AI that keeps the network up
Predict the cell-site failure before subscribers feel it, the churn before the port-out request, and the spectrum congestion before peak hour — built on your OSS/BSS data, in production at a global telco.
A Hominis app module · your telecommunications data, app-ified
See the failure, the churn and the congestion coming
Prediction across RAN, OSS/BSS, CDR and alarm streams — in production at a global telco.
Proactive network maintenance
Move from reactive fixes to predictive maintenance — forecast cell-site and transport failures from alarm, KPI and weather telemetry days before they degrade service.
In production · global telco
Churn prediction & retention
Score every subscriber's port-out risk from usage, billing-shock, network-experience and care-contact signals, then trigger the right save offer before the cancel call lands.
Industry benchmark: 10–15% lower churn
Spectrum & capacity optimization
Read the RF waterfall in real time — surface congested carriers and idle bandwidth so planners can reallocate spectrum and schedule densification where demand is actually moving.
RIS · SpectrumWaterfall instrument
RAN energy & cost optimization
Predict per-cell traffic troughs and safely sleep radios in low-demand windows — the largest controllable line item in a mobile operator's OPEX, tuned without dropping SLA.
Industry benchmark: up to 20% RAN energy saved
Service-assurance & fraud
Correlate cross-domain alarms into one probable root cause for the NOC, and flag SIM-swap, IRSF and subscription fraud on the CDR stream before revenue leaks.
Real-time on CDR / alarm streams
Runs at carrier scale, explains every flag
What moved it from reactive fixes into live network operations.
Models that run on telco-scale streaming data
The platform ingests CDRs, RAN counters and alarm floods at carrier volume and latency — not a batch sample. That throughput, and native integration with the OSS/BSS systems already in the network, is what moved it from reactive fixes to proactive maintenance in live production.
Root-cause explanations the NOC can act on
Every prediction surfaces the alarms and KPIs driving it, so an engineer sees why a site is flagged — not just a score. That interrogable root cause, plus on-prem/sovereign deployment for subscriber-data residency, is what earned trust inside the network-operations workflow.
One method, tuned for telecommunications
Assess, Transform, Sustain — the cycle every organization runs, dropped one level deeper for your sector's pains and sticky aspects.
Assess
Map OSS/BSS, RAN-counter, CDR and alarm data; rank by revenue-at-risk
We inventory your fragmented telemetry across RAN, transport, OSS/BSS and care — where the alarm floods, CDR streams and subscriber records live and how clean they are — then rank candidate use cases by revenue-at-risk and SLA exposure. Output in 4–6 weeks: a roadmap naming which churn, network-maintenance and spectrum bets pay back first, sequenced against your data readiness.
Transform
Streaming models on live RAN/CDR telemetry, wired into the NOC and retention
We build the failure-prediction, churn-scoring and spectrum-optimization models against your live RAN counters, CDRs and alarm streams, then harden them into the operator's own systems — predictions landing in the NOC console, save offers in the retention engine, congestion flags in capacity planning. Integration into operations, not a dashboard beside them.
Sustain (AIOps)
Retrain against shifting traffic, new RAN releases and alarm-storm drift
Telco data shifts under you — new spectrum bands, RAN software releases, tariff changes and seasonal demand all move the baseline. We monitor for drift against live KPIs, retrain on the network's operational rhythm, and watch alarm-storm conditions so root-cause accuracy holds during exactly the incidents that matter most.