Telecom runs on margins, scale, and uptime. We build churn prediction, network anomaly detection, and SI-driven customer care that operate at carrier data volumes in real time, cutting cost-to-serve and protecting the SLAs your customers are paying for.

Get a production-readiness review What makes telecom SI hard

01 · Use cases

Six ways we deploy SI in telecom.

From churn prediction to network anomaly detection, every engagement is built for the data volumes, latency requirements, and regulatory constraints of telecom.

01 · Use case

Churn Prediction & Retention

ML models that identify at-risk subscribers before they cancel. Triggered retention campaigns with predicted LTV scoring.

02 · Use case

Network Anomaly Detection

Real-time anomaly detection across network telemetry. Identify degradation, outages, and security incidents before customers complain.

03 · Use case

SI Customer Care

LLM agents trained on your support playbooks, billing systems, and network status APIs. Resolve common issues without human escalation.

04 · Use case

Predictive Maintenance

Failure prediction models for network infrastructure. Identify equipment likely to fail before it causes downtime.

05 · Use case

ARPU Optimisation

Propensity models for upsell, cross-sell, and plan migration. Personalised offers served at the right moment in the customer journey.

06 · Use case

Document & Contract Intelligence

RAG over roaming agreements, regulatory submissions, and SLAs. Answer questions over your internal document corpus instantly.

02 · What makes telecom SI hard

Four challenges we solve first.

Data volume at telecom scale
Billions of network events per day. SI pipelines need streaming ingestion and efficient feature stores, not batch jobs running overnight.
Real-time latency requirements
Fraud detection, anomaly alerts, and churn signals need sub-second inference. We build for streaming, not dashboards.
Legacy system integration
Telecom runs on OSS/BSS systems that are decades old. We integrate SI layers without requiring infrastructure replacement.
Regulatory data residency
Telecom data is subject to strict jurisdiction requirements. We design data architectures that enforce residency from the start.

Proof, not promises

Twenty-plus SI systems shipped to production. One playbook, six industries, and a team that stays past launch.

SI systems live in production: 20+
Senior engineers & researchers: 21+
Upwork rating, 3 verified reviews: 5.0
Founded in Lahore: 2022

Verified Upwork clientSI product engagement · 5-star review

03 · Common questions

Before you brief us.

Can you process network telemetry at scale?: Yes. We build streaming pipelines on Kafka, Flink, and Spark Streaming designed for the event volumes telecom infrastructure generates. Feature engineering happens in-stream, not in batch.
How do you integrate with existing OSS/BSS systems?: We build SI feature layers that sit alongside your existing systems via APIs and event streams. We don't require a core systems migration; we work with what you have.
What's a realistic churn reduction from an ML model?: In our engagements, well-scoped churn models targeting the top decile of at-risk subscribers typically recover 15–30% of that cohort through triggered retention. Results depend heavily on the quality of historical data and the retention tooling available.
How do you handle multi-jurisdiction data residency requirements?: We design data architectures with residency as a first-class constraint: regional data stores, jurisdiction-aware routing, and audit logs that satisfy regulatory review. This is specified in the architecture phase, not retrofitted.

Available for new engagements

Book a free telecom SI scoping call.

30 minutes, one of our seniors, no slide deck. By the end of the call you will know whether we are the right team, and if not, who is.

Get a production-readiness review Email info@octalcode.com

Or call +1 (512) 710-5701

Let's scope it.Instant answers · free project scoping