How Eyer works
Early intelligence for industrial operations.
Eyer connects to your existing infrastructure, learns the behavioural fingerprint of your operation, detects anomalies before failure becomes visible, and delivers context-rich, actionable alerts. Autonomously.
Autonomous Fingerprint Learning
The platform that knows your normal.
Three dynamic baselines per metric, built from your historical data, not a generic industry average. Each metric gets a lower baseline, a central baseline, and an upper baseline, all learned from how your specific asset actually behaves across operating states, load cycles, seasonal variation, and asset age. No manual configuration. No thresholds to set and maintain. Fully self-adapting.
The fingerprint updates continuously as your operation evolves. A new batch cycle, a repaired asset, a change in raw material: the baselines adapt without intervention. What was normal last quarter and what is normal today are both captured. Eyer handles any time-series data: OT systems, IT infrastructure, IoT devices, or cloud platforms. Data reaches Eyer via Telegraf, Prometheus, a custom agent, or a direct HTTP push, whichever suits your infrastructure.
Mathematical Correlation and Cascade Intelligence
Root cause, not just symptoms.
Eyer maps statistical dependencies across every signal in your operation, not by timestamp proximity, but by actual time-series behaviour. When a deviation appears in one signal, Eyer already knows which other signals are correlated with it and by how much. The result: a single alert that tells you what is primary and what is cascade, before the cascade is visible to any operator.
Traditional alarm management logs four alarms as four events. Eyer shows them as one correlated sequence with one root cause. This is the difference between reactive investigation after failure and proactive intervention before it.
Early, Context-Rich, Actionable Alerts
The right information before the window closes.
An Eyer alert is not a number and a red flag. It is a decision-ready briefing: which signal is deviating, how far from baseline and in which direction, which other signals correlate with it, what the statistical likelihood of continued deviation is, what the likely cause is, and what action is recommended. Delivered before any static threshold would have fired.
The early detection window is the competitive advantage. The salmon producer case detected a water chemistry deviation 2–3 hours before any operational impact was visible. No alarm ever fired. Eyer saw it.
Agentic AI on Your Operational Fingerprint
The intelligence layer your operation has never had.
Three layers of operational knowledge read together: your written knowledge (SOPs, P&IDs, incident reports, maintenance logs) plus your digital fingerprint (learned baselines per metric) plus the live signal. Eyer's MCP server makes this combined context available to any agentic AI workflow. Customers are building maintenance scheduling agents, regulatory compliance agents, and board briefing agents on top today.
The operational audit trail produced by Eyer is timestamped and contextualised per asset. This is the structured operational record that lenders, insurers, and regulators are beginning to require as proof, not assurance.
Non-invasive integration
Connects to your existing infrastructure.
Eyer handles any time-series data from OT systems, IT infrastructure, IoT devices, or cloud platforms. Data reaches Eyer via open-source agents (Telegraf, Prometheus), custom agents, or a direct HTTP push. No new sensors. No direct PLC access. No changes to your existing infrastructure.
Go deeper
algo.eyer.ai →
The Eyer algorithm: how three dynamic baselines are built, how correlation is calculated, and how the early detection window is derived.
docs.eyer.ai →
Technical integration documentation: Telegraf, Prometheus, HTTP push, and custom agent setup for your infrastructure.
Whitepaper →
Your Operation Fingerprint: 18 pages on how Eyer reads industrial time-series data. No fluff.
Eyer vs traditional threshold-based monitoring
A different class of operational intelligence.
| Attribute | Threshold-based monitoring | Eyer |
|---|---|---|
| Baselines | Static, manually set, rarely updated | Three dynamic baselines per metric, self-adapting |
| Alert correlation | Individual alarms fired when a value crosses a line | Correlated sequences: primary signal and cascades identified |
| Alert context | Signal name, value, and threshold crossed | What is deviating, correlated signals, likely cause, recommended action |
| Timing | Fires when the failure is already in progress | Fires before any threshold is crossed, inside the early detection window |
| Knowledge capture | Pattern recognition exists only in experienced operators | Fingerprint encoded from historical data; carries institutional knowledge forward |
| Configuration | Requires ongoing manual threshold maintenance | Fully autonomous: connects, learns, alerts |
Whitepaper
Your Operation Fingerprint
How Eyer reads industrial time-series data, builds three dynamic baselines per metric, maps statistical correlations across signals, and detects anomalies your existing monitoring has never seen. 18 pages. No fluff.
Download the whitepaperHow three dynamic baselines per metric are built from your historical data
What mathematical time-series correlation means for alarm management
The early detection window: what it looks like on real industrial data
How the operational fingerprint carries institutional knowledge forward