Knowledge lacks stewardship
Documents exist but ownership, access and updates are not organized.
Enterprise RAG and LLM
Determine whether your data calls for RAG, an LLM or another path
Review data, permissions and risk before choosing generative language for a business process.
Operational pain
Data that needs more than a generic chat
An answer drives action
The intended use needs boundaries because output can affect a process or customer.
F.A.L method
Technical choice guided by risk
- 01
Inspect sources and access
We assess quality, ownership, updates and permissions.
- 02
Test workflow suitability
We define where responses help, need review or should not act.
Delivery
Foundation for applied knowledge
Source map
An inventory of content, access and update owners.
Usage criteria
Approved scenarios, limits and controls for the chosen solution.
Fit
Signals to assess RAG or LLM
- An identifiable knowledge base exists
- Permissions and owners can be defined
- The workflow permits validation before expansion
Frequently asked questions
Does RAG eliminate incorrect output?
No. It still needs evaluation, scope boundaries and review matching the risk.
Should every document enter the knowledge base?
No. Include only sources with defined ownership, access and use.
Can your data support generative language use?
Request fit triage to assess sources, risk and the most suitable technical path.