Vitria VIA AIOps is an AI‑driven operations platform designed to improve service assurance, incident detection, and automated remediation across complex IT and network environments. It ingests logs, metrics, events, and traces from multiple sources, correlates them using machine learning and knowledge‑graph‑based context, and derives root causes of issues with high accuracy. The platform supports large‑scale, multi‑vendor, cloud‑native environments and enables autonomous operations such as self‑healing, dynamic traffic rerouting, and configuration rollbacks. VIA AIOps reduces noise, prioritizes incidents, improves mean‑time‑to‑detect and mean‑time‑to‑repair, and helps organizations transition toward fully autonomous operational models.

Key Features

Knowledge‑Graph‑Driven Intelligence Uses a structured knowledge graph to enhance prediction quality, correlation, and decision‑making.

• Provides contextual understanding across services and layers
• Shares learned knowledge across AI agents

Noise Reduction & Intelligent Correlation Reduces alarm noise and correlates alerts across services and topologies for accurate root cause identification.

• Up to 99% alarm noise reduction out of the box
• Identifies probable cause vs. symptom

Automated Root Cause Analysis Processes large volumes of data to pinpoint underlying issues quickly.

• Shortens troubleshooting time
• Provides clear, actionable insights

AI‑Driven Remediation & Likely Fix Suggestions Leverages NLP and ML models to detect solutions from logs and documentation.

• Suggests or executes remediation steps
• Lowers mean‑time‑to‑repair

Full‑Stack Observability Unifies monitoring across infrastructure, applications, and networks.

• Enables fault, performance, and change management
• Supports high‑volume streaming data environments

Autonomous Knowledge Acquisition AI agents autonomously build and update the knowledge graph.

• Reduces manual effort
• Continuously improves operational accuracy

Benefits

Faster Incident Detection & Resolution Improves mean‑time‑to‑detect and mean‑time‑to‑repair through advanced analytics and automation.

• Reduces operator workload
• Accelerates service restoration

Operational Efficiency Through Automation Automates triage, remediation, configuration tasks, and noise suppression.

• Reduces human error
• Frees teams from repetitive tasks

Improved Accuracy with Contextual AI Knowledge‑graph‑enhanced AI agents provide more precise predictions and correlations.

• More reliable root cause analysis
• Context‑aware decision support

Scalable for Enterprise & Telecom Environments Handles massive, distributed data volumes from millions of devices.

• Supports multi‑vendor, multi‑domain environments
• Adapts to complex IT and network infrastructures