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 "Detective’s Wall": Nodes and Edges
To understand how GraphRAG works, imagine a detective solving a complex case. They don't just have a pile of witness statements; they have a wall covered in photos connected by red strings.
This is exactly how a Knowledge Graph structures your data:
- Nodes (The "Nouns"): These are the distinct entities in your business. On the detective's wall are these photos.
- Examples: A Customer, a Product SKU, a Supplier, or a Warehouse.
- Edges (The "Verbs"): These are the meaningful relationships that connect the nodes. On the wall, these are the strings.
- Examples: A Supplier ships a Part. A Part is used in a Product. A Product is sold to a Customer.
Traditional RAG is like having the photos scattered on the floor. You can find a specific photo, but you can't see the big picture. GraphRAG is the wall. It allows the AI to follow the strings (Edges) from one image (Node) to another to uncover the whole story.
The Secret Sauce: The Ontology (Your Business Blueprint)
Business leaders often hear the word "Ontology" and dismiss it as academic jargon. In reality, an Ontology is your Business Blueprint.
If you hire a new human analyst, you have to teach them your business language: "A 'Customer' has a 'VAT Number' and belongs to a 'Sales Region'. A 'Product' has a 'SKU' and is made of 'Components'."
An ontology is exactly that definition, written in a way that the AI can understand. Without an ontology, your AI is guessing. With an ontology, your AI understands the strict rules of your business. It allows element61 to build "guardrails" around the AI, ensuring it knows the difference between a Competitor and a Partner and preventing costly hallucinations.
When Should You Invest in GraphRAG?
Not every problem requires a graph. If you need to search through HR manuals, the standard RAG is faster and cheaper. However, GraphRAG is the critical unlock for complex, high-stakes scenarios:
- The "Global Answer" Problem: Standard AI is bad at summarising massive datasets. If you ask, "What are the top 5 recurring complaints across these 10,000 support tickets?", a standard model only reads a few tickets at a time. Microsoft GraphRAG can map the entire dataset, cluster it into "communities" of topics, and give you a precise, evidence-based summary of the big picture.
- Supply Chain & Logistics: Understanding the ripple effects of a disruption requires "multi-hop reasoning" (Supplier -> Part -> Product -> Customer). Only a graph can trace this path reliably.
- Financial Compliance & Fraud: Fraud is rarely an isolated event; it’s a network of suspicious connections. GraphRAG can detect when seemingly unrelated invoices actually share the same bank account or address—something a text search would miss entirely.
How element61 Builds This: Microsoft Foundry & Cosmos DB
At element61, we don't just talk about these concepts; we build them using the enterprise-grade Microsoft Cloud stack. We are moving beyond experimental "notebooks" to robust, scalable architectures.
Our reference architecture for "Reasoning Engines" combines two powerful platforms:
- The Brain (Azure Cosmos DB with Gremlin API): We use Azure Cosmos DB to store your Knowledge Graph. Using the Gremlin API, we model your business entities (Customers, Assets, Contracts) and their relationships. This isn't just a static database; it's a dynamic web of data that grows as your business operates. It allows us to perform complex "graph traversals" - asking the database to find connections three or four levels deep in milliseconds.
- The Factory (Microsoft Foundry): To orchestrate this, we use Microsoft Foundry (formerly Azure AI Studio). Foundry acts as the "AI Factory" where we deploy the Large Language Models (OpenAI) that interact with your graph. We build AI Agents in Foundry that can query the Cosmos DB graph to retrieve facts, reason over them, and generate an answer that is grounded in your actual business data, not just general internet knowledge.
The Verdict: Moving to "Glass Box" AI
For C-levels, the shift to GraphRAG offers one final, massive benefit: Explainability.
When a standard AI gives you a strategic recommendation, it's often a "Black Box", you don't know how it got there. With GraphRAG, the AI can show its work. It can say: "I recommend delaying the campaign because Supplier X is affected by the strike (Link A), which impacts Inventory Y (Link B)."
This transparency turns AI from a novelty into a trusted decision-making partner.
Ready to upgrade your AI from a librarian to a strategist? At element61, we help organisations define their Ontology and build the "Reasoning Engines" that drive competitive advantage. Contact us to explore how GraphRAG can unlock the hidden value in your data.
Stay tuned for a working example for the technical audience in Part 2.
