AIOps & Intelligent Operations
Operational noise slows engineering teams down. We deploy and operate AIOps platforms that suppress alert floods, correlate events across your entire technology stack and surface only the alerts that need action, reducing MTTD by up to 80% and letting your teams work on real problems instead of chasing false positives.
What we deliver
ML-based event grouping that correlates thousands of raw alerts into a handful of actionable incidents. We configure topology-aware correlation rules so downstream alert storms don't mask the upstream failure that caused them.
Dynamic baselining of key performance metrics using time-series ML models. We detect deviations before they breach thresholds, alerting on trend changes, behavioral anomalies, and seasonal pattern breaks that rule-based monitoring misses.
Automated discovery and maintenance of service dependency maps that capture upstream and downstream relationships, so when a database slows down, the AIOps system immediately knows which applications and users are affected.
Pre-approved automated remediation playbooks that run directly from correlated incidents to restart services, clear queues, trigger failovers or scale resources before an engineer needs to be paged.
Executive and operational dashboards that translate raw telemetry into business impact metrics: services at risk, SLA trajectory, MTTD/MTTR trends, and cost-of-incident analytics, all updated in real time.
AIOps models decay without tuning. We continuously refine correlation rules, retrain anomaly detection models, and incorporate feedback from incident postmortems, so accuracy improves over time instead of degrading.
How it works
Every engagement follows the same five steps: baseline the current state, design the target model, roll out in stages, operate it, and improve against measurements.
Assess
Baseline the current state, name the gaps and put the success criteria in writing.
Design
Architect the target operating model and the toolchain it needs.
Deploy
Implement, configure and validate in a staged rollout.
Operate
24/7 management with contracted response times and proactive monitoring.
Improve
Continuous improvement driven by metrics, incidents and changes in the business.
Every engagement runs under a written SLA: a commitment, not a best-effort promise.
Dedicated engineers who know your stack. No generalist help-desk tier in between.
Service reviews every two weeks, roadmap updates every quarter.
Knowledge Hub
What we have written about running and managing technology, collected in one place.
01Does AIOps actually work, or is it marketing?
It can be either. The real benefit shows up in two places: correlating similar alerts into a single incident, and finding patterns across recurring incidents. Beyond those, products promising concrete gains deserve caution.
02How much data is needed?
Usually three to six months of history for a meaningful baseline. Models built on less treat seasonal patterns (month-end, campaign periods) as anomalies and generate false alerts.
03Is automated remediation safe?
Within a narrow scope and for reversible actions, yes: service restart, disk cleanup, scaling. Irreversible actions are not automated. Every automated action is logged and reviewed.
04Does it replace our existing monitoring investment?
No, it builds on it. AIOps does not produce data; it processes the data you already have. Where monitoring coverage has gaps, AIOps does not close them: the collection layer has to be complete first.
05How do we measure the result?
Reduction in alert volume, mean time to detect and resolve per incident, and the recurring incident rate. Unless those three are measured before deployment, no improvement can be shown afterward.
Let's work out where to start
Within two weeks you get it in writing: what works, what carries risk, and a prioritized roadmap.
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