RAG (Retrieval-Augmented Generation) development

RAG retrieves relevant passages from your documents at question time and gives them to the model, so answers are current, cited and access-controlled. Fine-tuning changes how a model behaves, not what it knows, and cannot keep up with changing content. For knowledge questions RAG is the right tool; the two are sometimes combined.

We store permission metadata with every chunk and filter retrieval by the requesting user, so nobody sees an answer built from a document they cannot open. Permissions are synced from the source system (SharePoint, Drive, Confluence) rather than copied once.

That depends mostly on retrieval quality and content quality. We build a test set of real questions with expected sources, measure retrieval hit rate and answer correctness, and tune chunking, embeddings and reranking until the numbers are good. We report those numbers rather than promising percentages up front.

For most teams pgvector in an existing Postgres is enough and simplest to operate. Pinecone or Weaviate suit larger scale and managed hosting; Qdrant and Milvus are strong self-hosted options; ChromaDB and FAISS fit prototypes and local use. We recommend based on scale, hosting and existing skills.

Yes. Connectors watch sources for changes and re-embed only what changed, so the index tracks the source of truth. Freshness and failed-sync alerts are part of the pipeline. For high-change sources such as ticketing systems we index near real time; for policy documents a nightly sync is usually enough and cheaper.

Yes. The full stack (embeddings, vector store and an open-source LLM) can run inside your cloud account or on your own hardware. Quality is somewhat lower than the best hosted models but often sufficient, and we help you decide with a side-by-side test.