Enterprise Knowledge Assistant (Trusted RAG)

Problem Statement
Enterprise knowledge is spread across many documents and systems, making it hard to find reliable answers. Search results are often inconsistent, and AI assistants hallucinate or return incorrect information. This increases the risk of exposing sensitive data.
Pain Signals
“RAG keeps hallucinating.”
“PoC is done, but we’re stuck.”
“We can’t trust AI answers in production.”
Enterprise Knowledge Assistants (RAG)
Challenges
Solution
Technology Stack
Outcomes
Problem Statement
Enterprises often have a repository of vast knowledge spread across documents, systems and teams; however, search is unreliable, and AI assistants hallucinate or expose sensitive data.
Why It Matters:
Cost: Teams spend hours searching or validating answers manually, thereby extending the timeline and costs.
Risk: Unreliable RAG offers hallucinated or ungoverned responses, creating compliance exposure.
Reliability: PoC chatbots fail under real enterprise data complexity.
Compliance: Access control, lineage and auditability are often missing.
Velocity: Slow answers block decision-making and frontline execution.
What Cloudaeon Delivers
A production-ready Enterprise Knowledge Assistant that retrieves trusted answers across structured and unstructured data. Cloudaeon delivers governed ingestion, metadata-aware retrieval, controlled generation, continuous evaluation and AI Ops monitoring, so that accuracy and reliability are engineered in from day one.
Ideal For:
CTO, CDO, CIO
Enterprise Architecture & AI Platform teams
Knowledge-heavy functions (Legal, Ops, Support, Engineering)
Pain Signals:
Most of the teams we speak with notice the following challenges:
“RAG keeps hallucinating.”
“We can’t trust AI answers in production.”
“Search works, but context is missing.”
“Security won’t approve our chatbot.”
“PoC is done, but we’re stuck.”
Conclusion
Cloudaeon’s Enterprise RAG Solution is designed to move organisations from fragile AI experiments to reliable, auditable knowledge systems, then scale through a dedicated POD and ongoing AI Ops.
Talk to an expert and see how this could work for you.
