Industry · IT Integration Environments
Your integration flows degrade gradually. Alerts arrive after the impact.
Message throughput, API latency, queue depth, pipeline error rates: these signals appear in your time-series data before any downstream system registers a problem. Eyer learns to read that fingerprint and surfaces deviations early.
Live customer: global industrial group, IT integration environment
A global industrial group runs Eyer on the time-series metrics from their IT integration environment: throughput across message queues, API gateway latency, and data pipeline flow rates. Eyer learned the fingerprint of normal integration behaviour from historical data and now surfaces correlation patterns across connected systems before business operations are affected.
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The IT integration challenge
High-volume flows. Complex dependencies. Gradual degradation.
Silent degradation
Message queues, API pipelines, and ETL flows rarely fail instantly. Throughput drifts, latency climbs, and error rates creep upward — each individually sub-threshold, collectively a pattern that predicts failure.
Cross-system correlation
When an integration pipeline degrades, the root cause is typically upstream. Understanding which system triggered the cascade requires correlating metrics across platforms — Eyer does this mathematically on the actual signal behaviour.
No operational baseline
Integration environments are dynamic: batches run on schedules, volumes shift with business cycles, behaviour changes after every release. Static thresholds cannot follow these patterns. Eyer learns three dynamic baselines per metric, self-adapting with no manual configuration.
How Eyer applies to IT integration environments
Fingerprint learning on integration flows, pipeline metrics, and message data.
Eyer handles any time-series data from integration platforms, API gateways, message brokers, or ETL systems. Data reaches Eyer via InfluxDB, Prometheus, Telegraf, or a direct push — no changes to your integration architecture. Eyer learns what normal looks like across all your flows, correlates related signals mathematically, and surfaces anomalies before downstream systems are impacted.