RAG Architecture and LLM Integration
Answers based on your real information, not guesses
A generic language model knows nothing about your business: it doesn't know your internal policies, your contracts, your technical documentation, or your product catalog. A RAG (Retrieval-Augmented Generation) architecture solves that by connecting the model to your own information, so every answer is grounded in real documents, databases, or APIs instead of what the model "thinks" it remembers.
What's included
- Indexing of your documents, internal wikis, or knowledge bases.
- Semantic search system over that information.
- Integration with the language model that best fits your case.
- Source citation in every answer, so it can be verified where it came from.
- Connection to databases or APIs for information that changes constantly.
Who it's for
For businesses with a lot of internal information — technical documentation, policies, support history, catalogs — that want both their team and their customers to find accurate answers about that information without having to search for it manually.
How we work
We start by understanding what information is critical and where it lives today. We design the indexing and search system before connecting the language model, because answer quality depends directly on how well the right information gets retrieved. We test with real questions from your business before calling the system done, not generic examples.
Frequently asked questions
What exactly is RAG?
Retrieval-Augmented Generation: instead of the language model answering only from what it "remembers" from training, it first searches for real information in your documents or databases and bases its answer on that.
Does this stop the model from making things up?
It significantly reduces it by grounding answers in real, verifiable sources, but no AI system is infallible. That's why we design verification mechanisms and cite the sources used in each answer.
Do I need my documents organized in a special way beforehand?
Not in advance. Part of the work is precisely structuring and indexing your existing information so the system can search it effectively.
Have internal information your team struggles to find? Let's talk.