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AI FOR TELECOMMUNICATIONS

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.
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5–10x

Acquisition cost vs retention cost — small churn drops protect outsized revenue.

30–50%

MTTR reduction from network anomaly detection and self-healing AI.

20–40%

Call-center deflection from AI virtual agents and real-time agent assist.

Achieve immediate, organization-wide results

Six measurable outcomes across underwriting, claims, and actuarial functions — deployed in months, not years.

Network Anomaly Detection

Real-time traffic, congestion, and fault detection across RAN, core, and transport. Cuts MTTR 30–50%.

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. Deflects 20–40% of routine contacts.

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
  • 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/6G coverage and capex-prioritization models
  • Roaming and interconnect cost optimization
  • Asset-failure prediction for transport and access network
From the playbook

How a Tier-2 mobile operator cut churn 18% and recovered $14M in annual revenue

A Tier-2 mobile operator with 4.2M postpaid subscribers was bleeding 22% annual churn — well above the regional benchmark. We built an explainable churn-prediction model fusing call detail records, network-quality experience scores, support-ticket sentiment, and competitor MNP (mobile-number-portability) signals. The model surfaced high-risk customers 60–90 days before they ported, with the top churn driver per cohort. Retention-offer targeting paired with field-engineering escalation for network-quality cases cut churn from 22% to 18%, protecting roughly $14M in annual recurring revenue for $1.2M in retention-offer spend. The same scoring engine now feeds the agent-assist tool used in 18M annual care contacts.

See more case studies →

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Speak with a telecommunications AI expert

A 45-minute scoping call. We’ll come prepared with your appetite, your loss-cost benchmarks, and a directional read on which models move the needle on your line of business.

Ask us about

  • 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/6G capex prioritization
  • Subscription, SIM-swap, IRSF, and roaming fraud ML
  • Standards (3GPP/IEEE) and patent landscape research-AI

    Contact Us
    Need experts to collaborate with for your AI/ML journey? Drop us an email and we will get in touch

    Frequently asked questions

    Can your models work with our existing OSS / BSS and CDR stack?
    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. 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

    Ready to talk telecommunications AI?

    Start with a 45-minute strategy session. We come prepared with a directional read on your line of business and a scoped proposal.

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