GraphRAG inference is the answering stage of an AI system that pulls from a knowledge graph, a web of connected facts, instead of just grabbing text that looks similar to your question. When you ask something, it follows the relationships between people, brands, and ideas to build the answer. For agencies, it is the difference between an AI that can find a fact and one that understands how everything on an account connects.
Also known as graph-based retrieval augmented generation, graph RAG
Standard retrieval works by similarity. You ask a question, the system finds chunks of text that resemble it, and hopes the answer is buried in there. GraphRAG takes a different path. Before you ever ask, it reads the source material and builds a structured map of the entities involved and how they relate: which brand owns which product, which campaign ran when, who reported to whom.
GraphRAG inference is what happens at query time. Instead of matching keywords, the system walks that map, following the connections to assemble an answer that reflects relationships rather than surface resemblance. That is why it can handle questions that span dozens of documents, where the answer depends on how the pieces fit together rather than on any single passage.
Most agency questions are not lookups. They are relationship questions, and GraphRAG inference is built for exactly that.
Connecting a sprawling account. When a client has a dozen product lines, three years of campaigns, and a tangle of sub-brands, keyword search returns fragments. A graph-based system can answer how those pieces relate, which is usually the real question.
Competitive and category mapping. Tracing how a competitor’s positioning shifted across channels and time is a relationship problem. GraphRAG inference is suited to it in a way simple similarity search is not.
Fewer confidently wrong answers. Because the system reasons over explicit connections, it is less likely to stitch unrelated snippets into a plausible-sounding mistake.
An agency builds an internal AI assistant on five years of work for a CPG client: briefs, campaign recaps, brand guidelines, and research decks. A strategist asks how the brand’s tone shifted after its 2024 repositioning and which campaigns drove the change. A standard search returns a pile of documents that mention tone. The GraphRAG-based assistant instead traces the connection between the repositioning, the campaigns that followed, and the language used in each, and answers with the throughline. The strategist gets a story, not a reading list.
The workshop covers how agencies can build AI tools on their own body of work, and what separates retrieval that connects ideas from retrieval that just matches words.