Telco · MVNO/MVNE
Conduit
ML feature pipeline for tier 2 MVNO/MVNE operators — turning raw CDRs into production-grade behavioural signals.
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Resumen
Conduit is a machine learning feature generation platform designed for MVNO and MVNE operators running on constrained data estates. It ingests raw Call Detail Records (CDRs) and transforms them into subscriber behavioural signals that feed production churn prediction and revenue assurance models.
Resultado
Replaced a third-party analytics dependency for a Tier 2 operator. The operator now runs churn prediction and revenue assurance from their own data estate, with full visibility into the feature pipeline.
What it does
Conduit ingests raw CDR streams and applies a configurable feature engineering layer to extract subscriber-level behavioural signals: call patterns, data consumption trends, roaming activity, and ARPU trajectory.
The pipeline is operator-parameterised — signal definitions are configured to match the operator’s network topology and commercial segments, not adapted from generic telecom templates.
Where it fits
Conduit sits between raw network data and the models that consume it. It is not a BI dashboard or a reporting tool. It is the data engineering layer that makes ML feasible for operators without a dedicated data science team.
Built for Tier 2/3 operators
Mid-tier operators run leaner data teams and typically depend on third-party analytics platforms that don’t expose the data needed to train custom models. Conduit removes that dependency by generating features from the operator’s own CDR estate.
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