Grounded answers, not confident guesses
Retrieval-augmented generation (RAG) connects an AI agent to your actual documents. Instead of answering from a model's general memory, it retrieves the relevant passages from your content and answers from those, so replies reflect your reality, not the internet's average.
That grounding is what makes citations possible: the agent can point to the policy, manual, or article an answer came from, so people can verify it and trust it.
- Answers built from your documents, wikis, PDFs, and tickets
- Source citations so answers are verifiable
- Stays current as you update your content
- Abstains when your documents don't cover the question
- Reduces hallucination compared to an ungrounded model
The retrieval quality is the whole game
A RAG system is only as good as what it retrieves. We invest in the unglamorous parts: cleaning and chunking your content well, choosing the right embeddings and search, and testing that the agent actually finds the right passage for real questions.
We also handle the boring truths: messy or contradictory docs produce messy answers, so part of the work is surfacing gaps and conflicts in your knowledge base, not papering over them.
- Document ingestion, cleaning, and chunking
- Vector search and retrieval tuning for your content
- Evaluation on real questions, not just a demo
- Access controls so users only retrieve what they're allowed to
- A path to keep the knowledge base fresh over time
More on custom ai agents
Frequently asked questions
Does RAG stop the AI from making things up?
It sharply reduces it by forcing answers to come from your documents, and citations let you verify. It's not a total guarantee, so we also tune the agent to say 'that's not in our docs' rather than guess.
What if our documentation is a mess?
That's common and workable. Part of the project is assessing your content, structuring it for retrieval, and flagging gaps and contradictions. Better source material means better answers, and we'll be honest about where yours needs work.
Can it respect who's allowed to see what?
Yes. We can scope retrieval to a user's permissions so the agent never surfaces documents someone shouldn't access. We design that access model with you as part of the build.