Flask Backend → MQTT/TLS → HiveMQ Cloud → Industrial Simulator The backend now includes the trained ML models, RAG components, knowledge base, and MQTT services inside a deployment-ready container. I also fixed an important initialization issue discovered during container testing: the database schema must be created before services such as the Alert Service attempt to load existing records. This is exactly the kind of issue that doesn't always appear during local development—but becomes critical when moving toward production. Building an AI system is not only about the model. The real challenge is making the entire system work together reliably. Mohammad Arshad Decoding Data Science #AI #IndustrialAI #PredictiveMaintenance #AgenticAI #MachineLearning #IIoT #Docker #MQTT #Engineering #ProDiagAI

I’m building ProofCheck — an AI Evidence Reconciliation Agent. The idea is simple: Don’t just summarize documents. Prove whether they agree. The project combines: • RAG and structure-aware document retrieval • Agentic investigation and tool use • Deterministic validation for calculations and evidence conflicts • Structured evidence and provenance • Safety boundaries and human-in-the-loop review • Evaluation with golden test scenarios One principle has guided the architecture: The LLM decides what to investigate. Deterministic systems establish what the numbers actually say. Today I completed the first working UI connected to the real backend, including investigation progress, error handling, and reset/clear behavior. There is still work to do — especially around reliability and edge cases — but that is exactly what makes this challenge valuable: building, testing, finding weaknesses, and improving the system one step at a time. On to the next checkpoint. 🚀 #AI #ArtificialIntelligence #AgenticAI #RAG #LLM #Python #AIEngineering #BuildInPublic #DecodingDataScience #AIChallenge

Today was about taking ProDiag AI V2 from a working prototype toward a more complete maintenance product. The focus was on strengthening the Agentic Maintenance workflow and making sure the different components work together correctly. Today I worked on: ⚙️ Maintenance Agent → Maintenance Plan 🔧 AI-recommended Spare Parts integration 💰 Parts, labour & total maintenance cost calculation 👨‍🔧 Engineer-in-the-loop approval workflow 📋 Approved proposal → Work Order generation 🔗 Linking Work Orders with alerts and fault events 🐛 Debugging and fixing data-flow issues across the workflow One important improvement was making sure the recommended spare parts and their actual costs flow correctly through the maintenance proposal and into the final Work Order. For example, a replacement fan blade, labour cost, and total estimated maintenance cost can now be carried through the approval process instead of being lost between different stages. The AI doesn't create a Work Order by itself. AI recommends → Engineer reviews → Engineer approves → Work Order is created. That's an important step toward making ProDiag AI useful in a real industrial maintenance environment. With 2 days left, the focus now shifts toward final testing, documentation, presentation, and preparing the project for submission. 🚀 #BuildInPublic #AIAgents #AIChallenge #PredictiveMaintenance #IndustrialAI #AI #Maintenance #AgenticAI #Engineering #SmartManufacturing #Industry40 #BuildWithAI