Grounded answers that follow the connections between facts — fewer hallucinations, and every answer backed by a traceable path you can cite.
Standard retrieval-augmented generation pulls back chunks of text that look similar to the question. That works until the answer depends on how things connect — this supplier is owned by that company, which is subject to this regulation, which changed last quarter. Flat vector search cannot follow those hops, so it either misses the answer or invents one.
A knowledge graph makes the relationships explicit. Entities become nodes, relationships become edges, and a question that requires reasoning across several connected facts can actually be answered by traversing them. GraphRAG combines that structure with language models, so answers are grounded in a traceable path through real facts rather than a plausible-sounding guess.
The payoff is fewer hallucinations and answers you can cite. When a model can point to the exact chain of facts behind a claim, "trust me" becomes "here is why" — which is the difference between a demo and something a regulated business can actually deploy.
We start from the questions the current system gets wrong, and work backwards to the entities and relationships those questions actually depend on. That defines a graph schema tailored to your domain, rather than a generic ontology that fits nothing well. We then extract entities and relationships from your sources to populate it.
On top of the graph we build the GraphRAG retrieval layer, combining graph traversal with vector search so the model gets both the connected facts and the relevant surrounding text. Every answer carries the path it came from, so a person can verify the reasoning instead of trusting it blindly.
Answer questions that span rules, entities and their changes over time.
Connect scattered documents, wikis and systems into one queryable whole.
Trace ownership, relationships and exposure across entities.
Link findings, methods and citations across papers.
Surface hidden connections between accounts and actors.
Reason over components, suppliers and dependencies.
We identify the questions the current system answers wrong.
We gather the sources and define the entities and relationships that matter.
We design a domain-specific graph schema.
We extract, populate the graph and build the GraphRAG layer.
We evaluate answer accuracy and the quality of the cited paths.
We deliver the graph, the retrieval layer and a maintenance guide.
We combine a graph database with modern retrieval, using LLMs to help build the graph and to answer over it.
It answers questions that depend on connections between facts, which flat vector search misses, and it returns a traceable path so answers can be cited.
No. We design a schema around your real questions and refine it as the graph grows, rather than boiling the ocean upfront.
We extract entities and relationships from your existing documents and systems, with review where accuracy is critical.
Grounding answers in an explicit fact path measurably reduces confident wrong answers and makes the rest checkable.
Compact, efficient models that run at the edge — low latency, full privacy.
Privacy-safe training data at scale — augment, balance, and unblock ML.
Benchmark, stress-test and adversarially probe models before they ship.
Book a 30-minute call. We will tell you honestly whether we can help.
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