Knowledge management with RAG when context is no longer searchable.
Large teams lose time because knowledge is spread across too many tools. RAG only works when sources, permissions, and daily usability are designed correctly.
engineering time saved in the Uber Genie example
faster research in documented deployments
questions handled in a large internal knowledge system
typical path to real team adoption
Uber Genie: make internal knowledge usable instead of burning senior time.
Critical answers existed across Slack, wikis, and documentation, but not in a searchable system with trusted context.
Starting point
What changed
Impact
questions handled
covered channels
time saved
JPMorgan: reduce research time and increase time spent with clients.
Advisors spent too much time collecting information across systems instead of using that knowledge in conversations.
Starting point
What changed
Impact
faster research
gross sales lift
client-base growth over time
How we operationalize knowledge systems at RakenAI.
We do not just build vector search. We build a usable layer of retrieval, citation, permissions, and daily team workflow.
Source integration
Documents, wikis, tickets, and chat history are connected deliberately.
Citations
Answers expose source and provenance instead of acting as unsupported claims.
Access control
Existing permissions stay part of the system instead of being bypassed.
Daily use
The system lives where teams already work, not in an isolated experimental UI.
What teams usually ask next.
When does a RAG system actually get used?
When it is embedded into existing work surfaces, exposes sources clearly, and feels meaningfully faster than the old search behavior.
Is this mainly internal or external?
Primarily internal. It improves team speed and answer quality. External visibility gaps are usually better addressed through the audit.
How do you reduce hallucination risk?
Through strong retrieval design, required source grounding, permission control, and hard limits on what the system is allowed to answer.
Build knowledge management that teams actually use every day.
We assess source quality, permissions, and workflow placement before a RAG system goes into production.