AI MVP Development for Telecom & Connectivity

Telecom operators sit on more machine-generated data than almost any other industry — streaming SNMP counters, NETCONF/YANG state, gRPC telemetry off gNodeBs and routers, plus millions of CDRs and IPDRs an hour — yet a NOC still drowns in alarm storms where one fiber cut lights up thousands of downstream traps. SpeedMVPs builds AIOps MVPs that do alarm correlation and topology-aware root-cause analysis on your live event stream, so a single probable-cause ticket replaces the flood. On the RAN side we train time-series models on RSRP/RSRQ/SINR, PRB utilization, and handover-failure counters to flag degrading cells and predict sleeping-cell or backhaul faults before the trouble tickets arrive — the SON-adjacent problem that Ericsson, Nokia, and O-RAN RIC vendors are all chasing, delivered against your data in a two-to-three-week build rather than a multi-quarter integration.

Fraud and revenue leakage are existential in telecom, and the attack patterns are nothing like a generic 'fraud model' — they are SIM-box interconnect bypass, International Revenue Share Fraud (IRSF), Wangiri call-back scams, roaming fraud that surfaces only after the TAP file settles days later, and subscription fraud at onboarding. We build revenue-assurance engines that mine CDR/IPDR streams with graph analytics to expose the many-to-one calling fan-outs and premium-rate destination clusters that betray a SIM box, and score new activations against velocity and device-reputation signals in real time. This maps directly to the anomaly and graph techniques behind our fintech-fraud-detection case study, retargeted to A-number/B-number topology, IMSI/IMEI pairings, and TM Forum revenue-assurance KPIs rather than card transactions.

Churn in a saturated carrier market is won or lost on experience, not price, so our propensity and QoE models blend BSS/CRM history with network-side truth: dropped-call rate, VoLTE/VoNR MOS, throughput at the cell edge, data-session setup failures, and repeated CPE reboots pulled via TR-069/TR-369 (USP). Rather than a churn score no one can act on, we ship next-best-action outputs — proactive credit, a femtocell offer, a truck-roll — wired into the CRM your care team already uses (Salesforce Communications Cloud, Amdocs, or a custom BSS). The customer-facing analytics layer mirrors the pattern in our enterprise-analytics-copilot work: a natural-language interface over network and subscriber data so a retention manager can ask 'which high-ARPU accounts saw degraded VoLTE in this market last week' without writing a query.

Care is the other margin lever. A telco RAG copilot has to be grounded in more than a help-center wiki — it needs live outage status, the subscriber's plan and entitlements, and real device diagnostics so it can run a TR-069 line check or reboot an ONT before escalating a truck-roll. We build agent-assist and self-service copilots that retrieve from your knowledge base and OSS in one turn, propose the fix, and hand off cleanly with full transcript context, deflecting the repetitive 'no internet' and billing-dispute contacts that dominate contact-center volume. Because robocall and spoofing complaints now flow through the same channels, we can also surface STIR/SHAKEN attestation context so agents and subscribers understand why a call was flagged.

None of this ships without respecting the regulatory reality of a carrier. Customer Proprietary Network Information is governed by 47 U.S.C. 222 and FCC rules, so CDR-derived features and any model training run inside your VPC with CPNI access controls and audit logging, never exported to a third-party cloud you don't control. We design around CALEA lawful-intercept boundaries, TCPA constraints on any outbound automation, E911/NG911 data sensitivity, and — for EU or multi-region operators — GDPR and data-residency rules on location and traffic data. The security posture leans on the same detection thinking as our ai-cyber-threat-intelligence build: signaling anomalies (SS7/Diameter), unusual roaming patterns, and network intrusion are treated as first-class model inputs, not an afterthought.

Delivery is engineered for an operator's stack, not a greenfield demo. We integrate through TM Forum Open APIs and ODA-aligned interfaces where they exist, consume streaming telemetry over Kafka and gRPC, and speak SNMP, NETCONF, and Diameter to the network and Amdocs/Netcracker/ServiceNow to the business side — so the AI service sits behind clean APIs your OSS/BSS can call without a rip-and-replace. SpeedMVPs has shipped 18+ production AI MVPs with a 15+ engineer senior team, each in a fixed 2-3 week cycle, and you receive 100% of the code, model weights, training pipelines, and architecture docs at handover — no per-CDR inference fees on your own data and no black-box lock-in when your vendor mix changes.