RAG-based Incident Intelligence
The belief that shaped this platform: Enterprise IT resolution times should not depend on how well a human analyst remembers past incidents. They should depend on how well the system connects the dots.
Context: A large enterprise IT service management environment with thousands of incidents, change requests, and RCA documents flowing through ServiceNow every day. Over the years, the organization accumulated a vast amount of operational knowledge, but most of it remained buried inside tickets, documents, and individual teams. Finding the right information at the right time depended more on experience than on the system itself.
The problem: Incident resolution relied heavily on tribal knowledge. Engineers manually searched historical incidents, correlated symptoms with previous outages, reviewed recent change requests, validated RCA documents, and pieced together investigation timelines before identifying the likely cause. The process was slow, inconsistent across teams, and often resulted in duplicated investigations, higher MTTR, and missed opportunities to reuse existing organizational knowledge.
The architectural shift: Instead of treating historical incidents as searchable documents, the platform transforms enterprise operational knowledge into an intelligent decision-support system. Using Retrieval-Augmented Generation (RAG), semantic retrieval, contextual reasoning, and incident relationship analysis, the platform understands the intent, affected services, operational context, recent changes, and historical patterns behind an incident before correlating it with similar cases. Rather than returning documents, it delivers explainable, evidence-backed recommendations with confidence scores, probable root causes, related incidents, change correlations, and recommended remediation steps.
How it works: The platform ingests ServiceNow incidents, change requests, and RCA documents into Azure Blob Storage, generates embeddings, and builds Azure AI Search indexes supporting semantic and contextual retrieval. Every new incident is enriched with historical knowledge, operational context, incident timelines, and related changes before generating recommendations. The system predicts the most probable root cause, identifies similar incidents, highlights supporting evidence, and recommends resolution strategies while exposing confidence levels for every recommendation. Engineer feedback, investigation outcomes, and newly resolved incidents are continuously incorporated into the enterprise knowledge base, enabling closed-loop learning that improves retrieval quality, recommendation ranking, and overall system intelligence without retraining foundation models.
Designed for change: Enterprise operations evolve continuously through new applications, infrastructure changes, deployments, and emerging failure patterns. The platform continuously adapts by incorporating newly resolved incidents into its knowledge base, identifying recurring failure patterns, detecting gaps in operational documentation, and strengthening relationships between incidents, services, change requests, and infrastructure components. By understanding dependencies rather than isolated incidents, the platform moves beyond knowledge retrieval toward operational intelligence—helping engineers identify not only what happened, but why it happened, what changed, how similar incidents were resolved previously, and which systems are most likely to be affected.
Evaluation & Continuous Improvement: The platform is continuously evaluated across retrieval quality, AI reasoning, operational efficiency, and business outcomes. Retrieval effectiveness is measured through RCA Recall@K, Similar Incident Matching Accuracy, Context Precision, and Recommendation Evidence Score. AI performance is monitored using Root Cause Prediction Accuracy, Recommendation Acceptance Rate, Groundedness, Hallucination Rate, and Confidence Calibration. Operational success is measured through MTTR Reduction, Time to First Actionable Recommendation, Retrieval Latency, Knowledge Reuse Rate, and Recommendation Explainability, while user adoption is assessed through Engineer Trust Score, Feedback Acceptance Rate, and Productivity Improvement across support teams. These metrics ensure the platform continuously improves both technically and operationally as enterprise knowledge grows.
Outcome: Delivered a 70% improvement in incident intelligence, significantly reducing Mean Time to Resolution (MTTR) while improving consistency across support teams. Engineers now receive explainable, context-aware recommendations supported by historical evidence, related incidents, operational timelines, and confidence scoring instead of manually correlating enterprise knowledge across multiple systems. The result is faster investigations, more consistent root cause analysis, increased reuse of organizational knowledge, and a continuously improving enterprise intelligence platform that becomes more effective with every incident it processes.