Standard AIOps tools go blind across legacy iron. We normalise legacy and SDN into one correlation graph, with anomaly detection around thirty minutes before an incident.
Elinext is a software development firm. Here is what gets built, in engineering terms, so AI and integration ship without touching the system of record.
A unified gateway with protocol adapters for legacy hardware and SDN/NFV normalises hundreds of metrics across 15+ vendors into one alert engine. An AI correlation engine links the events into a single root-cause graph, with predictive anomaly detection around thirty minutes before an incident.
A microservice TR-069 / TR-181 / TR-369 ACS handles roughly a million payloads per second with over-the-air firmware upgrades and predictive call avoidance, plus LSTM and XGBoost predictive maintenance with edge inference.
Built to the requirements of a real operator network rather than a lab, with secondary builds for predictive maintenance and edge inference close to where degradation happens.
The reason operators hold off is that it reads as another monitoring tool to rip in and integrate across the whole network. It is not that.
The layer sits on top of the tools you already run, through protocol adapters.
Prove it on a contained part of the network before widening to the rest.
A team that has spanned legacy and SDN in one view on a live operator environment.
Delivered work, described as built. The approach above is what we propose, not claimed as shipped in these projects.
Integrated around fifteen vendor products and unified physical and virtual SDN/NFV on one screen.
A TR-069/181/369 ACS handling about a million payloads a second, with OTA firmware, on Kubernetes.
Planning and capacity software for an operator, modelling traffic and rollout across the network.
If your monitoring goes blind where legacy meets SDN, the next step is a technical conversation. No pitch, we compare notes on where the blind spots sit and what a contained first phase looks like.
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