We build RAG pipelines so AI answers from your knowledge base securely, with citations and access control.
RAG is the right answer whenever accuracy matters more than creativity. We index your approved content into a vector store, retrieve the most relevant passages at query time, and require the model to answer from them — with citations back to the source document.
Retrieval is what makes AI answers checkable
Retrieval-augmented generation is the architecture we default to whenever accuracy matters more than creativity. Your approved content is indexed into a vector store; at query time the system retrieves the passages most relevant to the question; the model is then required to answer from those passages and to cite them.
The practical consequences are what clients value. Answers stay current because you update documents rather than retrain a model. Every response can be traced to a source, so staff and customers can verify it. And when an answer is wrong, the trace shows whether the retrieval or the generation caused it — the difference between a system you can fix and one you can only apologise for.
The parts that decide whether it works
Most RAG disappointments trace to unglamorous details. How documents are chunked determines whether a retrieved passage contains enough context to be useful. Metadata and filtering determine whether the system searches the right subset. Access control must be enforced at retrieval, not in the prompt, or a customer-facing assistant will eventually surface something internal. And the corpus itself needs an owner — outdated content produces outdated answers with complete confidence.
We treat those as design decisions with your input rather than defaults, and we build an evaluation suite from your real questions, including the ones where the correct response is to refuse.
Where it applies
Customer-facing support and product questions grounded in your documentation. Internal assistants over policies, procedures and historical records. Natural-language querying of operational data, as we have scoped for asset-intelligence platforms where a field user wants a plain-English answer with a citation back to the source record. In each case the value is not novelty — it is removing the delay between a question and a reliable answer.