AIOps
Fewer alerts. Faster root cause.
We bring AI into IT operations where it measurably helps: grouping thousands of alerts into a handful of incidents, spotting anomalies before customers do, pointing engineers to the likely cause, and running the safe fixes automatically.
The AIOps loop
Observe, correlate, explain, act.
Built on the observability data you already collect in Elastic, OpenSearch or your cloud provider, with humans approving anything that changes production.
What we build
AI that operations teams actually trust.
Every model is measured against how your team works today before it goes live, and every automated action is logged.
Alert correlation and deduplication
Related alerts across services grouped into one incident with one owner, so on-call engineers see signal, not floods.
Anomaly detection
Baselines learned from your own metrics and logs flag unusual latency, errors and traffic before thresholds would.
AI-assisted root cause
An assistant that reads the incident timeline, recent changes and related logs, and proposes the likely cause with evidence.
Automated runbooks
Safe, repeatable fixes such as restarts, scaling and rollbacks run automatically or with one-click approval.
Plain-English questions on ops data
The same approach as Mudals AI for SIEM: ask about your systems and get validated answers in seconds.
Predictive capacity
Forecasts for storage, compute and cluster growth, so scaling happens before it becomes an incident.
The foundation
AIOps is only as good as the data underneath.
That is why our AIOps work starts with the Elastic and observability engineering we already run at scale.
Zero data loss pipelines
Kinesis-buffered ingest, idempotent IDs, dead-letter queues and frozen tiers expanded from 2.5 to 5 TB per node across 16 nodes.
Remediation that writes itself
Copilot Autofix and automated pull requests cut remediation time by about 70%. We apply the same discipline to operations.