Custom AI/ML solutions for telecommunications industry
Custom AI and ML for telcos, MVNOs, and tower operators. Network anomaly detection, churn prediction, customer experience automation, and capacity planning — built for telco scale.
AI for telecom network operations, customer care and capacity planning
Six measurable outcomes across network operations, customer care, revenue and capacity planning, built on your OSS, BSS and network telemetry.
Network Anomaly Detection
Real-time traffic, congestion and fault detection across RAN, core and transport, with a probable root cause for the NOC to confirm.
Churn Prediction & Retention
ML on CDRs, usage, and CX signals. Explainable churn-driver insights to power targeted offers.
Agent Assist & CX Automation
Real-time call summarization, next-best-action and intent classification, with agents reviewing each suggestion before acting on it.
Capacity Planning & RAN Optimization
Cell-site demand forecasting, beam-management ML, and capex prioritization per geography.
Fraud & Security ML
Subscription fraud, SIM-swap, IRSF, and roaming-abuse classifiers tuned to telco-specific patterns.
Standards & Patent Research AI
RAG-grounded research-AI over 3GPP, IEEE, ITU standards, USPTO / EPO patents, and your internal R&D library. For network engineering, standards strategy, and patent teams.
Capabilities across the telecommunications value chain
Network Operations & RAN
- Real-time anomaly detection across RAN, core, and transport
- Self-healing playbooks and automated root-cause analysis
- Beam-management and 5G slicing optimization
- O-RAN rApps and xApps for closed-loop RIC optimization
- Configuration-drift and security-posture monitoring
Customer Experience & Care
- Agent-assist LLMs with real-time summarization and next-best-action
- Intent classification and self-service deflection
- CX sentiment and pain-point detection
- Outage-call surge prediction and pre-emptive comms
Revenue, Churn & Growth
- Churn prediction with explainable risk drivers
- Lifetime-value and next-product-likelihood modeling
- Cross-sell and offer-targeting ML
- Fraud detection (subscription, SIM-swap, IRSF, roaming)
Capacity, Planning & Investment
- Cell-site demand forecasting and capacity planning
- 5G-Advanced (Rel-20) and 6G (Rel-21) capex-prioritization models
- Roaming and interconnect cost optimization
- Asset-failure prediction for transport and access network
How a customer care AI agent works for a mobile operator
In a mobile operator’s care center, every billing, plan or network-quality contact needs the subscriber’s account and network history before anyone can answer it. We build a customer care agent that classifies each contact’s intent and answers routine questions from call detail records, network-quality experience scores and support-ticket history in the operator’s OSS and BSS. Care leads decide what the agent may resolve and live agents take every other contact with the full transcript in hand, while network-quality cases reach field engineering with the supporting telemetry attached.
See AI call center agents →Speak with a telecommunications AI expert
A 30-minute scoping call. We’ll come prepared with your churn and MTTR benchmarks, and a directional read on which models move the needle across your network and care operations.
Book a Strategy Session- Network anomaly detection across RAN, core, and transport
- Churn prediction with explainable risk drivers per subscriber
- Agent-assist LLMs and self-service deflection
- Cell-site capacity planning and 5G-Advanced/6G capex prioritization
- Subscription, SIM-swap, IRSF, and roaming fraud ML
- Standards (3GPP/IEEE) and patent landscape research-AI
Frequently Asked Questions
Yes. We integrate with Amdocs, Netcracker, Oracle BSS, and Ericsson Telecom Charging via standard APIs and Kafka streams. CDRs ingest via standard 3GPP formats; we handle vendor-specific RAN telemetry from Ericsson, Nokia, Huawei, Samsung, and Open RAN deployments, including rApp and xApp integration against the O-RAN Non-RT and Near-RT RIC. Models can run as recommendations your NOC and care teams approve, or fully autonomous in well-bounded loops.
With careful sampling, edge inference, and streaming architectures. Most network-anomaly models run on aggregated KPIs (1–15 minute windows) rather than raw packet data, and edge inference handles latency-critical use cases (DDoS, fraud) without round-tripping to the cloud. For raw telemetry needs, we partition by domain (RAN, IP core, transport) so the model surface stays manageable.
All training and inference happens in your environment or single-tenant cloud you own. PII is pseudonymized at ingest, and we use differential privacy for cross-subscriber analytics. We comply with GDPR, CCPA, e-Privacy, lawful-intercept boundaries, and emerging state-level privacy rules. Models ship with audit-ready data lineage and right-to-erasure compliance.
Yes. The underlying ML platform (telemetry ingest, feature store, model registry, monitoring) is reusable across business units. Domain models differ — mobile focuses on RAN and SIM fraud, fixed on field-ops and broadband-quality, B2B on SLA-aware capacity and service-tier scoring — but most converged operators run one shared stack with multiple model families.
Explore AI/ML solutions for telecommunications
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Private AI for business on infrastructure you control: on-premise AI, private LLMs, chat, search and RAG deployed on your servers or in your cloud tenant.
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Ready to talk telecommunications AI?
Start with a 30-minute strategy session. We come prepared with a directional read on your operation and a scoped proposal.