Key Highlights
Motadata ObserveOps delivers deep, multi-protocol device performance visibility down to the interface level, with AI-driven baselines separating anomalies from expected variation in role-appropriate dashboards.
Monitor device health beyond binary up/down status.
CPU, memory, and temperature monitoring for routers, switches, firewalls, and wireless access points.
Interface-level utilization, error rate, and discard tracking per network port.
Multi-vendor monitoring via ICMP, SNMP, SSH, and vendor APIs for consistent data collection.
Device resource trend analysis for capacity planning and degradation early warning.
Measure the signals that determine user and application experience.
Latency measurement over WAN links, segment paths, and inter-site connections.
Throughput and bandwidth utilization per interface, device, and network segment.
Packet loss monitoring for links and paths where loss affects application performance.
Jitter measurement for voice, video, and latency-sensitive application assurance.
Separate genuine performance problems from expected behavior variation.
Machine learning baselines trained on historical performance patterns per device and interface.
Adaptive threshold adjustment responding to traffic pattern changes and capacity expansions.
Anomaly detection that flags deviations before they breach static alert thresholds.
Cross-device correlation identifying performance problems with shared upstream causes.
Deliver the right performance view to all stakeholder roles.
Executive dashboards with network health scores, SLA adherence, and performance trend summaries.
Operational dashboards for NOC engineers with device-level metrics, active alerts, and heat maps.
Engineering dashboards with interface-level drill-down, protocol statistics, and capacity utilization.
Custom dashboard builder for site-, vendor-, and function-specific performance views.
Identify performance changes that matter, beyond simple threshold crossings.
Continuous comparison of current performance against established baselines per entity.
Deviation severity scoring based on the magnitude and duration of the departure.
Trend deviation detection for sustained drift that never crosses a static threshold.
Proactive alerting on baseline deviations before application or user impact occurs.
Monitor at the port and interface level for precise diagnostic detail.
Per-interface statistics including in/out utilization, error counters, and discard events.
Top-N interface identification by utilization, errors, or discards for fast bottleneck spotting.
Interface performance comparison between similar device types for anomaly context.
Historical interface-level data for post-incident review and recurring problem identification.
Intelligence
Network monitoring that only detects outages misses most conditions that degrade application performance. A WAN link at 95% utilization is up. But the applications traversing it hit packet loss and rising latency that degrade user experience, with no availability event triggering.
Performance Insight monitors the network between binary up and down. It captures the degradation that lives where infrastructure is technically available but operationally insufficient. AI-driven baselines make it visible the moment it deviates from expected behavior, not when it finally crosses a threshold set with no knowledge of what normal looks like.
How It Works
Collect device and interface performance metrics via ICMP, SNMP v2c/v3, SSH, and vendor APIs.
Run protocol-based discovery via SNMP, CDP, LLDP, and routing table queries.
Normalize performance data into Motastore's unified network telemetry model.
Train AI baseline models on historical performance patterns per device and interface.
Apply anomaly detection and adaptive threshold evaluation to incoming performance data.
Trigger performance alerts based on deviation severity and SLA impact assessment.
Render performance dashboards with instant refresh from live network telemetry.
Each device. Each interface. All performance signals, in one intelligent platform.
Role-Based Value
Show that network performance is measured and managed at the level that determines application and user experience.
Show that network performance is measured and managed at the level that determines application and user experience.
Give NOC and network engineering teams a unified monitoring platform covering all devices, interfaces, and segments.
Give NOC and network engineering teams a unified monitoring platform covering all devices, interfaces, and segments.
Receive anomaly-based alerts with device context and baseline comparison data.
Receive anomaly-based alerts with device context and baseline comparison data.
Use interface-level granularity and historical trending to spot the ports, segments, and devices approaching capacity limits.
Use interface-level granularity and historical trending to spot the ports, segments, and devices approaching capacity limits.
From Visibility to Control
45% faster root-cause identification through cross-domain correlation and AI-driven anomaly detection.
Proactive degradation detection through baseline monitoring before threshold-based alerts fire.
Interface-level granularity enabling precise bottleneck identification in complex topologies.
AI adaptive thresholds reducing false positive alerts from expected traffic variation.
Tailored dashboards delivering the right performance context to each operational role.
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