Ops
Your AI Stack, Always Healthy.
Monitoring. Alerting.
Incident Response. Automated.
Operational intelligence agent for your on-premise AI infrastructure. GPU health, model performance, inference latency, auto-scaling, predictive maintenance. The agent that keeps all your other agents running.
Six Layers of Infrastructure Intelligence
From GPU temperature to model drift — Ops monitors, predicts, and responds before problems reach your users.
GPU Health Monitoring
Real-time tracking of GPU temperature, memory utilization, power draw, and clock speeds. Alerts before thermal throttling or memory exhaustion impacts your inference workloads.
Model Performance
Track accuracy, drift, latency percentiles, and throughput for every deployed model. Detect degradation trends before they affect production quality.
Auto-Scaling
Intelligent scaling based on inference queue depth, GPU utilization, and request patterns. Scale up before demand spikes, scale down to save resources.
Predictive Maintenance
ML models trained on your infrastructure patterns forecast GPU failures, memory exhaustion, and capacity bottlenecks 24–72 hours before they impact production.
Incident Response
Automated detection, diagnosis, and remediation. Most incidents resolved before your team is aware. Configurable escalation thresholds and runbook automation.
Dashboard & Reporting
Unified operational dashboard with real-time metrics, historical trends, SLA tracking, and automated weekly reports. Know your infrastructure health at a glance.
Detect → Diagnose → Respond → Learn
Every incident follows a 5-stage intelligence pipeline. Most are resolved automatically before your team is even aware.
Anomaly identified
Root cause analysis
Auto-remediate or escalate
Confirm resolution
Update runbooks
Ops Keeps Your Stack Running So You Don’t Have To
Four core operational workflows, each fully automated with configurable escalation thresholds.
Daily Operations
Automated health checks every minute
- →GPU temperature and utilization monitoring
- →Inference queue depth and latency tracking
- →Model accuracy and drift detection
- →Disk space and memory usage alerts
- →Automated log rotation and cleanup
Capacity Planning
Predict before you provision
- →GPU utilization trend analysis
- →Inference demand forecasting
- →Storage growth projection
- →Network bandwidth planning
- →Cost optimization recommendations
Incident Management
Detect, respond, resolve automatically
- →Anomaly detection across all metrics
- →Automated root cause analysis
- →Self-healing remediation scripts
- →Escalation to on-call engineers
- →Post-incident reports with timelines
Model Lifecycle
From deployment to retirement
- →Deployment health verification
- →A/B testing metrics collection
- →Performance degradation alerts
- →Automated model rollback triggers
- →Version tracking and audit trails
DIY Monitoring vs ATC Ops
Grafana dashboards and manual SSH checks catch problems after the fact. Ops prevents them.
Manual / DIY Monitoring
- ✕Static alert thresholds with constant false alarms
- ✕Manual SSH into servers to diagnose issues
- ✕No correlation between GPU, model, and infra metrics
- ✕Reactive — you find out after users complain
- ✕No predictive capability for hardware failures
- ✕Runbooks in wikis that nobody reads at 3 AM
- ✕Hours of engineer time per incident
ATC Ops Intelligent Monitoring
- ✓Adaptive thresholds that learn your infrastructure patterns
- ✓Automated root cause analysis in seconds
- ✓Unified view of GPU, model, and system health
- ✓Proactive — issues resolved before users notice
- ✓Predictive maintenance with 24–72h failure forecasting
- ✓Automated runbooks that execute themselves
- ✓Most incidents auto-resolved in under 60 seconds
Built on Proven Infrastructure Tools
Industry-standard monitoring plus AI-powered intelligence layer. All on-premise.
Monitor Your AI Stack
Already running on-premise AI? Deploy Ops to monitor, alert, and maintain your stack. Setup in hours, value in days.
Your Data. Your Premises. Your AI.