Industry
Edge Computing | Cloud Infrastructure | Telecommunications
Business Challenge
Modern cloud-native edge platforms generate a continuous stream of infrastructure, application, and Kubernetes events. While observability platforms effectively detect anomalies, engineering teams often spend valuable time interpreting error messages, searching technical documentation, reviewing historical incidents, and determining the appropriate corrective action.
The opportunity was to leverage Artificial Intelligence to augment existing observability investments by transforming raw operational telemetry into contextual, actionable guidance – accelerating incident resolution while reducing dependence on specialized platform expertise.
Solution Approach
Led the product vision and solution strategy for an AI-assisted incident resolution capability that combined Retrieval-Augmented Generation (RAG) with an open-source Large Language Model (LLM) to provide intelligent operational assistance.
Streaming error events from the enterprise observability platform were ingested through Apache Kafka message bus and enriched using a centralized knowledge repository containing platform documentation, deployment procedures, operational runbooks, historical incidents, and known error signatures.
The AI engine analyzed incoming topic events and generated contextual recommendations, including probable root cause, explanation of the failure, recommended corrective actions, and references to relevant operational knowledge – enabling engineering teams to transition from reactive troubleshooting to AI-assisted decision making.
Solution Architecture
┌─────────────────────────────────────────────┐
│ Engineering Operations • Service Desk • ChatOps
└─────────────────────────────────────────────┘
▲
┌─────────────────────────────────────────────┐
│ AI Incident Recommendations
└─────────────────────────────────────────────┘
▲
┌─────────────────────────────────────────────┐
│ AI Incident Processing Engine
└─────────────────────────────────────────────┘
▲
┌─────────────────────────────────────────────┐
│ Apache Kafka Event Streaming
└─────────────────────────────────────────────┘
▲
┌─────────────────────────────────────────────┐
│ Enterprise Observability Platform
└─────────────────────────────────────────────┘
▲
┌─────────────────────────────────────────────┐
│ Cloud-Native Edge Platform
└─────────────────────────────────────────────┘
Business Outcomes
- Captured institutional knowledge into an AI-powered knowledge repository.
- Accelerated infrastructure incident diagnosis through AI-assisted analysis.
- Reduced Mean Time to Resolution (MTTR).
- Improved consistency of engineering response across operational teams.
- Reduced dependency on specialized platform expertise.
- Enhanced operational resilience for mission-critical cloud-native infrastructure.
- Established a scalable foundation for AI-assisted Operations (AIOps).
Technologies
Artificial Intelligence
- Retrieval-Augmented Generation (RAG)
- Open-Source Llama Large Language Model
- Vector Database
- AI-Assisted Operations (AIOps)
Cloud-Native Platforms
- Kubernetes
- Apache Kafka
- Enterprise Observability Platform
- Cloud-Native Edge Infrastructure
How Reasoned Insights Applies This Experience Today
Reasoned Insights helps organizations embed AI directly into operational workflows by combining Agentic AI, Retrieval-Augmented Generation, cloud-native architectures, and observability platforms. We enable enterprises to transform operational data into intelligent decision support, reducing operational complexity, accelerating incident response, and improving service resilience through practical, business-focused AI solutions.