Retrieval-augmented generation (RAG) searches your own documents for the passages relevant to a question and gives them to a language model, which answers with citations. We build the full pipeline: ingestion, chunking, embeddings, hybrid search, reranking, permissions-aware retrieval and evaluation, so answers stay accurate as your content changes.
Ask your whole company.
A knowledge assistant answering from your documents and citing its sources.
Pick a question to see how the assistant answers from your documents.
Built to hold up.
Ingestion and indexing
Connectors for PDFs, Google Drive, SharePoint, Notion, Confluence, email, databases and websites, kept in sync automatically.
Hybrid search and reranking
Vector plus keyword search with rerankers, tuned on your own questions for relevance.
Permission-aware answers
People only get answers drawn from documents they are allowed to see.
Citations and evaluation
Every answer links to its sources, and a test set scores accuracy on each change.
Private deployment
Run on your cloud or servers, with open-source models when data cannot leave.
Questions we often get.
Retrieval-augmented generation: the system finds the relevant passages in your documents first, then a language model answers using only that material and cites it.
Have something that should run itself?
Tell us what is slowing your team down. We will come back within one working day with an honest view on whether it is worth building and what it would take.