Connect your enterprise knowledge base to large language models via Retrieval-Augmented Generation. AI systems that respond with accurate, up-to-date information, minimising hallucination.
Connect your enterprise knowledge base to large language models via Retrieval-Augmented Generation. AI systems that respond with accurate, up-to-date information, minimising hallucination.
Base LLMs are limited to their training data and cannot access current enterprise knowledge. RAG solves this: documents are split into semantic chunks, indexed in a vector database, and when a user query arrives the most relevant content is retrieved in real time and added to the LLM's context window. Answers are no longer guesses — they are source-verified, citable responses, dramatically reducing hallucination risk.
Catalogue all knowledge sources — documents, databases and APIs.
Split text into semantic chunks; generate embedding vectors.
Store embeddings in Pinecone, ChromaDB or Weaviate.
Wire retrieval results into the LLM prompt context window.
Measure retrieval precision; tune chunk size and top-k for quality.
Index PDFs, Word docs, web pages, SQL and API data into a unified vector knowledge base.
Embedding-based nearest-neighbour search finds relevant content far beyond keyword matching.
Ground every response in source documents; deliver verified, citable information.
Automatically re-index when new content is uploaded, keeping the knowledge base always current.
Combine semantic vector search with keyword search for maximum precision and recall.
Document-level permissions — users only retrieve content they are authorised to see.
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