DSS - Data Science & Strategie

GraphRAG goes beyond traditional RAG by not only retrieving relevant information but also following the chain of relationships between business entities. While Vector RAG finds similar text fragments, it often misses the causal links needed for real decision-making. GraphRAG, on the other hand, connects supplier, component, product, and customer data to deliver explainable, business-ready insights.

In the last two years, most organisations have successfully deployed their first "Chat with your Data" pilots. These systems, built on Traditional RAG (Retrieval-Augmented Generation), are excellent librarians. If you ask, "What is our travel policy?", they can instantly find the PDF, read page 12, and summarise the allowance for dinner expenses. But what happens when you ask a question that requires reasoning, not just reading? "How will the delay in the supplier shipment from Shanghai impact our Q3 production targets for the Alpha product line, considering our current inventory levels?" A traditional AI will fail here. It will find documents mentioning "Shanghai," "Q3," and "Alpha," but it won't understand the causal chain. It sees words; it doesn't see the relationships between your supplier, your inventory, and your production schedule. To move from "AI that reads" to "AI that thinks," we need to upgrade from Vector Search to GraphRAG.

The enterprise AI landscape has shifted from "Copilots" - chat-based assistants - to Agentic AI. At Microsoft Ignite 2025, this shift was formalized with Microsoft Foundry. This is not just a rebrand of Azure AI Studio; it is a fundamental re-architecture designed to move organizations from AI experimentation to an "AI Factory" model. For our customers, this means the ability to build fleets of autonomous agents that are secure, governed, and capable of executing complex business processes.