Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
Telecom runs on margins, scale, and uptime. We build churn prediction, network anomaly detection, and AI-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.
From churn prediction to network anomaly detection every engagement is built for the data volumes, latency requirements, and regulatory constraints of telecom.
ML models that identify at-risk subscribers before they cancel. Triggered retention campaigns with predicted LTV scoring.
Real-time anomaly detection across network telemetry. Identify degradation, outages, and security incidents before customers complain.
LLM agents trained on your support playbooks, billing systems, and network status APIs. Resolve common issues without human escalation.
Failure prediction models for network infrastructure. Identify equipment likely to fail before it causes downtime.
Propensity models for upsell, cross-sell, and plan migration. Personalised offers served at the right moment in the customer journey.
RAG over roaming agreements, regulatory submissions, and SLAs. Answer questions over your internal document corpus instantly.
Billions of network events per day. AI pipelines need streaming ingestion and efficient feature stores, not batch jobs running overnight.
Fraud detection, anomaly alerts, and churn signals need sub-second inference. We build for streaming, not dashboards.
Telecom runs on OSS/BSS systems that are decades old. We integrate AI layers without requiring infrastructure replacement.
Telecom data is subject to strict jurisdiction requirements. We design data architectures that enforce residency from the start.
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.
We build AI 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.
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.
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.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.