Retrieval-Augmented Generation
RAG gives AI agents access to your actual data — so they answer from facts, not training data. It's the difference between a chatbot that makes things up and one that cites your internal documentation.
What RAG Does
Retrieval-augmented generation connects language models to external knowledge sources at query time.
The Problem
Language models have fixed training data. They don't know about your internal docs, recent events, or proprietary information. Without RAG, they either hallucinate or refuse to answer.
The Solution
RAG retrieves relevant documents from your knowledge base, injects them into the model's context window, and lets it generate answers grounded in your actual data.
How It Works
Documents are split into chunks, converted to vector embeddings, and stored in a vector database. When a query arrives, similar chunks are retrieved and fed to the model alongside the question.
What You Get
Answers that cite your sources. An AI that knows your policies, procedures, and data. A system that stays current as your knowledge base evolves.
Value Pathways
How RAG creates compounding value over time.
Knowledge Capture
Every document, email thread, and wiki page becomes searchable context for your AI. Institutional knowledge that lives in scattered files becomes a unified, queryable resource.
Reduced Hallucination
Grounding responses in retrieved documents dramatically reduces fabrication. The model answers from evidence, not imagination. Citations let users verify.
Compounding Value
As you add more documents, the system gets more capable. New policies, updated procedures, recent reports — they're all available to agents immediately after ingestion.
Cross-Team Sharing
Knowledge that was siloed in one team's drive becomes accessible to agents serving the whole organisation — with access controls that respect the original permissions.
Security Postures
How this works across different deployment models and security requirements.
Use managed vector databases and embedding APIs — Pinecone, Weaviate Cloud, OpenAI embeddings. Fast to deploy, minimal ops burden.
Run your own vector store and embedding models on your infrastructure. Full control over where documents are processed and stored.
Fully offline RAG with local models and vector stores. Documents never leave your network. Required for classified or regulated data.
Sensitive documents stay on-premise, general knowledge uses cloud APIs. Route queries based on classification and sensitivity level.
Ready to build a RAG system?
I design and build RAG pipelines across all security postures — from cloud-native to fully air-gapped.