Retrieval-augmented generation that brings your own information into the conversation.
Let’s build itAn example: connect approved product documentation and release checklists to an assistant that cites its sources. Retrieval, access rules and answer quality are evaluated against real team questions.
A useful response. Connected to its sources.
RAG retrieves relevant material and supplies it as context to a model. Useful retrieval begins with source quality: document parsing, structure, metadata, update frequency and access permissions. We assess these before selecting an embedding model or database.
A knowledge assistant should help people find the evidence behind an answer. Source references, retrieval evaluation, permission-aware filtering and clear handling of missing information belong in the experience. Retrieval can improve grounding; it does not eliminate the need to verify model output.
Connect ingestion to the way your content changes. Design for document additions, edits, removals and access changes, then test representative questions. A staged rollout makes it possible to evaluate retrieval quality and identify gaps in the source material.
Organizations with document collections, internal knowledge bases, product documentation or support content.
Useful context before we start building.
Retrieval-augmented generation, or RAG, combines information retrieval with a language model. A system finds relevant source material and includes it in the model’s context when generating an answer.
No. RAG provides retrieved information at query time. Fine-tuning changes a model through additional training. They address different needs and can be combined when justified by the use case.
Yes. Retrieval can be designed around the permissions of the source systems. Access requirements and identity integration need to be defined before the knowledge system is implemented.
Tell us what you want to build.
We’ll discuss the scope and a useful next step.
