AI for Telecom Operators
Customers who leave before your retention team knows they are at risk, network faults found after subscribers call to complain, and fraud patterns that rules catch only after the damage is done: these are the operational and revenue problems that AI addresses in telecom.
We build AI systems for telecom operators, MVNOs, and ISPs: churn prediction, network anomaly detection, AI customer support for billing and service queries, intelligent fraud detection for usage anomalies and SIM swap fraud, predictive maintenance for network assets, and demand forecasting for network capacity planning. Every system is scoped against your subscriber data, network telemetry, and a specific retention or operational outcome.
Churn prediction models that score each subscriber by departure risk 30-60 days before they cancel
Network anomaly detection that surfaces fault signatures in telemetry data before subscribers report service issues
SIM swap and usage anomaly fraud detection that flags suspicious patterns before significant revenue impact
Capacity demand forecasts that let network planning teams allocate investment ahead of congestion
Recent outcomes
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6× deeper insights
Text-based interviews converted to automated phone calls
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1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
The problem
Are you finding out a subscriber is about to churn only after they've already submitted a PAC code or called to cancel?
Is your network operations center responding to faults that your telemetry data could have predicted 24-48 hours earlier?
Short answer
RaftLabs builds AI for telecom operators across the US, UK, Europe, Canada, and the UAE: churn prediction at a 30-60 day horizon, network anomaly detection, SIM swap fraud scoring, and demand forecasting. Shipping production software since 2015. A first model is scoped and fixed-price after a discovery phase, then expanded across more signals.
Key takeaways
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A subscriber's usage has been sliding for six weeks. Their last two bills ran higher than they expected. They called support once, waited, and hung up. Every one of those signals sat in your data. None of them reached a person who could act.
Then the PAC code request lands, and the retention team scrambles with a discount that arrives too late.
The churn signal was in the usage data before the call. The fault signature was in the telemetry before the outage. The fraud pattern was in the account activity before the swap. The only question is whether a model is reading them.
Telecom AI is most valuable when it shifts operations from reactive to anticipatory. The churn signal is in the usage data before the subscriber calls. The network fault signature is in the telemetry before the service degrades. The fraud pattern is in the account activity before the swap completes. The question is whether a model is reading those signals.
Telecom operators in the top quartile of AI adoption run 4 to 7 percentage points higher EBITDA margin than the median. The gap between AI leaders and laggards is now wider in telecom than in any other industry (McKinsey, "Telcos' AI inflection point"). Applied across the full customer journey, AI has been linked to churn reduction of around 30 percent (McKinsey). For operators facing thin margins and high acquisition costs, the case for building these systems is direct.
RaftLabs has shipped production software since 2015 for clients including Aldi, Nike, Cisco, and Lockheed Martin, rated 4.9/5 by clients on Clutch. The team that scopes your problem in week 1 is the team that ships it: no offshore handoff after the contract is signed. GDPR and telecom data-protection requirements are scoped in week 1, not retrofitted before launch, and every project is scoped and fixed-price before development starts, with a scope change handled as a priced change request rather than absorbed into the final invoice.
Everything on the left should already be true for your operation. Even one thing on the right, and a scoped discovery conversation matters more than a build right now.
You're a mobile operator, MVNO, or ISP with 12-24 months of subscriber, usage, and network telemetry to train a model on.
You have a specific retention, fraud, or network outcome in mind, not AI for its own sake.
You can give a model access to your BSS, OSS, or CRM systems via API.
What we build
| AI system | The signal it reads | Where the decision lands |
|---|---|---|
| Churn prediction | Usage decline, bill shock, support contacts, days to contract end | Retention workflow, 30-60 days before cancellation |
| Network anomaly detection | Per-element KPI deviation from a learned baseline | Prioritized NOC alert queue, ranked by subscriber impact |
| SIM swap and fraud | Recent contact, device and address changes, request timing | Hold for verification before the swap processes |
| Care deflection | Live bill and account data via the BSS API | Resolve routine contacts, escalate complex ones with context |
| Capacity forecasting | Historical traffic, growth trends, event calendars | Capacity upgrade schedule, before congestion hits |
| Predictive maintenance | Temperature, power draw, error rates, hardware health | Field dispatch by failure probability, not calendar |
Churn, fraud, network faults, or support costs: tell us the specific problem and we will assess which AI system reduces it and what your data supports.
How we build
Telecom AI does not start with a model. It starts with the signal already in your data and the exact decision point where acting on it changes the outcome. We build backward from that decision. A first model reaches a production pilot in 10 to 16 weeks, then expands.
We map which subscriber and network signals already exist and where they live: CDRs, usage records, and CRM contact logs in the BSS, KPIs and telemetry in the OSS, labeled fraud or churn outcomes in your case history. The output is a written scope, a fixed price, and the one intervention point the model has to reach. No build starts without your sign-off.
