ENTERPRISE AI SOLUTIONS
Knowledge and RAG Systems
Turn scattered, stale and mutually contradictory organizational knowledge into a retrieval layer that both people and AI can trust.
01 / THE PROBLEM
The business problem
Organizational knowledge usually exists — spread across shared drives, chat history, personal notes and one senior colleague's memory, in versions that contradict each other. Attach RAG to that and you don't get better answers. You get stale information distributed faster.
02 / IMPACT
Operational impact
When answer correctness cannot be relied on, users revert to asking a person. Usage declines while maintenance cost continues. The genuinely dangerous case is the one where a user believes a stale answer.
03 / APPROACH
Our approach
Knowledge governance comes before retrieval technology. We establish source, owner, validity period and applicable scope for each body of knowledge, then handle chunking, embedding and retrieval. The system must be able to point at the document behind an answer, and must say it does not know when no reliable basis exists — rather than producing something plausible.
04 / ARCHITECTURE
Typical architecture
- 01 Knowledge source inventory and ownership definition
- 02 Chunking and embedding strategy
- 03 Hybrid vector and keyword retrieval
- 04 Permission filtering at retrieval time, not at presentation time
- 05 Source citation and traceability
- 06 Knowledge gap capture and feedback loop
05 / DELIVERABLES
Deliverables
- Knowledge inventory and governance rules
- Retrieval system implementation
- Permission-aware retrieval layer
- Source citation presentation
- Knowledge gap reporting and update workflow
06 / GOVERNANCE
Governance and deployment
- Permission filtering happens during retrieval, never only in the UI
- Every answer traces back to a source document
- Stale knowledge has an explicit expiry mechanism
- When no basis is found, the system says so rather than speculating
07 / USE CASES
Use cases
COMMON SITUATIONS
Common situations
- RAG answers look reasonable but cite superseded documents
- Users cannot see the basis for an answer, so they don't rely on it
- Contradictory departmental documents are ingested without reconciliation
Frequently asked questions
- Can a RAG system return incorrect information?
- Any retrieval system is bounded by source quality, which is why we put knowledge governance ahead of retrieval technology and require the system to cite its basis and admit when none exists. That does not guarantee zero error — it makes error detectable and traceable.
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