Industry Experience

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Network Traffic Forecasting

Client: European telecom operator

The challenge
Capacity planning was largely reactive and driven by rule-based monitoring. This led to inefficiencies and frequent last-minute interventions.

Our role
We embedded two ML engineers directly into the client’s data platform team.

What we delivered

• Time-series models to forecast network traffic
• Integration of live telemetry data into the forecasting pipeline
• Automated retraining and performance monitoring

The outcome

• ~18% improvement in forecast accuracy
• ~12% reduction in last-minute capacity interventions
• Full production rollout within three months

Credit Risk Model Productionization

Client: EU digital lending platform

The challenge
The risk model was already built, but the production infrastructure behind it wasn’t stable. As a result, performance and reliability were inconsistent.

Our role
We embedded an ML engineer and a data engineer directly into the client’s risk team.

What we delivered

• Rebuilt the training and inference pipelines
• Implemented a proper feature store and model versioning
• Added an explainability layer to support transparency and compliance

The outcome

• ~35% faster credit decision times
• Fewer production model failures
• Retraining cycles reduced from days to hours

Shipment Volume Forecasting & Capacity Planning

Client: Regional logistics operator

The challenge
Shipment forecasts were often inaccurate, leading to warehouse bottlenecks and inefficient fleet allocation—especially during peak periods.

Our role
We embedded an ML engineer and a data engineer directly into the planning team.

What we delivered

• Time-series models to forecast shipment demand
• Integration of operational and seasonal data sources
• An automated forecasting pipeline with reporting dashboards
• Ongoing monitoring to detect forecast drift

The outcome

• ~15% improvement in forecast accuracy
• More efficient fleet utilization during peak periods
• Less manual effort in day-to-day planning

Predictive Maintenance System

Client: Energy distribution operator

The challenge
Large volumes of sensor data were being collected, but none of it was being used in a structured, predictive way. Maintenance remained largely reactive.

Our role
We deployed a small ML engineering pod — two ML engineers and one cloud engineer — working closely with the client’s internal teams.

What we delivered

• Anomaly detection models to identify early signs of failure
• Data ingestion pipelines and a real-time alerting system
• Direct integration with existing maintenance workflows

The outcome

• ~15–20% reduction in unplanned downtime
• More efficient and predictable maintenance planning
• Full operational rollout within nine months

AML Alert Optimization

Client: Mid-sized European bank

The challenge
The bank’s AML transaction monitoring system was generating too many false positives, overwhelming the compliance team. Updating models was slow, and validation for regulatory review was complex and time-consuming.

Our role
We embedded a senior ML engineer directly into the risk analytics team.

What we delivered

• Rebuilt the transaction scoring pipeline with stronger feature engineering
• Implemented a structured model versioning and validation framework
• Added an explainability layer to support audit and regulatory transparency
• Automated retraining and performance monitoring

The outcome

• ~20% reduction in false positives
• Faster model validation during regulatory reviews
• Lower manual workload for the compliance team