Predictive AI for Cell Tower Failures

Challenge

Goals: A telecom provider needed an AI system. It had to forecast potential cell tower outages. Predictions were based on traffic, latency, and hardware telemetry. Enable predictive maintenance and reduce unplanned downtime.

Solution

Solutions: We built a time-series ML model using XGBoost and LSTM on data from 10K+ towers, integrated with real-time logs and SNMP streams. It delivers alerts and risk scores via a central dashboard.

Results

Results: Downtime fell 38%, dispatch efficiency tripled, and the client saved $1.2M in maintenance in year one through predictive insights.

Technologies & Tools

More Work

AgroNet ZERO

AI-Driven Data Analysis for Sustainable Agriculture

SellerCapital

Client Scoring System and Internal Data
Collection

Lumenis

Leading Medical Equipment Website