AI enterprise knowledge and RAG system
Enterprise knowledge becomes discoverable, role-aware and usable with source context.
How the process could work
- select sources
- structure content
- implement access
- connect queries with passages
- provide source context
- surface uncertainty
- incorporate feedback under control
Public solution pattern without a client name, claimed accuracy or invented adoption figures.
Pattern structure
Challenge
Policies and specialist knowledge are dispersed, while an AI system must not receive unchecked access to all content.
Starting point
Knowledge domains, owners, approval state, currency and sensitivity are mapped.
Solution
A RAG system retrieves relevant approved passages for a request.
What people decide
Business owners approve sources, maintain validity and review consequential statements.
Data protection
Only necessary approved content is included; access and protection follow the actual information estate.
Outcome
The pattern shows traceable knowledge delivery; quality and coverage would be validated with representative questions.
Further development
New knowledge domains can be added once ownership is clear.
How the solution is built
- curated sources
- ownership and validity metadata
- retrieval
- answer or agent
- access control
- maintenance process
Components
- Knowledge Agent
- knowledge base
- RAG
- source information
- permissions
- expert review
Which process should work better in your organisation?
We begin with the work, the people and the current process — then assess which form of AI genuinely makes sense.