Eyer
Eyer is an AI-powered observability and AIOps platform, designed to integrate seamlessly into existing technology stacks via APIs. The key function of Eyer is a...
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What is Eyer?
Eyer is a headless AI anomaly detection platform for time series data that autonomously learns what normal behavior looks like across every metric you ingest, without requiring data scientists, threshold configuration, or rules to write. The platform detects deviations and correlates signals across your entire stack before alerting, so your team receives actionable, context-rich alerts with root cause direction instead of raw noise.
Key features include autonomous baseline learning that updates continuously as environments change, a correlation engine that maps relationships between systems and metrics based on behavioral patterns, API-first ingestion supporting any time series data, and native integrations with Boomi, Prometheus, InfluxDB Telegraf, SCADA historians, and REST APIs. Eyer delivers alerts via Slack, SMS, or webhook with affected systems identified and root cause context included.
Eyer is designed for operations teams running complex, data-rich environments including IT Operations, DevOps, SRE, OT and manufacturing facilities, Boomi integration monitoring, and aquaculture operations (RAS, sea cage, CCS). The same AI engine applies across all verticals, configured for the data and failure modes relevant to each context. It is cloud-hosted and integrates into existing infrastructure without migration or rip-and-replace, delivering results from data connection to actionable anomalies in under a week.
Eyer pricing
Pricing model: Freemium
Pricing is scoped to the deployment based on the number of metrics, environments, and support requirements. A 4-week free trial is available with no credit card required. Contact the company for specific pricing. The starter plan is $199 per month with 21 days free trial, including access plus 1000 time series metrics and online support.
Eyer pros
- 85% reduction in alert noise
- No data scientists required — fully autonomous
- No threshold configuration needed
- No rules to write
- 3:1 ROI within 180 days
- Correlates signals across entire stack before alerting
- Root cause context included with every alert
- Maps affected system chain automatically
- Detects equipment drift before failure
- Monitors Boomi integration health autonomously
- Detects early mortality signals in aquaculture
- Correlates water quality, feed, and sensor data
- Cloud-hosted and headless architecture
- API-first with open API ingestion
- Integrates without migration or rip-and-replace
- Alerts routed via Slack, SMS, or webhook
- Baselines update continuously as environment changes
- Works across IT, OT, and integration environments
- Proof of value with historical data before commitment
- Fast deployment — actionable anomalies in under a week
Eyer cons
- Cloud-hosted only — no on-premise option
- Requires 3-12 months historical data for proof of value
- Pricing scoped per deployment (not transparent)
- Partner-first GTM may limit direct support
- Limited developer test accounts available
- No proprietary sensors provided
- Primarily focused on time series data only
- Requires existing data sources with API export
Frequently asked questions about Eyer
What is Eyer?
Eyer is a headless AI anomaly detection platform for time series data. It autonomously learns normal behaviour across every metric you ingest — across IT operations, manufacturing, OT environments, and aquaculture — and alerts your team only when something meaningful deviates. No data scientists required.
What is anomaly detection?
Anomaly detection is the automated identification of abnormal patterns in time series data. Eyer's AI builds dynamic baselines for every connected metric and detects deviations — correlating signals across your environment before alerting, so your team receives root cause context rather than raw noise.
How does Eyer's correlation engine work?
Eyer's correlation engine maps relationships between systems and metrics based on behavioural patterns. When an anomaly is detected, the topology identifies which upstream and downstream components are affected — so your team acts on cause, not symptom.
What data sources does Eyer support?
Eyer ingests any time series data via open APIs. Native integrations include Boomi, Prometheus, InfluxDB Telegraf, SCALA historians, and REST APIs. If a system exports time series data, Eyer can ingest it. No proprietary sensors or infrastructure changes required.
Where does Eyer deploy?
Eyer is cloud-hosted and headless. It operates across IT operations, DevOps/SRE, OT and manufacturing environments, Boomi integration monitoring, and aquaculture (RAS, sea cage, CCS). The same AI engine applies across all verticals — configured for the data and failure modes relevant to each context.
How do I get started with Eyer?
The fastest path is a historical data analysis. Share 3–12 months of time series data from one environment — Eyer surfaces the anomalies, drift events, and correlations present in your data, then presents findings in a 30-minute call. If the value is clear, we scope a live 30-day POV with defined success criteria.
What is the pricing model?
Pricing is scoped to the deployment — based on the number of metrics, environments, and support requirements. A 4-week free trial is available with no credit card required. Contact the company for specific pricing.
Does Eyer offer partnerships?
Yes. Eyer operates a partner-first GTM model. Technology and channel partners can embed the Eyer platform in their own service offerings.
Is there a developer test account available?
Yes. Reach out to support. A limited number of test accounts are available for developers actively contributing to the community.
What are the customer outcomes with Eyer?
Customers report 85% reduction in alert noise, 3:1 ROI within 180 days, and zero data scientists required. One Head of Enterprise Integration at a Large Manufacturing Conglomerate said they now have deep, actionable insight and proactive alerting while delivering more with significantly reduced monitoring overhead. An Enterprise Integration Architect at a Major US Retail Corporation noted the ability to understand the rippling effect from disk operations to customer experience is transformational.