AI Root Cause Analysis
Turn hours of debugging into seconds. Our deterministic AI engine correlates millions of signals to pinpoint the exact broken commit, pod, or configuration.
How it works
Multi-Signal Ingestion
We ingest logs, metrics, and traces across your entire stack in real-time, building a semantic map of your infrastructure.
Deterministic Correlation
Our engine identifies patterns of failure across disparate systems, linking related anomalies into a single incident timeline.
Automated Diagnosis
Operyn pinpoints the root cause down to the specific line of code or configuration change that triggered the failure.
Explainable AI
Decisions your team can inspect before they trust
Operyn shows the evidence, recent changes, and policy context behind each diagnosis.
Diagnosis
Connection pool exhaustion after deploy `v2.4.1`
Operyn correlated elevated DB wait time, pool exhaustion errors, and the most recent deployment to identify the likely root cause in seconds.
Why this decision
Recommended action
Rollback `payment-api` to `v2.3.9`
Zero-downtime rollback available. Production approval required.
AI decision
incident/payment-api
Diagnosis
Connection pool exhaustion after deploy `v2.4.1`
Evidence points to deploy-related DB saturation in production.
Deploy
v2.4.1 completed 11m ago
Evidence
1,247 matching errors across 3 pods
Action
Rollback `payment-api` to `v2.3.9`
Policy
Prod approval gate
Connected infrastructure context.
Isolated telemetry is just noise. Operyn weaves together data from every layer of your stack to build a complete picture of why an incident occurred.
Structure and parse millions of log lines to find errors and stack traces.
Detect subtle performance regressions and resource bottlenecks.
Trace the path of failed requests across distributed microservices.
Correlate every incident with your recent GitHub commits and deployments.
Zero Hallucinations.
Unlike general-purpose LLMs, Operyn uses a specialized inference engine specifically trained on engineering telemetry. Every diagnosis is backed by raw data links you can verify.
Old Way vs. Operyn
Stop digging through silos once and for all.
Manual Debugging
- Hours spent searching across logs and metrics
- Context switching between multiple tabs and tools
- High risk of human error during stressful outages
- Manual correlation of traces and code changes
The Operyn Way
- Seconds from alert to root cause diagnosis
- Unified context from every layer of your stack
- Deterministic, evidence-based AI reasoning
- Automated impact analysis on every deployment
Frequently Asked
How does the AI avoid hallucinations?
Unlike standard LLMs, our engine is deterministic. It requires raw data evidence (logs, traces) for every assertion it makes. If the data doesn't support a theory, it doesn't present it.
Does my data leave my infrastructure?
We offer flexible deployment models including hybrid and self-hosted for Enterprise plans. For Cloud plans, data is processed in SOC2-compliant isolated environments with zero retention.
How long does integration take?
Most teams are up and running in under 15 minutes. We integrate directly with your existing observability tools like Datadog, AWS, and Prometheus.
The brain of your operations.
Ready to see how Operyn can help your team? Let's talk.