RAG Development: AI Knowledge Assistants
We build AI assistants that answer questions from your own documents and data, cite the source for every answer, and only show people what they are allowed to see. This approach is called retrieval-augmented generation, or RAG.
Problems we solve
People waste time searching wikis, drives and old emails.
Support teams answer the same questions every day.
Generic chatbots make things up about your products.
What we build
- Internal knowledge assistants Answers from policies, procedures and wikis for your team.
- Customer support assistants Answers from your help center and product docs, with handoff to a person.
- Sales and proposal assistants Find past answers, case studies and specifications in seconds.
- Permission-aware search Results filtered by who is asking.
- Source citations Every answer links to the document it came from.
How it works
- Gather Identify the sources and how they stay up to date.
- Index Prepare and index content, keeping permissions intact.
- Tune Test answers against real questions and improve retrieval.
- Launch Release in the tools your team already uses, with feedback built in.
How to start
Most knowledge assistants start with a prototype sprint on one set of documents. Tell us where your knowledge lives.
Questions about Knowledge Assistants (RAG)
What is RAG?
Retrieval-augmented generation. The assistant first finds relevant passages in your documents, then writes an answer based on them, with citations.
Will it make things up?
Grounding answers in your documents and showing sources greatly reduces this. We test against real questions and set the assistant to say it does not know when sources are missing.
Which sources can it use?
Common ones include SharePoint, Google Drive, Confluence, Notion, help desks, PDFs and databases.
Can different people see different answers?
Yes. The assistant respects the permissions of the person asking.