Demonstrated in production with Evamp & Saanga
An operator's data already knows which subscriber is on the wrong bundle, which one is drifting toward churn, and where the network under-serves demand. This platform turns that data into decisions — subscriber intelligence, personalised recommendations, geospatial analytics, and agentic customer support in one enterprise AI layer.
Interface shown is a representative demonstration.
The Showcase
Delivered with Evamp & Saanga for a telecommunications provider in the Middle East: an intelligent bundle-recommendation system that replaced one-size-fits-all promotions with per-subscriber AI.
Per-subscriber analysis: voice and data consumption, behavioural trends, purchasing history, and preferences — scored continuously, per SIM.
Personalised packages: the right voice, data, and digital-service bundle recommended to each individual — in the market benchmark, roughly one in three prepaid users sits on the wrong bundle for their actual usage.
Commercial impact: targeted recommendations lift package conversion, customer satisfaction, and retention — revenue from relevance, not promotion volume.
Multilingual by design: the deployed experience runs bilingually — the same architecture extends to any market's language mix.
The Platform
The bundle-recommendation system is one implementation of a broader Telecommunications AI Platform — a modular ecosystem combining LLMs, multi-agent systems, predictive analytics, and geospatial intelligence, built for mobile operators, broadband providers, and enterprise networks. Cloud or on-premise, integrated with the operator's own systems of record.
Behaviour, usage history, location, and purchasing patterns scored into the most appropriate voice, data, roaming, and digital packages per customer.
Engagement, service usage, and spending habits built into a full behavioural picture — lifting customer lifetime value and retention.
Subscriber behaviour overlaid on coverage, regional demand, and infrastructure performance — where to invest, where to expand, where revenue leaks.
Multilingual conversational agents handling billing, package management, troubleshooting, and account services — escalating to humans with full context.
Real-time performance monitoring, anomaly detection, fault prediction, and AI-assisted diagnostics with recommended corrective actions.
Automated reports, KPI dashboards, and conversational business intelligence — leadership interrogating enterprise data in natural language.
Case Scenarios
The platform is designed so new AI services deploy on the same engine and the same data foundation. Telco benchmarks put 25–40% of inbound customer contacts within reach of well-built AI agents — and that's one module of ten.
Spot subscribers drifting toward leaving weeks before they do — and the targeted action most likely to keep them.
Network demand forecast from subscriber growth, traffic patterns, and seasonal behaviour — capex planned ahead of congestion.
Unusual subscriber behaviour, billing anomalies, and operational risk surfaced by predictive monitoring.
AI copilots for engineering, care, retail, and management — knowledge retrieval and workflow automation per function.
Technical documentation, procedures, and organisational expertise turned into searchable AI assistants (RAG).
What a deployment changes
Package conversion and subscriber retention up — promotions replaced by per-SIM relevance.
First-contact resolution up and support cost down, with humans reserved for the cases that need them.
Network investment decisions grounded in demand geography rather than averages.
One data foundation: each new module compounds the value of the last, without redesign.
Why It Matters
Telecommunications providers already own the richest behavioural dataset in any market — usage, location, payments, contacts. What's usually missing is the layer that turns it into next-best actions across commercial, network, and care teams. This platform is that layer: production AI engineered for enterprise environments, delivered from research to deployment, and built to evolve with the operator as new use cases emerge.
Engineered and deployed by Aliera's AWRTD teams — agentic LLM systems, evaluation harnesses, and data pipelines integrated into production customer-facing operations.
Platform interfaces shown are representative demonstrations — client data and production dashboards remain confidential under NDA.
We build the intelligence layer on the systems you already run — starting with a measurable pilot, not a platform migration. Tell us where it hurts.