Telecom · Network anomaly detection
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.
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.
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.
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.
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.
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.
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.
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.
Metrics are being verified in our case-study pack. The deployment is live; we publish numbers only once independently confirmed.
Elisa
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.
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.
We have seen enough failed NOC AI pilots to know what sinks them. We will tell you before you fund one:
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.
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.
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.
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.
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.
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 45-min scoping call
