AI for telcos that need reliability, speed, and scale.
We build AI systems for telecom operators and MVNOs - from real-time fraud detection to churn prediction models that integrate with your existing BSS/OSS stack.
What we build for telcos
Network Optimisation
AI-driven traffic management, predictive maintenance for network infrastructure, and anomaly detection to reduce outage time and improve QoS across large network topologies.
Churn Prediction & Retention
Machine learning models that identify at-risk subscribers 30–90 days before churn, enabling targeted retention interventions that reduce churn rates by 20–35%.
Customer Service Automation
AI-powered virtual agents that resolve billing queries, plan changes, and technical support tickets without human escalation - reducing contact centre costs by up to 60%.
Fraud Detection
Real-time detection of SIM swap fraud, subscription fraud, international revenue share fraud (IRSF), and toll bypass - with sub-second decisioning for high-volume event streams.
Revenue Assurance
AI systems that identify billing anomalies, revenue leakage, and unbilled usage events across complex interconnect and roaming agreements.
5G & IoT Analytics
Predictive analytics for 5G slicing optimisation, IoT device behaviour anomaly detection, and capacity planning for connected device fleets.
Why telco AI is different
Telco data complexity
Network events, CDRs, signalling data, and CRM records are heterogeneous at massive scale. We have the data engineering experience to build reliable telco data pipelines.
Real-time requirements
Fraud detection and network anomaly detection require sub-second inference. We build low-latency inference pipelines that can handle telco event volumes.
Regulatory compliance
Telecom is heavily regulated. We build systems that comply with GDPR, PECR, and national telecom regulations across multiple jurisdictions.
Legacy integration
Most telco environments include legacy OSS/BSS systems. We have experience integrating modern AI layers with TIBCO, Amdocs, and legacy billing platforms.
Telecom AI - results at scale
35%
Churn rate reduction in first 12 months for a Tier-2 MVNO using ML retention scoring
60%
Contact centre cost reduction via AI virtual agent handling billing and plan queries
< 500ms
End-to-end IRSF fraud detection latency on high-volume CDR event streams
90 day
Advance churn prediction window - allowing proactive retention before contract expiry
Churn AI deep dive
How our churn prediction model works
Signal inputs
Usage patterns
Data consumption, call volume, roaming usage, app category breakdown - compared to historical baseline per subscriber
Billing events
Late payments, plan downgrades, data top-ups, and complaint calls - each weighted as churn precursor signals
Network quality
Per-subscriber dropped call rate, throughput degradation, and geographic coverage gaps - NPS proxy from network data
Competitive signals
Contract expiry dates, competitor promotional cycles, and port-out velocity in each market segment
Model outputs & actions
High risk
Proactive outreach call + personalised retention offer within 48h. Offer tier calibrated to predicted LTV.
Medium risk
Targeted SMS or in-app message with relevant upsell or loyalty reward. No inbound call required.
Low risk
Standard NPS survey triggered. Feedback fed back into signal weighting for future predictions.
Model retrained monthly on fresh CRM + network data. Threshold calibration reviewed quarterly with your retention team. Performance reported as churn rate delta vs. control group.
Before vs. after
What AI changes for telecom operations
Tech stack
The technology behind our telco AI systems
Data ingestion & streaming
ML & AI models
BSS / OSS integration
Infrastructure & observability