RAG & Knowledge Systems
We build retrieval-augmented generation systems that let an LLM answer from your own documents, PDFs, and knowledge base, with citations, so responses are accurate and traceable instead of made up.
What it is
RAG connects an LLM to your content. Instead of guessing, the model retrieves the most relevant passages from your data and answers from them, with sources. It is how you get an assistant that knows your product, not the whole internet.
Who it is forSaaS teams, consultancies, and support orgs sitting on documentation that customers and staff constantly search through.
pgvectorPineconeSupabaseLangGraphTypeScript
What we do
- Ingest and chunk your docs, PDFs, and pages, then embed them into a vector store.
- Build retrieval with tags and labels so answers stay scoped to the right content.
- Wire the LLM to answer with citations and a clear 'I don't know' path.
- Support both pre-loaded knowledge bases and live user uploads.
Have an idea for an AI agent?
Tell us the outcome you want. We will come back with a clear scope, timeline, and quote, usually within a day.