Problem Statement

AI initiatives are launched with high expectations to deliver confident answers and automation at high speed.

However, in reality, models degrade quietly where accuracy drops, hallucinations rise, and no one can explain why.

Why It Matters

• Cost: Engineers spend weeks firefighting inaccurate outputs instead of improving systems for automation and speed.

  
  

• Risk: Unreliable AI decisions create compliance and reputational exposure.

  
  

• Reliability: Outputs vary day to day with no clear signal.

  
  

• Compliance: Lack of an audit trail to answer why a model responded the way it did.

  
  

• Velocity: Teams pause rollouts because trust collapses.

What Cloudaeon Delivers

Cloudaeon operationalises AI reliability through a structured AI Ops layer. We implement continuous evaluation pipelines, LLM-as-judge scoring, retrieval-quality metrics and policy guardrails to measure accuracy, detect drift and enforce safety. Outputs are observable, explainable and production-ready, integrated into model lifecycle workflows and operated as a system, not a demo.

Our AI engineers focus on continuous evaluation where failures are visible and ownership is explicit, thereby moving teams from demo to operational trust.

Ideal For

• CTOs and CDOs responsible for production AI risk

  
  

• AI and Platform teams running RAG or agent systems

  
  

• Enterprises moving from PoCs to adoption

Pain Signals

Most of the teams we speak with notice the following challenges:

• “Accuracy looked fine last month, now it’s unpredictable”

  
  

• “We don’t know when the model is wrong”

  
  

• “Every release feels risky”

  
  

• “Compliance asked how outputs are validated, we couldn’t answer”

Architecture Overview

Conclusion

AI that isn’t measured will fail silently. The real risk isn’t bad answers. It’s not knowing when they start.

Talk to an expert and find out how this could make a difference.