RAG (Retrieval-Augmented Generation)

Why RAG?

Large language models are powerful but don’t know your internal docs, policies, or product details. Retrieval-Augmented Generation (RAG) fetches the right pieces of your data at query time and feeds them to the model, so answers are grounded in your content—with citations you can check.

What you get

• Accuracy – Answers based on your real documents and APIs, not the model’s training cut-off or guesswork.
• Sources – References to the exact documents or passages used, so you can verify and comply.
• Control – You decide what data is in scope; no need to retrain a model when content changes.
• Speed to value – Often faster and cheaper than fine-tuning, with updates by refreshing your index.

What we build

• Document RAG – PDFs, wikis, Confluence, Notion, or file shares indexed and searchable; the model reads retrieved chunks and answers with citations.
• API and structured data – Connect internal APIs or databases so the assistant can pull live data (inventory, CRM, etc.) when answering.
• Hybrid search – Combine keyword and semantic search so both exact terms and “meaning” are used to find the best context.
• Evaluation and tuning – We help you measure answer quality and improve retrieval + prompts so the system keeps getting better.

Who it’s for

RAG fits teams that have a lot of internal knowledge and want a chatbot, support assistant, or internal Q&A tool that actually uses that knowledge. We design the pipeline (ingest, chunking, embedding, retrieval, and prompt) so it fits your stack and scale.

Interested in RAG for your docs or data? Contact us with your use case and we’ll outline a practical plan.