PRODUCT
Retrieval-Augmented Generation, grounded in your knowledge graph.
Every answer cites the exact paragraph, table cell or transaction it came from. The model can't invent entities or relationships that aren't in your graph. No black box. No data leaves your environment.
Standard RAG indexes documents as flat chunks and asks a language model to stitch the answer together. In a demo this looks magical. In a regulated workflow it breaks: the model retrieves the right paragraph but answers from a different one, two documents disagree and the system silently picks one, a relationship that spans three documents never gets connected because each chunk sits alone.
For high-stakes work in financial, OSINT and public sector, probably right is insufficient.
GraphRAG is a question-answering interface for your data with three properties standard RAG cannot give you. Answers are grounded in concrete entities and relationships. Every claim links back to its source: a page, a row, a transaction. And the graph lets the model reason across documents: it follows the path from a person to a company to an invoice to a shipment and answers questions that no single chunk could.
Documents, databases and structured exports are ingested and parsed. Entities, relationships and events are extracted into the knowledge graph against your ontology. A semantic index sits beside the graph for natural language search, but every retrieval is anchored to a graph node.
When you ask a question, GraphRAG identifies the entities involved, traverses the graph along the relationships that matter for that question, pulls back the supporting source fragments, and asks the language model to compose an answer using only those fragments. The model never invents an entity or a relationship that is not in the graph.
The output is an answer plus a citation set: every sentence carries a link to the page, row or document that supports it. If the citation is missing, the answer is missing.
In media to query continuity facts across thousands of episode scripts: have these characters met before, who was last seen with whom, when did this thread close. In internal corporate knowledge bases where employees ask natural-language questions and get back the page of the policy that answers them. In OSINT and security workflows where every claim about a person, organisation or event needs to be backed by source.
Chatbots optimise for fluency. GraphRAG optimises for being right and being able to prove it. The graph is what makes the difference: it forces the model to reason over a structured representation of your data rather than guessing across loose chunks of text.
That structure is also what makes the system auditable. A regulator, a court, an internal compliance officer can ask why the system gave a particular answer, and the answer is a path through the graph and a list of sources. The system can be wrong, but it cannot be unaccountable.
Is this just RAG with extra steps?
No. Vanilla RAG retrieves text chunks. GraphRAG retrieves entities and relationships, then uses text only as evidence. The reasoning unit is the graph, not the chunk.
Which LLMs does it work with?
Any. Open-source (Llama, Mistral, Bielik for Polish) or commercial. The architecture is model-agnostic, so you can swap models as the field evolves without rebuilding the graph.
Can it run fully offline?
Yes. Air-gapped deployment with local models is supported and deployed in production with public-sector and security clients.
How long until we see results?
The standard pilot is three weeks: scope, ontology and graph build, first answers on real data. Full rollout depends on data volume and integration scope.
How is this different from Networks Notebook?
Networks Notebook is the analytical workspace: graph exploration, hypothesis testing, network analysis. GraphRAG is the question-answering interface on top of the same graph. Most clients use both.
Bring a sample dataset. We will show you the same question answered by vanilla RAG and by GraphRAG, side by side, with citations.
Ready to explore?