Proactive AI Alerts

Stop reacting to downtime. RobotOps uses Agentic AI and Predictive Analytics to detect silent degradation, explain root causes, and trigger automated recovery workflows before operations stop.

Alert types

Predictive Anomalies: Z-score deviations in motor current, vibration, or battery temp that signal future failure.
AI-Detected Risks: Agentic analysis of "silent" issues like gradual throughput decline or micro-stoppages.
Mission Lifecycle: Alerts for stalled, failed, or excessively long missions across any vendor.
Safety & E-Stop: Instant notification of safety violations or emergency stops with camera context.
Threshold Violations: Classic rule-based alerts for battery < 20%, wifi < -80dBm, etc.

Severity levels

Critical

Immediate downtime risk. AI triggers auto-escalation.

High

Throughput impacted. Technician dispatch recommended.

Medium

Degradation detected. Schedule maintenance soon.

Low

Minor deviation. Logged for long-term trending.

The "Self-Healing" Workflow

1. Predictive Detection

Telemetry feeds our baseline engine. AI detects a motor current spike on Robot 4 that matches a "bearing wear" pattern.

2. Agentic Enrichment

The AI Agent investigates: checks recent maintenance logs, correlates with map zones, and cites the manufacturer manual.

3. Automated Action

If confident, the system triggers a Rule: "Mark robot 'Maintenance Required' and create Jira ticket #492."

4. Resolution & Learning

Technician confirms the fix. The AI learns from this feedback loop to improve future predictions.