Retrieval-Augmented Generation for accurate, source-cited AI responses.
Optimised vector store architecture using Pinecone, Weaviate, or Qdrant for lightning-fast semantic search at scale.
Automated ingestion pipelines that chunk, embed, and index documents from PDFs, databases, wikis, and APIs.
Every AI response includes verifiable source citations with page numbers, links, and confidence scores.
Combine semantic vector search with keyword-based BM25 retrieval and metadata filtering for maximum relevance.
Document-level permissions ensuring users only access information they are authorised to see.
Incremental indexing that keeps your knowledge base current as documents are added, modified, or archived.