An engineer asks: "Why did section B-12 go down three times last year?" Standard RAG finds three different documents — three downtime acts — and that is all it can offer. It doesn't see that all three cases involve the same pump, one that had already been repaired twice under warranty and once replaced off-schedule. The answer is in the archive, but it is spread across five documents, and without a link between them it is just three unrelated facts.
That is the ceiling of flat retrieval: it answers a question about a passage, not about an asset. The moment a question needs to walk a chain — asset → incident → repair → repeat incident — the system has to know not just what the documents say, but that they are talking about the same pump.
GraphRAG addresses this by adding a layer of entities and relations on top of the text index: instead of scattered passages, the system builds a graph — asset → document → event → decision. In their 2024 paper, Edge et al. (Microsoft) showed that a graph-based approach using community summaries closes a class of questions plain top-k retrieval misses — especially ones that need an overview of the whole corpus, not a single passage.
Back to the pump on section B-12: with a graph, the engineer's answer is no longer a list of three acts but a chain — equipment passport, warranty repair #1, incident #1, an off-schedule repair, incident #2, replacement. You see not just what happened, but why it kept happening.
There is a catch: a graph only links what can be linked. If the equipment passport, work orders, and downtime acts each refer to the same pump differently — an inventory number here, an informal shop-floor name there — the graph simply won't see it as one asset. So the first step toward GraphRAG isn't picking a technology; it's checking whether the archive has a consistent identifier that survives a change of document.
In Knovium, the graph is not a replacement for retrieval but a continuation of it: retrieval finds the relevant passages first, then the graph arranges them by asset and event. The engineer still gets a text answer, but behind it is not one matching paragraph — it's a traced chain where every link is clickable down to a specific document and page.