HEAL is an AI-driven IT operations platform. It predicts, finds, and prevents application and infrastructure problems before users feel the impact.

What HEAL does

• Spots early warning signs before they turn into incidents.
• Runs automatic root cause analysis to cut manual triage time.
• Triggers self-healing actions to reduce on-call work.
• Catches issues that simple threshold-based monitoring misses.

How it works

Workload-behavior correlation

HEAL learns the normal link between two things.

• Workload. The volume, mix, and payload of incoming requests.
• Behavior. How the system responds. Latency, errors, and resource use.

When that link breaks, HEAL flags it as a signal.

Machine Learning Engine (MLE)

The MLE produces four output types.

• Events. Raw observations from monitoring data.
• Early warnings. Leading signs of an upcoming issue.
• Incidents. Confirmed problems that need action.
• Signals. Linked patterns across the stack.

Situational awareness

HEAL adds context that other tools miss.

• Seasonal patterns (daily, weekly, monthly).
• Rollbacks and deployments.
• Resource contention.
• Transaction-level dependencies.

Preventive healing

HEAL finds the root cause, picks a fix, and runs the action. It can run on its own, or wait for an operator to approve the action.

Data flow

1. Collect. HEAL agents gather workload, performance, and infrastructure data.
2. Correlate. The MLE links workload patterns to system behavior.
3. Detect. Anomalies show up as early warnings or incidents.
4. Diagnose. Root cause analysis points to the failing component.
5. Heal. Automatic or operator-approved actions are triggered.
6. Report. Dashboards show health, trends, and outcomes.