Relevance is only one part of the picture
Semantic search helps find information that is similar in meaning to a question. That is valuable when terminology differs between sources. Business research often needs another kind of context as well: which company operates a location, how two records refer to the same entity, or which activity connects a supplier to a market. Similarity and relationships answer different questions.
What a graph contributes
A knowledge graph represents entities and the relationships between them. A product can satisfy a requirement, a document can describe a process, and an event can affect a supplier or an asset. Explicit links make these connections inspectable. The graph is only as useful as the evidence behind its nodes and edges, so each relationship needs an understandable meaning and source context.
Combining retrieval approaches
A practical GraphRAG pattern begins with relevant information found through vector retrieval and expands the context through selected graph relationships. The resulting evidence is supplied to a language model for a specific task. Neo4j’s GraphRAG tooling supports graph and vector retrieval patterns. The best pattern depends on the question, data structure, and evaluation results; adding a graph is not a universal shortcut to better answers.
Use models with purpose
Different tasks place different demands on language models. Field extraction, classification, matching, and narrative synthesis should be evaluated separately. Fast inference can improve throughput and responsiveness, but latency alone does not establish correctness. Model choice should balance quality, response time, cost, and the sensitivity of the information involved.
Learning and recovery need boundaries
A self-learning workflow needs a defined feedback loop: capture corrections, review them, evaluate the proposed change, and release an improvement. A self-healing workflow needs a defined recovery path: detect a failure, retry safely where appropriate, and escalate unresolved exceptions. These are engineering disciplines with measurable outcomes, rather than a promise that a system will operate without supervision.
From architecture to a useful deliverable
For a customer, the test is whether the information supports the decision. A custom data product provides consistent records; connected knowledge adds meaning and relationships; an intelligence feed helps track relevant change. Each needs clear scope, source evidence and an agreed approach to updates. Vector search, graphs and models are methods behind that delivery. Their value is the relevant, understandable information they help provide.