Machine Learning Engine

Upon deployment, our ML models begin by ingesting your real-time and historical packet, flow, and device data (SNMP, etc.). It doesn't just look at averages; it learns the unique "rhythm" of your network—including normal peaks, valleys, and application-specific behaviors—across different times of day, week, and month. This multi-dimensional, constantly evolving baseline is the foundation for all true anomaly detection.

DART AI uses time-series forecasting models to predict traffic volume and link utilization minutes or even hours into the future. When it anticipates impending congestion or latency that would violate your baseline, it automatically calculates and recommends (or applies) an optimal alternate path before performance is impacted. This isn't simple failover; it's intelligent, proactive load management.

When an anomaly is detected, DART AI's ML engine performs real-time correlation across thousands of metrics. It asks: "What else happened at the exact same time?" It automatically clusters related events, differentiating between symptoms (slow application) and the root cause (a specific DNS resolution delay, a failing NIC on a switch, or a misconfigured QoS policy). This turns a flood of alerts into a single, actionable insight with supporting evidence.

By analyzing long-term trends, our models provide accurate, data-driven forecasts for future capacity needs. It can answer critical questions like: "Based on our current growth trajectory, when will we need to upgrade the link to our AWS East region?" or "Which branch offices are most at risk of bandwidth constraints next quarter?" This transforms capacity planning from a reactive exercise into a strategic, budget-friendly process.