Detect. Diagnose. Fix. The AI Reliability Engineer.

Your infra detects
its own failures.

ReliaOps detects anomalies before alerts fire, diagnoses root cause in minutes, and remediates automatically — while your team sleeps.

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~8 min

Lead time before incident fires

70%+

Incidents targeted for auto-resolution

Class C irreversible actions auto-executed

≥92%

Confidence threshold to auto-act

EMR pipeline · memory anomaly detected 94% conf · auto-scaled 4 min before OOM

K8s pod CPU trending +12%/min · anomaly detected · restarted 1 of 8 pods

RDS read replica lag · detected 6 min early · promoted replica before failover

Airflow DAG backlog growing · scale-out suggested · approved in Slack · done

API error rate p99 +340ms · anomaly detected · correlated to bad deploy · rollback proposed

EMR pipeline · memory anomaly detected 94% conf · auto-scaled 4 min before OOM

K8s pod CPU trending +12%/min · anomaly detected · restarted 1 of 8 pods

RDS read replica lag · detected 6 min early · promoted replica before failover

Airflow DAG backlog growing · scale-out suggested · approved in Slack · done

API error rate p99 +340ms · anomaly detected · correlated to bad deploy · rollback proposed

How it works

Detect. Diagnose. Fix.
In that order.

Most tools react after the incident. ReliaOps runs the full loop before your on-call engineer is even paged.

🔍

Detect

Continuous telemetry analysis across 4 detection layers. Identifies anomalies with confidence score and lead time — minutes before threshold breach.

Statistical anomaly detection (L1)
Per-service signal analysis (L2)
Historical pattern recognition (L3)
Cross-signal correlation (L4)

🧠

Diagnose

Correlation engine maps the signal to root cause. RAG over your runbooks surfaces the relevant fix. LLM generates a human-readable explanation.

Multi-signal correlation
BM25 + embedding RAG over runbooks
Plain English RCA summary
Confidence-scored action proposals

⚡

Fix

Six-gate safety layer vets every action. Class A executes automatically. Class B requires Slack approval. Class C always needs a human — no exceptions.

Reversibility classification (A/B/C)
Blast-radius hard cap
Auto-rollback if metrics worsen
Full audit trail, SOC 2 ready

Use cases

Incidents we're built
to prevent.

Real failure patterns from production systems — and how ReliaOps responds.

⚡

EMR Pipeline OOM

Data engineering · AWS EMR / Spark

T−12mDETECTMemory trending +340MB/min. OOM forecast in 11 min, confidence 91%.

T−10mDIAGNOSERoot cause: partition skew on join — 3 tasks consuming 80% executor memory.

T−9mGATEAction proposed: scale executor memory 32GB→48GB. Class A. Reversible.

T−8mFIXAuto-approved. EMR config updated. Spark reconfigured. Pipeline continues.

✓ Pipeline completed on time. Zero manual intervention. MTTR: 0.

☸️

Kubernetes Cascade

Platform engineering · EKS / GKE

T−6mDETECTCPU +12%/min across 3 pods. Pattern matches pre-cascade fingerprint (conf 88%).

T−5mDIAGNOSEBad deploy 14 min ago. New container version leaking goroutines. Correlated across 3 signals.

T−4mGATEHelm rollback to previous version proposed. Blast radius: 3 pods of 24. Class A.

T−3mFIXSlack approve → Rollback executed. All pods healthy within 90 seconds.

✓ No customer-facing impact. Cascade prevented. Team notified post-hoc.

🗄️

RDS Replica Lag

Backend services · AWS RDS Aurora

T−8mDETECTReplica lag growing 200ms/min. Write storm from batch job detected. Failover risk in ~7 min.

T−7mDIAGNOSEBatch job running full-table scans. Cross-correlated with CloudWatch IO metrics.

T−6mGATEThrottle batch job write rate + promote read replica. Two-step Class A action.

T−5mFIXAuto-executed. Lag reduced to 18ms within 2 min. Batch job resumed at safe rate.

✓ Zero failover. Batch completed 14 min later. No SLA breach.

🔄

Airflow DAG Backlog

ML pipelines · Apache Airflow / MWAA

T−15mDETECTTask queue depth +340 tasks/min. Worker pool saturation in ~13 min.

T−13mDIAGNOSEThree heavyweight ML training DAGs triggered simultaneously by upstream data arrival.

T−12mGATEScale Celery workers 4→8. Suggest-mode: posted to #platform-alerts for approval.

T−11mFIXApproved via Slack button. Workers scaled. All DAGs completed within SLA.

✓ SLA maintained. ML training pipeline on schedule. 1-click approval.

The trust model

Automation earned,
not assumed.

Every customer starts in shadow mode. Auto-remediation rights are earned through evidence — not granted on signup.

STAGE 0

Shadow Mode

ReliaOps watches silently. Logs what it would have detected and done. Weekly digest shows the counterfactual.

Weeks 1–4

STAGE 1

Suggest Mode

Slack notifications go live. Every suggestion is acknowledged or dismissed. Precision/recall reported monthly.

Weeks 4–8

STAGE 2

Tier-1 Auto

Customer opts specific (signal, action) pairs into auto-mode. Class A only. Every auto-action posts a receipt.

Week 8+

STAGE 3

Custom Auto

Class B actions eligible after 90 days of Class A success. Full audit trail. Blast-radius caps are non-negotiable.

Month 6+

Your infra detects
its own failures.

Detect. Diagnose. Fix.
In that order.

Detect

Diagnose

Fix

Incidents we're built
to prevent.

Automation earned,
not assumed.

Works with your stack.
No rip and replace.

Stop reacting.
Start Preventing.

Your infra detectsits own failures.

Detect. Diagnose. Fix.In that order.

Detect

Diagnose

Fix

Incidents we're builtto prevent.

Automation earned,not assumed.

Works with your stack.No rip and replace.

Stop reacting.Start Preventing.

Your infra detects
its own failures.

Detect. Diagnose. Fix.
In that order.

Incidents we're built
to prevent.

Automation earned,
not assumed.

Works with your stack.
No rip and replace.

Stop reacting.
Start Preventing.