Semantic Search & RAG

Traditional keyword search fails when users don't know the exact terminology. Semantic search understands meaning — so a query for "how do I cancel my subscription" matches a help article titled "Account Termination Process", even if those words don't appear in the query.

Retrieval-Augmented Generation (RAG) goes further. Instead of returning a list of links, it reads the most relevant documents and generates a direct, conversational answer with citations. Users get the information they need in one shot, with links to the source material so they can verify accuracy.

At MrjnIT, we build RAG systems from the ground up — ingesting your documents (PDFs, Confluence pages, Notion databases, website content, internal wikis), embedding them into a vector database (Pinecone, Weaviate, Qdrant, or pgvector), and building the retrieval and generation pipeline on top. The result feels like having an expert who has read every document in your organisation.

Our RAG systems include chunk-level citation (users can click through to the exact paragraph the answer came from), hybrid search combining vector and keyword matching for best-of-both-worlds accuracy, and incremental ingestion pipelines that keep the knowledge base up to date as your content changes.