01
Observe
Ingest telemetry from the environments you connect.
Infrastructure Intelligence
AI-Powered Infrastructure.
From Detection to Resolution.
Trego-AI continuously understands your cloud environment, detects operational risk, recommends controlled remediation, and learns from every incident.
Operational lifecycle
Observe. Understand. Predict. Remediate. Learn.
A continuous loop — not a dashboard of disconnected tools.
02
Understand
Correlate signals into operational meaning.
03
Predict
Surface risk when enough history exists to support it.
04
Remediate
Propose, policy-check, approve, then execute.
05
Learn
Persist outcomes to improve the next recommendation.
Your infrastructure tells you what’s wrong. Trego-AI tells you what to do next.
Platform
Three capabilities. One control path.
01
Infrastructure Intelligence
Understand infrastructure health, dependencies, anomalies and recurring operational patterns — from resources that have actually been discovered.
02
Controlled Remediation
AI proposes remediation. Policy evaluates it. A human approves where required. Trego-AI executes and records the result.
03
Continuous Learning
Historical incidents and remediation outcomes improve future recommendations. No history means no invented score.
Intelligence engine
Powered by NeuralOps
NeuralOps is the intelligence and remediation engine behind Trego-AI — not a side feature.
Signals in
Azure
Datadog
Metrics
Logs
NEURALOPS
- Correlate
- Understand
- Predict
- Recommend
- Learn
Control out
Policy engine
Approval gate
Remediation
Integrations
Connected where the product actually is.
Microsoft Azure
App Service, Automation, Logic Apps, App Configuration
Datadog
Monitor webhooks into the remediation workflow
AWS
Not implemented in this product yet
Security
Control before execution.
Remediation is not a public action. Policy evaluates every proposal. Human approval is required where you configure it. There is no anonymous self-registration and no public trial onboarding.
Insights
Numbers only when they are earned.
Success rates, MTTR and resource counts appear in the product only when they are calculated from connected telemetry or persisted Trego-AI execution records. If history is insufficient, the product says so.