Large language models are powerful, but out of the box they don't know your business, your policies, or your documents. Retrieval-Augmented Generation (RAG) changes that.
With RAG, we connect your content—documents, presentations, PDFs, audio transcripts, and more—to an LLM so answers are grounded in your data, not the model's training set.
Step 1: Choose the right content
Start with a focused corpus: policies, playbooks, SOPs, or implementation guides. You get faster wins and better signal for tuning.
Step 2: Ingest and index
We parse documents, segment them into chunks, generate embeddings, and store them in a vector index. This is the backbone of semantic search.
Step 3: Retrieval + generation
When a user asks a question, the system retrieves the most relevant chunks and passes them to the LLM. The answer is generated using only that context—improving accuracy and traceability.
Step 4: Governance and observability
We add guardrails (prompting, filters, policies) and observability so you can see how the system behaves and continuously improve it.
If you're exploring RAG for your organisation, reach out and we can walk through your use case.