Eyer's AI learns the normal operating signature of every sensor, process, and system in your OT environment — and alerts your team the moment something starts to drift. Before the line stops. Before the shipment is missed. Before the customer calls.
Modern manufacturing generates enormous volumes of sensor and system data across SCADA, MES, ERP, and WMS. The monitoring tools most teams rely on fire on failure — not on drift. By the time an alert triggers, the cost is already incurred.
Equipment performance, fill rates, throughput, and cycle times drift gradually before failure. Threshold-based monitoring misses the early signal entirely — your team inherits a firefight instead of a warning.
OT environments produce high-frequency sensor streams across dozens or hundreds of assets. Most alerts are noise. The signal that matters — a process starting to behave differently — gets buried.
When production stops, your team faces a diagnostic challenge across MES, SCADA, ERP, and supplier systems simultaneously. Without correlation context, root cause isolation takes hours — each minute of downtime carrying significant cost.
Integration failures between ERP, WMS, and supplier portals go undetected until a material delivery is missed or a customer order stalls. By then, the operational and commercial damage is done.
Eyer's AI builds dynamic baselines across every OT metric — sensor readings, process throughput, equipment cycle times, integration latency. Baselines update automatically as your environment evolves. No rules. No threshold configuration. No data scientists required.
Early drift detection gives your team time to act before failure. Correlated alerts direct attention to the root cause — not the symptom.
Eyer correlates signals across systems before alerting. One context-rich alert per incident — not hundreds of disconnected sensor events.
The Eyer collector connects to OPC-UA, Modbus, REST APIs, and data historians. No changes to existing OT architecture required.
Eyer learns your operational baselines in approximately one week. Actionable anomalies surface in the second week.
Eyer ingests sensor streams, equipment telemetry, and system integration data via OPC-UA, Modbus, REST, or data historian. No changes to existing PLC or SCADA configuration.
Eyer's models learn normal behaviour across every asset and process. Baselines account for production schedules, shift patterns, seasonal variation, and equipment-specific behaviour — no manual configuration.
When a process starts to drift, Eyer's correlation engine maps the downstream impact chain before alerting. Your operations team receives a single, context-rich alert with root cause direction via Slack, SMS, or webhook.
Eyer ships a Model Context Protocol server, enabling anomaly and correlation output to be consumed directly by any LLM or AI agent. Connect to maintenance runbooks, equipment documentation, and escalation workflows to enable autonomous investigation and further reduce MTTR.
Eyer's intelligent monitoring is fundamentally changing our approach to what is feasible. Having a conversation with a GenAI-powered interface that makes sense of performance issues across our production environment is transformational.
Operations Manager, Major US Manufacturing CompanyBefore Eyer, we had virtually no insight into key workloads. Now we have deep, actionable insight and proactive alerting — we're meeting SLAs with confidence while delivering more with significantly reduced monitoring overhead.
Head of Enterprise Integration, Large Manufacturing ConglomerateBefore connecting to a live environment, we run Eyer against your historical sensor and operational data and show you what your current tools missed — drift events, anomaly patterns, and correlated failures that preceded downtime or quality issues. No infrastructure changes. No commitment required.
We respond within 2 business days. No infrastructure changes required.