RAG
Search the right passages first, then let the model write.
RAG is the retrieval layer behind a trustworthy assistant. We ingest PDFs and HTML, store embeddings for semantic search, retrieve the passages that match a question, and only then generate an answer that cites those sources. When new documents arrive, the corpus can be refreshed on a schedule.
How we work with you
Ingestion
Parse titles, numbers, status, summaries, and full text from the files and pages you care about.
Vector search
Embeddings in a vector store so questions match meaning, not only exact keywords.
Cited generation
The model answers from retrieved chunks and links back to the regulation, article, or file.
Fresh corpus
Scheduled updates so newly published documents enter search without a manual reindex every time.
Choose RAG when the assistant must stay tied to a changing library of source documents.