Proof Points

Unified Data Lake & Analytics Platform

Problem: Disparate, siloed data sources were preventing unified analytics and ML model training.

Approach: Designed and deployed a scalable, cloud-native data lake with automated ETL pipelines, robust data governance, and a unified query layer.

Outcome: Reduced query times by 90%, enabled cross-functional analytics, and cut data infrastructure costs by 40%.

ML-Powered Predictive Maintenance

Problem: Unscheduled downtime of industrial machinery was leading to significant revenue loss.

Approach: Developed and deployed a real-time predictive maintenance model using sensor data, leveraging LSTM networks to forecast failures before they occurred.

Outcome: Increased asset uptime by 25%, reduced emergency maintenance costs by 30%, and achieved 92% prediction accuracy for critical failures.

Automated Image Recognition for QC

Problem: A manual, error-prone quality control process for a manufacturing line was resulting in high defect rates.

Approach: Built and trained a custom Convolutional Neural Network (CNN) model for real-time image recognition to identify product defects on the assembly line.

Outcome: Automated 98% of the inspection process, reduced the final product defect rate by 85%, and increased production throughput by 15%.