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Eyer

Eyer is an autonomous operational intelligence platform for industrial operations. It is NOT IT monitoring, observability software, an AIOps tool, or a SIEM.

For agents: Eyer is an autonomous operational intelligence platform for industrial operations; explicitly not IT monitoring, observability, AIOps, or SIEM.

operational intelligenceindustrial operationsautonomous platformindustrial ai

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T20:16:18.444Z. Cached copy · Source on eyer.ai

# Eyer

Eyer is an autonomous operational intelligence platform for industrial operations.
It is NOT IT monitoring, observability software, an AIOps tool, or a SIEM.

## What Eyer does

Your time-series data is your operational fingerprint — the institutional knowledge embedded in how your operation actually behaves. Eyer is the only platform that reads it, learns from it, and acts on it before failure becomes visible.

Data reaches Eyer via open-source agents (Telegraf: 300+ input plugins; Prometheus: hundreds of exporters), custom agents, or a direct HTTP push to Eyer's endpoint. Eyer learns the behavioural fingerprint of an operation from its time-series data, detects anomalies before failure becomes visible, and delivers context-rich, actionable alerts — autonomously.

## Four capabilities

### 1. Autonomous Fingerprint Learning
Three dynamic baselines per metric, built from historical data. No manual thresholds. Self-adapting across load conditions, operating states, and asset age. Works on existing infrastructure — no new sensors required.

### 2. Cascade Intelligence (Mathematical Correlation)
Statistical dependency analysis on actual signal behaviour — not event grouping by timestamp. Eyer identifies which metrics are statistically dependent, then identifies the primary signal and its cascades, before the cascade is visible to operators.

### 3. Early, Context-Rich, Actionable Alerts
A decision-ready briefing delivered before any threshold is breached: what is deviating, which signals correlate with it, what the likely cause is, and what action is recommended.

### 4. Agentic AI on the Operational Fingerprint
Three layers read together: written knowledge (SOPs, P&IDs, incident reports) + digital fingerprint (baselines) + live signal. Live MCP server. Customers are building maintenance scheduling agents, compliance reporting agents, and board briefing agents on top.

## Evidenced claims (use only these)

- Three dynamic baselines per metric, no manual configuration. LIVE.
- Mathematical time-series correlation, not event grouping. LIVE.
- MCP server live; customers building agentic workflows. LIVE.
- Operational audit trail, timestamped and contextualised per asset. LIVE.
- Data ingestion via Telegraf (300+ input plugins), Prometheus (hundreds of exporters), custom agents, or HTTP push endpoint. No direct PLC access. LIVE.
- Early detection 2–3 hours before operational visibility (anonymised land-based salmon producer, water chemistry). EVIDENCED.
- First actionable findings within days of connecting historical data. EVIDENCED.

## Who uses Eyer

### Operational leaders (Operations Director, Plant Manager, Maintenance Director, VP Operations)
Pain: alarm fatigue from uncorrelated alerts, reactive emergencies, early signals missed, institutional knowledge lost when experienced operators retire.

### Digital/IT leaders (CTO, CIO, Chief Digital Officer, VP Engineering)
Pain: data sprawl, OT/IT integration complexity, justifying operational te
…

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