This project involved designing and implementing an AI decision-support system to improve how process stability and process capability decisions are made in a steel manufacturing context. The core problem addressed was the time, inconsistency, and misdirected effort involved in manually interpreting process data. The solution was built as a structured, end-to-end workflow that ingests raw process data, performs parallel analysis of process behavior and capability, and synthesizes results into a clear, leadership-ready narrative grounded in company standards.
The implemented Azure AI solution integrates multiple Azure AI services: • A fully functional Prompt Flow hosted in an Azure AI Hub Project. • End-to-end data ingestion from Azure Blob Storage using a Python data access node. • Parallel analytical branches for Process capability analysis and Process behavior (stability) analysis. • A persistent, RAG-backed Aggregator Agent hosted in an Azure AI Foundry Project that synthesizes results into a leadership-ready narrative.
If I had to explain the architecture to a non-technical manager, I would describe it as a decision pipeline: raw process data goes in, specialized steps analyze stability and capability in parallel, and a final expert step produces a clear recommendation. Each component does one job well, which makes the system easier to understand and trust.
I see this pattern being valuable anywhere data-driven decisions require interpretation, not just metrics – such as financial analysis or risk reviews.