RAG Knowledge Systems
Give your teams instant access to your organisation's collective knowledge with accurate, cited answers drawn from your own documents. Built on Retrieval-Augmented Generation and deployed on your infrastructure.
Private Knowledge Indexing
Your documents — PDFs, Word files, wikis, database exports are chunked, embedded, and stored in a vector database that runs entirely on your infrastructure. No data is ever sent to an external embedding API.
Hybrid Search
Combines dense vector search (semantic similarity) with sparse BM25 search (keyword matching). A reranking model selects the most relevant passages, improving retrieval accuracy on technical and domain-specific content.
Document-Level Access Controls
Users only retrieve content they are authorized to see. Access filters are applied at query time — a sales rep cannot retrieve HR policies, a junior employee cannot access board documents.
Source Attribution
Every response is accompanied by citations: which document, which section, which page. Users can verify the source directly. This is critical for regulated industries where answers must be traceable.
The Knowledge Access Problem
Most organisations have decades of accumulated knowledge locked inside documents that are difficult to search effectively. Policy manuals, technical specifications, research reports, meeting notes, compliance documentation — the information exists, but finding the specific answer to a specific question requires knowing which document to open, which section to read, and how to interpret it in the right context.
Traditional keyword search helps but often returns too many results or misses semantically relevant content that uses different terminology. RAG solves this by combining semantic understanding (the AI understands what you mean, not just what you typed) with precise document retrieval (every answer is grounded in a specific source passage). The result is an AI system that can answer questions about your internal knowledge with the accuracy of a subject matter expert and the speed of a search engine.
How RAG Works
Document ingestion
Your documents are parsed, cleaned, and split into semantically meaningful chunks (typically 256 to 512 tokens). Each chunk is embedded into a dense vector using a locally hosted sentence-transformer model. The vectors are stored in a vector database (Qdrant or Weaviate) running on your infrastructure.
Query processing
When a user asks a question, the query is embedded using the same model. The vector database returns the most semantically similar document chunks. In parallel, a BM25 keyword search retrieves exact-match results. A reranking model combines both result sets and selects the most relevant passages.
Answer generation
The retrieved passages are provided to the language model as context, along with the user's question. The model generates an answer strictly grounded in the retrieved content, with explicit citations to source documents. If the retrieved context does not contain enough information to answer confidently, the model indicates this rather than guessing.
Knowledge Base Scale
RAG systems scale from small document collections to enterprise-grade knowledge bases. We have deployed systems ranging from hundreds of documents (focused departmental knowledge bases) to millions of document chunks (organisation-wide knowledge platforms). Key considerations at different scales:
- —Small (under 1,000 documents): A single-node deployment with Qdrant and a 7B parameter model is sufficient. Can run on a single GPU server with 24GB VRAM. Typical use case: departmental knowledge base or compliance documentation.
- —Medium (1,000 to 50,000 documents): Requires careful chunking strategy and embedding model selection. Hybrid search becomes important to handle both semantic and exact-match queries. May require a larger model (13B+) for complex reasoning across retrieved passages.
- —Large (50,000+ documents): Distributed vector database deployment with replication and sharding. Embedding pipeline must be parallelised to handle initial indexing in reasonable time. Query routing and caching strategies become important for response latency under concurrent load.
Use Cases by Industry
Technology Stack
Vector databases, embedding models, and retrieval frameworks