We confirm the labeled outcomes the model learns from and set the baseline it has to beat: today's churn save rate, today's fraud catch rate, today's mean time to detect a fault. A model that cannot beat your current rules workflow is not worth shipping, and this is where we find that out, not in week ten.
We build the model to land where the decision gets made, not in a dashboard nobody opens: a scored subscriber that enters the retention workflow, a ranked NOC alert, a held SIM swap. Working outputs hit a staging environment inside the first sprint, with bi-weekly demos and validation running alongside every sprint.
The model runs in shadow against live traffic before it touches a single subscriber, so you see its precision on your real data ahead of cutover. Then production deployment with drift monitoring and a retraining trigger configured before handover, plus 8 weeks of post-launch support. From here, the pilot expands to the next signal.
Most telecom AI projects do not fail on the algorithm. They fail on the things around it. These are the failure modes we scope against before a line of model code is written.
Today's telecom AI scores a signal and hands a human the decision. The next generation acts on it. Agentic network operations close the loop: a model that detects a degrading cell sector also opens the ticket, tests the remediation, and reroutes traffic while an engineer supervises rather than executes. We build toward that in steps, not in one leap. Ship the scoring model first, earn trust on its precision, then hand it progressively more of the response. An agent you cannot audit is one you cannot run in a regulated network, so every action stays logged, bounded, and reversible.
Tell us the churn, fraud, or network problem you want to solve. You get a written scope and a fixed price after a discovery phase, with no development starting before sign-off.
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Read moreChurn prediction for telecom operators is a binary classification problem: for each subscriber, predict the probability that they will leave within a defined horizon, typically 30, 60, or 90 days. The model uses features derived from your subscriber records and usage data: contract remaining term, tariff type, usage volume trend over the last 3 months, customer service contact history, payment history, handset age, and network quality experience on the cell sites the subscriber uses most. Subscribers above a churn probability threshold enter a retention workflow before they submit a cancellation or PAC request. The intervention threshold is tuned against subscriber value tiers and offer cost structure to avoid discounting subscribers who would have stayed without an incentive.
Standard network monitoring generates alerts when a KPI crosses a threshold. Threshold alerting catches obvious degradation but misses subtle early signatures and generates large volumes of false positives when thresholds are set broadly. Network anomaly detection models learn the expected behavior of each network element under different traffic conditions, time of day, and seasonal patterns. A deviation from the model's expected value surfaces as an anomaly even if the absolute KPI hasn't crossed a static threshold. This means you see the early signature of a failing piece of equipment days before it becomes a service-affecting fault. Output is a prioritized alert queue for the network operations center, ranked by anomaly severity and estimated subscriber impact.
SIM swap fraud detection is a classification model that scores each SIM swap or port-out request by the probability that it is fraudulent. Features include the account holder's history of contact with customer service in the preceding 48-72 hours, device change history, account age, recent address or email changes, time of request, and the submission channel. High-risk swap requests are held for additional verification rather than being processed automatically. The model is trained on your historical SIM swap data labeled with confirmed fraud outcomes. Because SIM swap is used to take over high-value accounts and bypass SMS-based two-factor authentication, early detection prevents downstream fraud losses disproportionate to the cost of the swap itself.
Network capacity demand forecasting predicts traffic load at the cell site, backhaul segment, or core node level over planning horizons of weeks to months. Inputs include historical traffic data by element and time period, subscriber growth projections, planned network events such as major sporting events, and new site activation schedules. The model produces forecasts at the granularity your planning team uses, with uncertainty bounds. Network planning teams use these forecasts to schedule capacity upgrades before congestion occurs rather than reacting to subscriber complaints.
Telecom AI projects at RaftLabs are scoped and fixed-price before development starts. We scope the first model as a fixed-price pilot, then expand across more signals once it proves out on your data. A focused first system such as a churn prediction model or SIM swap fraud classifier typically runs 10-16 weeks from kick-off to production. Cost depends on the scope of data integration, the number of models, and the complexity of the intervention workflow. You receive a written scope and fixed price after a discovery phase, with no development starting before sign-off.
Yes. A voice agent authenticates the subscriber, pulls the current bill from the BSS API, and walks through the charge items generating the inquiry, taking payment on the same call where needed, in 3 to 4 minutes against 8 to 12 for a human agent working three separate systems manually. During outage events, inbound volume can spike 5 to 10x normal levels; the agent checks the OSS for a confirmed outage in the caller's area and proactively states the restoration estimate before running any diagnostic flow, which absorbs the spike without a queue.
For churn prediction, we need subscriber records, usage history (voice, data, SMS by month), customer service contact logs, and ideally network quality data per subscriber. For fraud detection, we need historical SIM swap or port-out records labeled with confirmed fraud outcomes, account activity logs, and contact history. The discovery phase maps your available data to the specific model being built and identifies gaps before development starts. Projects with 12-24 months of labeled historical data produce the most reliable models.
Work with us
We scope AI for Telecom Operators in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.