Case studies

The problem

A top-five bank processed 2M+ daily transactions with a rule-based fraud system. The 12% false positive rate meant the fraud team spent most of their time reviewing legitimate transactions. Real fraud was slipping through.

What we built

We built a real-time ML pipeline: gradient-boosted trees for known fraud patterns, plus deep anomaly detection for novel attacks. The system scores every transaction in under 50ms and retrains weekly on newly flagged data.

What changed

• Detection accuracy went from ~88% to 99.7%
• False positives dropped 45%
• Sub-50ms latency per transaction
• Fraud team refocused on investigation, not triage

The problem

200+ stores with inconsistent inventory. Overstocking cost millions in waste while stockouts lost revenue. Manual forecasting couldn’t handle seasonal patterns, promotions, and regional differences.

What we built

Multi-variate demand forecasting combining time series, weather, promotional calendars, and local economic data. Integrated directly with the existing ERP for automated purchase orders.

What changed

• 35% improvement in inventory accuracy
• Significant reduction in waste and stockouts
• Automated replenishment across all stores
• Paid for itself within 3 months

The problem

Monthly churn was eating into revenue. Retention campaigns were spray-and-pray — targeting everyone instead of the customers actually at risk of leaving.

What we built

Built a churn prediction model using behavior data, usage patterns, support interactions, and sentiment from call transcripts. Integrated with CRM to trigger targeted retention offers automatically.

What changed

• 94% prediction accuracy at 30-day window
• 22% improvement in retention rate
• Retention spend became 40% more efficient
• NPS improved as offers became relevant

The problem

Unplanned downtime on critical production lines was extremely costly. Maintenance was either reactive (too late) or calendar-based (wasteful). 15+ sensor types with no unified monitoring.

What we built

IoT sensor integration with real-time anomaly detection using LSTM networks. Predicts failures 7-14 days in advance, giving maintenance teams time to plan repairs during scheduled downtime.

What changed

• 73% reduction in unplanned downtime
• Failures predicted 7-14 days in advance
• Unified monitoring dashboard for all lines
• Extended average equipment lifespan by 20%