Telecom · Network anomaly detection
Your NOC's threshold alerts are trained to be ignored
When every interface flap and CPU spike fires an alarm, engineers learn to discount the wall. The fault that takes down a cell cluster or a fibre segment goes unreported for minutes — because it looks the same as the noise.
Banao builds anomaly detection over live network telemetry — SNMP counters, NetFlow, syslog, OSS feeds — and replaces static threshold alarms with models that know what normal looks like for each node, at each hour, under each traffic pattern.
Elisa— live telemetry anomalies ranked and triaged in the existing alarm pipeline.
The first call is free · 45 minutes · no obligation
What we build
What the anomaly detection deployment covers
Anomaly detection is not one model. It is the ingestion pipeline, the baseline modelling, the alert triage, and the NOC workflow — we own all four.
Per-node adaptive baselines
Static thresholds fire on everyone's normal. Banao trains per-node, per-hour baselines so a 200 Mbps spike on a low-traffic branch is flagged while the same spike on a backbone link is not.
Multi-source telemetry ingestion
The model reads SNMP counters, NetFlow and IPFIX records, syslog streams, and OSS alarm feeds together — because a real fault is a pattern across sources, not a spike in one counter.
Alert clustering and noise suppression
Related alarms on the same fault are grouped before the NOC sees them. One high-priority cluster replaces fifty individual threshold fires — the NOC reads the situation, not the alarm list.
Root-cause correlation
Where the telemetry supports it, the system traces a fault cluster back to its likely origin: a device, a segment, or a configuration change, surfaced as the first thing the engineer checks.
NOC triage dashboard
A live view of ranked anomalies by severity and customer impact — ordered by what is breaking, not by what fired last. Built to fit the screen your engineers watch, not a generic AIOps portal.
Feedback loop and continuous retraining
NOC engineers mark false positives and confirm real faults. Every correction feeds back into the model, so precision improves with each shift instead of drifting as the network changes.
Receipts
Where this runs today
Metrics are being verified in our case-study pack. The deployment is live; we publish numbers only once independently confirmed.
Threshold noise cut, real faults surface before subscribers report them
A major European operator ran its NOC on static thresholds that generated hundreds of alarms per shift. Engineers had stopped reading most of them. Banao built anomaly models over live telemetry and fed the ranked output into the existing alarm pipeline — no new tooling needed in the network.
Dogfooding
We run our own company on the AI we sell
Banao operates ~300 engineers on its own AI products before any client sees them. InterviewGod screens our own hires; Vikaas runs our own demand generation. A system that has to survive our internal operation is already stress-tested before it reaches your NOC.
Network anomaly detection punishes anything that cries wolf. We hold our own deployments to the same standard: if the alert rate corrodes trust, the system is broken — regardless of what the accuracy metric says.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When anomaly detection is not the fix
We have seen enough failed NOC AI pilots to know what sinks them. We will tell you before you fund one:
- Sparse telemetry: if your network devices report at 5-minute polling intervals and syslog is sampled, the model sees too little signal to beat an analyst with a spreadsheet. We audit the feed before quoting anything.
- Stable, predictable topology: small networks with few links and predictable traffic patterns rarely generate enough anomalies to justify the build. A good monitoring threshold setup is a faster answer.
- No NOC workflow change: an anomaly detection model that fires into a ticket queue nobody reads is worthless. If there is no plan to change how the NOC triages, the model output will be ignored within two weeks.
How we start
How we start — two weeks before you commit to a build
You have been pitched AIOps by every network vendor. We start by showing you whether the signal in your telemetry is strong enough to beat your current process.
- 01
AI Discovery Sprint
2 weeks · fixed price
We ingest a sample of your historical telemetry and run an anomaly detection feasibility test against your real alarm logs. You leave with a precision/recall baseline, a cost-of-problem analysis, and a go/no-go recommendation — yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Live telemetry ingestion, per-node baseline training, alert clustering, and integration with your existing alarm management and ticketing systems. The NOC dashboard is part of the deliverable.
- 03
Production & continuous learning
Deployment into the live NOC workflow with NOC-team onboarding, operator feedback wiring, and a monthly model refresh cycle. The system improves on your network, not a benchmark dataset.
FAQ
Frequently asked questions
Can this work with our existing NMS and alarm management platform?
Yes. Banao writes the anomaly output into your existing alarm management system — Netcool, ServiceNow, Zabbix, or a proprietary platform — so the NOC does not need a new tool. The integration is a first-week deliverable, not an afterthought.
How long before the model knows our network's normal?
Roughly two to four weeks of historical telemetry is enough to establish per-node baselines for most network profiles. The Discovery Sprint ingests this data and tells you what precision you can expect before you fund the full build.
Our network is a mix of vendors — Cisco, Nokia, Huawei, some white-box. Is that a problem?
No — it is the common case. Banao normalises SNMP, NetFlow, IPFIX, and syslog from mixed-vendor environments at the ingestion layer. The anomaly model works on the normalised signal, not vendor-specific output formats.
What stops the model from making the false-positive problem worse?
Two things: alert clustering groups related alarms from the same fault before the NOC sees them, and the per-node baseline means a normal traffic pattern on any given node does not fire. The NOC feedback loop then corrects remaining false positives, and those corrections retrain the model. Precision improves over time rather than staying fixed.
How does this interact with our existing capacity planning and root-cause analysis tools?
The anomaly detection output can feed downstream into your existing RCA tools as a structured event stream. We wire the integration during the build phase. The anomaly system is not a replacement for capacity planning — it is an early warning that something is breaking, so your engineering team can act before it becomes a capacity crisis.
Get started
Find out if your telemetry can beat your current alarms
Bring your NMS logs and your worst false-positive period. In 45 minutes we will tell you whether an anomaly model on your network is worth building — and what the first two weeks would produce.
Book a Discovery Sprint