We build retrieval-augmented systems whose language model answers only from a verified body of your knowledge, so that every statement it makes can be traced to a source you control.
What retrieval-augmented generation is
A language model on its own answers from what it absorbed in training, which is general, dated and unattributed. Retrieval-augmented generation changes the arrangement: before the model writes, the system retrieves the passages relevant to the question from a store you maintain and gives them to the model as its material. The model composes; the store supplies the facts.
Why the store matters more than the model
The quality of the answer is bounded by the quality of what is retrieved. A store of unsorted documents returns unsorted passages, some of them contradictory, some obsolete. We ground retrieval in a knowledge graph and in documents that have been classified against it, so that what reaches the model is current, attributed and consistent with the rest of what your organisation holds to be true.
Governed integration
We constrain the model to cite what it was given and to say when the store holds nothing relevant. Outputs are logged with the passages that produced them, so an answer can be audited in the same way as any other record. Access to the store follows your existing access control, so a user cannot receive, through the model, material they could not read directly.
What you get
A system that reasons over structured enterprise knowledge rather than statistical pattern alone, that can be corrected by editing the source rather than retraining the model, and whose every answer names its evidence.
