AI-driven Smart Ticket Triaging
Brillio
Brillio’s Smart Ticket Triaging solution brings agentic intelligence to end-to-end alert triaging for enterprise IT operations. IT teams today are overwhelmed by high alert volumes and redundant incidents, leading to increased ticket loads, support fatigue, and rising operational costs—while resolution times continue to lag.
Powered by ADAM (Agentic Data & Applications Management) AI Agents, the solution autonomously processes metrics and log-based alerts by suppressing non-relevant signals, preventing duplicate tickets, and enabling intelligent ticket creation, prioritization, and routing. It performs cross-service log correlation for root cause analysis and leverages SOPs and historical resolutions to recommend or automate remediation.
In production environments, the solution has achieved ~90% reduction in ticket volumes through noise suppression across multiple applications, significantly reducing L1 support effort and improving engineer productivity by focusing only on high-impact, actionable incidents.
Designed for enterprises with high alert volumes across in-house and commercial off-the-shelf applications, the solution enables 24×7 autonomous operations, reduces monitoring fatigue, and transforms IT operations from reactive monitoring to AI-driven, autonomous incident triaging - without disrupting existing ITSM and observability ecosystems.
Key Capabilities: • Alert Suppression: Filters non-relevant and downward-trending alerts to prevent unnecessary ticket creation • Actionable Alert Handling: AI-driven categorization, prioritization, and ticket creation with recommendations • RCA & Remediation: o Log alerts → cross-service correlation for root cause detection o Metric alerts → SOP and historical pattern-based remediation or auto-closure • Intelligent Prioritization & Routing: Assigns tickets based on severity, workload, and on-call schedules • Duplicate Ticket Prevention: Detects and consolidates similar incidents to maintain ticket hygiene • Centralized Operations Dashboard: Real-time visibility into MTTR, FLR, ticket trends, and agent performance
Business Outcomes:
• Up to 90% reduction in ticket volumes through intelligent noise suppression • Faster MTTR with accurate RCA and automated triaging • Reduced L1 effort and improved productivity • Lower monitoring fatigue by focusing only on actionable alerts • Always-on (24×7) autonomous operations
Built using Azure-native services such as Azure OpenAI, Azure Monitor, Log Analytics, and Azure Machine Learning, the solution enables scalable, secure, and AI-driven IT operations aligned with Microsoft’s AI-first vision.
Technical Architecture: The solution is built on a modular, agent-based architecture that integrates observability data, AI-led decisioning, and ITSM execution to enable autonomous alert triaging. • Ingestion Layer: Captures real time metrics, traces, logs & events etc. alerts from observability tool and historical ticket data, knowledge base articles etc. from ITSM platforms. Normalizes and unifies data across systems. • AI Agent Layer: Transforms raw alerts into context-aware, actionable insights: o Observability Agent - Evaluates system health, performance, and behaviour across applications and infrastructure o Metrics Diagnosis Agent - Analyses threshold-based alerts and identifies anomalies requiring action o Logs Diagnosis Agent - Correlates logs across services to detect root cause patterns o Ticket Diagnosis Agent - Enriches alerts using historical tickets, SOPs, and contextual insights o Ticket Triaging Agent - Automates categorization, prioritization, assignment, and routing • Diagnose & Correlate Layer: This layer combines deterministic and probabilistic AI models o Deterministic Engine - Rule-based triaging, SOP-driven workflows, Policy-based decisioning o Probabilistic Engine - Pattern recognition, RCA using historical trends, AI-driven recommendations o Cross-domain correlation- Links metrics, logs, and incidents, Identifies root cause across distributed systems • Integration Layer: API based integration with ITSM tools (e.g., ServiceNow) & enterprise systems • Dashboard Layer: Real-time visibility into ticket trends, MTTR, FLR, and agent performance for various personas (e.g. CIOs, App Owner, Service Manager) • Underlying Platform Components: RAG pipeline, AI models, vector database, and orchestration engine for continuous learning and scalability.
Azure Services Used: Below is a representative mapping of Azure-native services used to build and deploy the solution:
🔹 AI & Machine Learning- Azure OpenAI Service , Azure Machine Learning Data & Storage- Azure Data Lake Storage (ADLS) , Azure Cosmos DB / Azure SQL Database , Azure AI Search 🔹 Integration & Messaging- Azure Event Hub / Service Bus , Azure Logic Apps, Azure API Management 🔹 Compute & Orchestration- Azure Kubernetes Service (AKS), Azure Functions, Azure Container Apps 🔹 Observability & Monitoring- Azure Monitor, Application Insights