Monitoring and
Alerting
Continuous monitoring of every operator, device, and autonomous agent in your environment. The platform evaluates behavioural patterns in real time, detects anomalies through AI-driven baselines, and executes automated responses through configurable triggers.
Real-time alerts and warnings
Every entity is monitored against configurable thresholds. When conditions are breached, alerts are classified by severity and routed through the appropriate notification channels. Critical events trigger immediate escalation.
Operating outside designated zone for 4m 12s
Throughput 62% below hourly baseline
Access pattern deviation detected in Sector 7
Battery level below 15%, charging station occupied
Custom triggers and actions
Define rules that bind specific conditions to automated responses. When a trigger fires, whether from a sensor reading, a geofence breach, or an access anomaly, the platform executes the associated action sequence without manual intervention.
Automatic deviance detection
AI models trained on operational baselines continuously evaluate system behaviour and flag deviations. When an entity acts outside its established norms, the platform generates a deviance report with severity classification, probable cause analysis, and recommended remediation.
Unlike rule-based alerting, deviance detection identifies anomalies that no human would think to write a threshold for. The AI learns what normal looks like and flags everything else.
What Miradoris enables
Real-time behaviour tracking
Every operator, humanoid, device, and environmental sensor is monitored against established behavioural baselines in real time.
Configurable severity levels
Define thresholds for Info, Warning, and Critical alerts per entity type. Escalation rules route alerts to the appropriate response channel.
Geofence and zone monitoring
Track entity positions relative to defined zones. Receive alerts when entities enter restricted areas or leave designated operating zones.
Baseline learning
AI builds behavioural profiles for each entity, process, and environment over time. Profiles adapt as operational patterns evolve.
Anomaly scoring
Every deviation is scored by severity with confidence intervals. High-confidence anomalies trigger automatic responses, low-confidence events are queued for review.
Root cause correlation
AI correlates deviations with system state, environmental conditions, and recent changes to identify probable root causes automatically.
Approach analysis
Manual Monitoring
Traditional SCADA Alerts
Miradoris
RecommendedFrequently asked questions
What types of entities can be monitored?
Any entity modeled in the Miradoris ontology can be monitored: humanoids, AGVs, conveyors, sensors, operators, environmental systems, and custom device types. Each entity type has configurable behavioural baselines and alert thresholds.
How does AI deviance detection differ from threshold alerts?
Threshold alerts fire when a single value crosses a predefined limit. AI deviance detection learns normal behavioural patterns across multiple dimensions and flags deviations that may not breach any individual threshold but represent abnormal composite behaviour.
Can triggers execute actions on physical systems?
Yes. Trigger actions can send commands to PLCs via OPC UA, dispatch humanoid instructions via ROS 2 or REST, adjust device parameters via MQTT, and activate safety protocols. Actions are logged with full audit context.
How are notification channels configured?
Channels are configured per alert severity and entity type. A critical alert on a safety system might route to SMS and PagerDuty, while an informational pattern deviation routes to the dashboard only. Routing rules are managed through the platform interface.
How quickly are anomalies detected?
Behavioural evaluations run continuously against streaming data. Detection latency depends on the metric type: real-time sensor anomalies are flagged within seconds, while trend-based deviations require sufficient observation windows for statistical confidence.
Can monitoring rules be tested before deployment?
Yes. Triggers can be set to a Testing state where they evaluate conditions and log results without executing actions. This allows validation against live data before arming the rule for production response.
Be among the first
We are looking for partners willing to test Miradoris in real operational environments. Early adopters get priority access to the platform at significantly reduced rates.
We'll review your request and follow up. No unsolicited contact.