Knowledge retrieval and RAG: answers from your own data, with the source attached
Retrieval is what lets a model answer from your documents and data instead of from its training. I build that layer over the sources you already have, such as Confluence, Jira and database exports, with permissions enforced at query time and every answer linked to where it came from.
Key takeaways
- An answer without a source is a guess the reader cannot check.
- Permissions have to hold at the moment of the query, per user.
- Structured data usually wants a generated query, not a vector search.
- Stale and duplicate pages hurt retrieval more than the choice of model.
Two kinds of retrieval, and which data wants which
- Documents
- Policies, runbooks and tickets are searched by meaning, and the passages found are handed to the model with their links.
- Tables
- Ledgers, exports and issue fields are better served by translating the question into a query, running it, and returning the rows.
Many real questions need both, for example a policy passage plus the records it applies to.
A delivered example: questions over audit exports
For Auditlab the data was tabular, so the engine turns an auditor’s question into SQL and returns the result on the Jira ticket, with the query kept for the audit file. The measured saving and the controls are in the Auditlab case study.
Where do-it-yourself retrieval stops
Pointing a chatbot at a folder works for a demo. It stops working when two versions of a policy disagree, when a user asks about a space they may not open, or when someone needs to know why an answer was given. Cleaning the sources, carrying permissions through and keeping the trail are most of the real work.
When retrieval is the wrong call
If the platform’s built-in search assistant already reaches the content, use it. If the knowledge is not written anywhere, retrieval has nothing to find, and the first job is documentation. And if the questions are a fixed list, a report answers them more cheaply than a model.
This is often part of an FDE engagement. Before an agent can route tickets or answer from your knowledge base, the configuration underneath has to be right. The AI Readiness Audit finds out what needs fixing first, in two weeks, for a fixed $3,500.
Questions
Do you need to copy our data somewhere?
How do you measure answer quality?
Will it work with Rovo?
If your team answers the same questions by digging through the same systems, bring one of those questions to a call.
6–12 weeks
Fixed per scoped build