Knowledge retrieval that understands what connects to what.
GraphStaff’s proprietary graph-based engine follows the structure and relationships in your knowledge to assemble context that is more complete, explainable, and useful.
The answer is rarely in one chunk.
A policy depends on a definition. An endpoint depends on an authentication rule. Useful knowledge is connected, but conventional retrieval treats every passage as if it lives alone.
Every question activates a neighborhood.
The engine follows meaningful connections, so the context includes the definitions, policies, and exceptions the answer depends on.
Similarity finds a passage. Relationships build an answer.
Passages that sound like the query
Relevant sources plus connected prerequisites and constraints
Often filtered around the search layer
Applied while traversing candidate knowledge
A ranked list of text chunks
Sources and relationships that explain why context was selected
Frequently asked questions.
The short version of what teams usually want to know before they see GraphStaff in action.
What does graph-based retrieval mean?
It means GraphStaff represents knowledge as connected entities, concepts, pages, and relationships rather than treating every passage as an isolated block of text. Retrieval can follow those connections to assemble the context around a question.
Is this the same as a vector search system?
No. Semantic similarity can be one useful signal, but similarity alone does not express how concepts depend on, belong to, or constrain one another. GraphStaff uses explicit structure and relationships to guide retrieval and context assembly.
Why does this improve an AI answer?
Many questions require context spread across several related sources. Following known relationships helps the engine retrieve supporting definitions, prerequisites, policies, and exceptions instead of returning only the passage that sounds most similar to the question.
How are permissions handled inside the graph?
Access checks happen before protected knowledge becomes answer context. The engine filters candidate nodes and relationships using the requester’s identity and groups, so restricted material is not passed onward to the model.
Bring us a question your current search gets wrong.
A useful proof of concept starts with a difficult question and the knowledge needed to answer it. We’ll show you how the graph changes retrieval.