Analytics
Cross-domain business intelligence with predictive analytics, NL queries, and automated reporting
Analytics is the intelligence layer that spans all zones — aggregating data from every operational domain into actionable insights. From churn prediction to ticket volume forecasting, DevOps AI delivers predictive intelligence that helps MSPs make data-driven decisions.
What You'll See
Real screens from the DevOps AI control plane — populated with representative data.
8 Process Areas
Each process area is classified with a Human-in-the-Loop (HITL) gate level — defining the boundary between AI autonomy and human oversight. Click any process area for a deep dive.
QBR Aggregation Engine
L1 — NotifyAutomated compilation of quarterly business review data from all zones — service delivery, security posture, compliance status, and recommendations.
Deep Dive →Churn Prediction Model
L1 — NotifyML-driven churn risk scoring using engagement patterns, ticket sentiment, payment behavior, and service utilization signals.
Deep Dive →Ticket Volume Forecasting
L0 — Fully AutomatedPredictive models for ticket volume by client, category, and time period — enabling proactive staffing and resource allocation.
Deep Dive →Security Risk Scoring
L0 — Fully AutomatedComposite security risk scores per client combining vulnerability data, compliance gaps, threat exposure, and control effectiveness.
Deep Dive →Natural Language BI Queries
L0 — Fully AutomatedAsk questions in plain English — 'Which clients had the most P1 tickets last quarter?' — and get instant, accurate answers with visualizations.
Deep Dive →Automated Report Generation
L1 — NotifyScheduled and ad-hoc report generation with customizable templates, data source selection, and distribution management.
Deep Dive →Cross-Domain Correlation
L0 — Fully AutomatedAI-powered pattern detection across zones — correlating security events with ticket spikes, compliance gaps with service issues, and more.
Deep Dive →Benchmark Intelligence
L0 — Fully AutomatedAnonymous peer benchmarking across the DevOps AI platform — how does this client compare to similar organizations on key metrics?
Deep Dive →Understanding HITL Gate Levels
Every process area in DevOps AI is classified by its Human-in-the-Loop (HITL) gate level — defining when AI acts autonomously and when human approval is required.
AI executes autonomously with full logging. No human approval needed. Examples: ticket classification, monitoring alerts, report generation.
AI executes and notifies the assigned human. Human can review, override, or escalate after the fact. Examples: SLA predictions, patch scheduling.
AI prepares and recommends, but a human must explicitly approve before execution. Examples: change requests, contract modifications, campaign launches.
Humans perform the action with AI providing decision support only. Examples: legal review, privileged access approval, incident legal response.
Who Uses Analytics?
See how Analytics transforms daily operations for these roles.
Works With
Analytics integrates deeply with these operational zones.