Eyer's AI learns what normal looks like across your entire environment — infrastructure, services, integrations, and business KPIs — and alerts your team only when something meaningful deviates. No threshold configuration. No rules to write. No data scientists required.
SRE and DevOps teams running complex environments face the same failure mode: too many alerts, too little context, too much time spent triaging noise instead of resolving root cause. Static thresholds fire constantly on non-events. The signals that matter — gradual service degradation, correlated failure chains, subtle infrastructure drift — arrive below the threshold and go unnoticed until impact.
Threshold-based systems generate constant false positives. Teams habituate to ignoring alerts — including the ones that matter.
When a real incident fires, the alert tells you something is wrong. It doesn't tell you what caused it, what's affected downstream, or where to start.
Service degradation that occurs over hours or days never triggers a threshold alarm. By the time it's visible, the impact is already in production.
Infrastructure metrics, application traces, integration health, and business KPIs live in separate tools. Correlating across them manually during an incident is slow and error-prone.
Eyer ingests from Prometheus, Telegraf, REST APIs, and any time series source. No migration, no changes to existing infrastructure, no proprietary agents.
Eyer's models learn normal behaviour across every connected metric. Baselines update continuously as your environment changes — no manual reconfiguration required.
When something deviates, Eyer's correlation engine maps the affected system chain before alerting. Your team receives a single alert with root cause direction and downstream impact already identified — routed via Slack, SMS, or webhook.
Eyer ships a Model Context Protocol server, making it straightforward to connect Eyer's anomaly detection and correlation output to any LLM or AI agent. Enrich with your own context — runbooks, escalation paths, deployment logs, knowledge bases — to reduce MTTR further and enable autonomous investigation workflows.
Eyer is headless and API-first. If a system exports time series data, Eyer can ingest it. No rip-and-replace. No proprietary sensors.
Prometheus, Telegraf, CloudWatch, Datadog — Eyer adds an anomaly detection layer on top of what you already have.
Response times, error rates, throughput, queue depths — across every service in your stack.
Data flow anomalies, latency drift, and silent failures in integration layers before downstream systems are affected.
Eyer correlates technical signals with business metrics — so a drop in order throughput is connected to the infrastructure event that caused it.
Before 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 ConglomerateThe ability to easily understand the rippling effect from a disk operation issue on a local database to how it impacts customer experience in our retail stores is transformational.
Enterprise Integration Architect, Major US Retail CorporationBefore committing to a live integration, we run Eyer against your historical data and show you the anomalies your current tools missed — with timestamps, correlated signals, and operational context. If we find nothing meaningful, we tell you directly.
We respond within 2 business days. No commitment required.