RAG Systems

What Is RAG?

Retrieval-Augmented Generation (RAG) connects a large language model to your proprietary data. Instead of relying on the model's training data (which may be outdated or irrelevant), RAG retrieves relevant documents from your knowledge base and uses them as context for generating accurate, grounded responses.

Why RAG?

Accuracy

Every response is grounded in your actual data. No hallucinations about your products, policies, or processes.

Transparency

RAG systems can cite their sources, so users know exactly where information came from. Essential for compliance-sensitive industries.

Freshness

When your data updates, the RAG system immediately reflects those changes. No retraining required.

Control

You decide exactly what data the AI has access to. Sensitive documents can be restricted by user role or department.

What We Build

• Internal knowledge assistants — Help teams find answers from company documentation
• Customer-facing Q&A — Let customers self-serve from your product docs and FAQs
• Document analysis tools — Upload and interrogate PDFs, contracts, and reports
• Research assistants — Academic and market research with source tracking

Architecture

Our RAG systems follow a proven architecture:

1. Ingestion pipeline — Documents are chunked, embedded, and stored in a vector database
2. Retrieval layer — Semantic search finds the most relevant chunks for each query
3. Generation layer — The LLM synthesises an answer using retrieved context
4. Evaluation layer — Automated checks for relevance, accuracy, and completeness

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Want to connect AI to your data? Let's talk about RAG.