Every Alert.
Before You're Paged.
You already have alerts. You need answers. Qorxenna is the last-mile observability layer that actively logs into devices, unleashes a squad of AI agents to correlate evidence, and delivers root cause analysis before your engineers even open their laptops.
What happened
while you slept?
Every incident that triggers overnight is automatically investigated — evidence correlated, root cause identified, next steps drafted — before your team arrives in the morning.
Your engineers wake up to structured verdicts, not an inbox full of raw alerts and nothing investigated.
Built for network operations teams
that can't afford to miss an incident
From initial alert to root cause verdict — without a human in the loop until a decision actually needs to be made.
Autonomous Alert Triage
Every network alert is automatically investigated the moment it fires — before a human is paged. Evidence is correlated across device configuration, event logs, and monitoring data to produce a structured verdict in under 10 seconds.
Root Cause Analysis
Structured verdicts with calibrated confidence. Every finding is cited back to the evidence that supports it — no black-box conclusions.
Configuration Audit
Continuous configuration snapshot collection. Every change is versioned, diffed, and automatically correlated with any incident that follows it.
Geo Network Map
Your entire device estate on an interactive map — overlaid with live incident state, AI triage status, and actionable next steps per site.
Live Diagnostics
On-demand SSH diagnostics without leaving the platform. Audit-logged, read-only command execution across your device estate.
Human-in-the-Loop Safety
The AI investigates and recommends. Engineers decide and act. Approval gates are built into the workflow — no autonomous remediation without explicit authority.
Observable AI Reasoning
Every AI investigation step is visible in real time. Watch evidence being gathered, see where confidence is high or low, step through completed investigations at your own pace.
Three Evidence Lanes.
Every investigation draws from three independent sources of truth — separately collected, independently analysed, and correlated in every verdict. No single point of failure in the evidence chain.
Monitoring Context
Real-time inventory discovery, physical port statuses, and metrics via active polling integration.
Event Context
Device-generated events streaming from centralized aggregators.
Configuration Diff
Direct connection captures the true running state, parsed cleanly into structured payloads for the AI.
$ show running-config interface Gi0/1
- switchport mode access+ switchport mode trunk
From alert to verdict.
In seconds.
The investigation pipeline runs automatically the moment a problem is detected. No human needs to start it. No query needs to be written.
Alert Detected
A network problem is detected — from monitoring, a poll failure, or a configuration drift event. The investigation begins immediately, without waiting for a human.
Evidence Gathered
Available evidence is collected and correlated: configuration history, event log entries, and monitoring signals — all scoped to the device and time window of the incident.
Analysis Complete
The AI investigation pipeline analyses each evidence stream and synthesises findings into a structured verdict — with calibrated confidence and explicit citations.
Verdict Delivered
A root cause hypothesis, next steps, and supporting evidence are stored and surfaced to the engineering team. When a decision needs to be made, a human makes it.
The AI shows its work
A verdict without evidence is just a guess. Qorxenna doesn't guess — and it tells you exactly what it doesn't know.
Every Finding Is Cited
Every AI verdict traces back to real, collected evidence — configuration history, event logs, or monitoring data. No findings without a source. No sources without a link to the raw data in the platform.
Example citation
“Interface eth1 has been down for 6 days · Confirmed by monitoring · Last config change: 3 days prior — no related diffs.”
Calibrated Confidence
Every verdict carries a confidence rating: HIGH, MEDIUM, or LOW — with an explanation. The AI doesn't produce false certainty. If evidence is thin, the verdict says so.
Confidence scale
Transparent Gaps
When a data source is unavailable or evidence is missing, the verdict explicitly notes the gap and adjusts confidence accordingly. The AI never hallucinate around missing data.
Example gap note
“Log data unavailable for this device — syslog adapter not configured. Verdict confidence downgraded to MEDIUM. Configure a log source to improve accuracy.”
Monitoring is the first mile.
We do the last mile.
Dashboards only tell you something broke. Connect your existing monitoring, logs, and ITSM — Qorxenna adds the massive cognitive power of multi-agent triage on top.
Data Source Adapters
Monitoring
Log Aggregation
Ticketing & ITSM
Adapters are isolated at the platform edge. Swapping or adding a data source requires no changes to the investigation engine.
Bring Your Own AI
Qorxenna uses any OpenAI-compatible LLM provider. Deploy on-premise with a local model, or connect to a hosted provider — the investigation pipeline is completely provider-agnostic.
Network operations data stays on your infrastructure. No telemetry, no data exfiltration — verified by design.
See Qorxenna
diagnose a live incident
We'll walk you through autonomous triage against real device infrastructure — not a scripted slideshow. Bring your hardest network operations problem.
What you'll see