RAG and knowledge assistants
grounded in your data.

RAG is the right tool when answers must be grounded in a body of changing knowledge: documentation, policies, tickets, contracts. Fine-tuning changes behavior and style, not facts; a plain prompt works when the knowledge fits in context. We pick the pattern that fits, and often combine them.

Getting this wrong is expensive. We have seen fine-tuning projects that should have been RAG, and RAG systems that should have been a single well-designed prompt. We name the reason for the choice.

We build the full pipeline: data preparation and chunking, embeddings and retrieval, grounding and citation so answers point back to the source, and access control so the assistant never surfaces a document the user is not allowed to see.

Quality is measured, not hoped for. A RAG system ships with an eval set that scores answer accuracy and grounding on every change, so you can see quality drift before your users do.

Knowledge changes, so retrieval has to be maintained: re-indexing, monitoring retrieval quality, and watching for the silent degradation that erodes trust. We run it with you, then hand it over with the data pipeline and the eval baseline that make it maintainable.

This is the Build & Run offer applied to RAG: code in your repositories from day one, IP transfer in the contract, and a team that can keep it accurate after we step back.