Case Studies

Mobile App Development • Retail & eCommerce

Intuitive Shopfloor Management Mobile App for a Toy Manufacturer

Developed a cross-platform mobile app to automate and reduce human involvement in monitoring, operating, and scheduling manufacturing machines.
Key Metrics
  • 45% improvement in Production Planning Efficiency
  • 3x output through optimal Resource Scheduling
  • 65% reduction in unplanned downtimes
  • 100% paperless manufacturing

The app was built for remote monitoring and controlling plastic molding machines. The need for physical involvement was eliminated using automated workflows, beacons, and QR code scanners. Enabled operators to access their tasks, operate the machine with task-specific code and update the progress via the app. Integrated with peripheral production systems, combined multiple data sources, REST APIs using AWS AppSync GraphQL mutations. Created in-app analytics for tracking and optimization.

key Features
  • NFC & Barcode Scanning
  • Real-time Data Sync
  • Internationalization
  • Notifications & Alerts
  • Inspection & Audit Trail
Key Metrics
  • 45% improvement in Production Planning Efficiency
  • 3x output through optimal Resource Scheduling
  • 65% reduction in unplanned downtimes
  • 100% paperless manufacturing
AI & ML Automation • Manufacturing

AWS Sagemaker based Computer Vision Solution for a Manufacturer

Developed object detection at the Edge using AWS consumable production-ready services like Sagemaker, Groundtruth, IoT Greengrass, S3, and Lambda.
Key Metrics
  • 80% efforts reduced in data annotation & labeling
  • 65% time saved in ML Modelling
  • 54% lower total cost of ownership
  • One-click deployment to the cloud

Client is required to submit the old physical devices for the new inventory fulfillment request. They wanted to replace this time-consuming process with computer vision. Configured Raspberry Pi device and made it interact with IoT Greengrass Core using Lambda. Captured images, annotated using Groudtuth, and loaded into S3 buckets. Built ML model using AWS Sagemaker built-in algorithm XGBoost. Deployed model back on Greengrass device using Lambda for object detection. Established ETL for database transformation and enabled Quick Sight insights.

key Features
  • Raspberry Pi Device Configuration
  • Video to Frames Conversion
  • Auto Annotation & Data Labelling
  • AWS Built-in-Algorithms
  • Image Classification Model on Edge
Key Metrics
  • 80% efforts reduced in data annotation & labeling
  • 65% time saved in ML Modelling
  • 54% lower total cost of ownership
  • One-click deployment to the cloud
Data Management • Health Care

Member Data Management and Extraction from Payer Systems

Managed Facets administrative system from backend and created procedures to extract and manage data
Key Metrics
  • 72% reduction in Data extraction time
  • 98% data validation accuracy achieved
  • 24 hour support for data warehouse management
  • 20% reduction in manual effort through job automation

To meet the CMS compliance and data analytics requirements – member, provider, and claims data need to be processed from multiple tables. We performed data masking, anonymization, cross-referencing, duplicate removal, and business level validations at various levels. Next, we converted the data into the required format, including .txt, EDI, JSON, and other related formats. Finally, we created a data warehouse to store the data and data marts to create analytics on top of the data.

key Features
  • Rearchitect The System Design with Modern Tech Stack
  • Migration of Millions of Healthcare Records into Azure
  • Automated Data Validations
  • Data Warehouse Management
  • Custom File Formatting
Key Metrics
  • 72% reduction in Data extraction time
  • 98% data validation accuracy achieved
  • 24 hour support for data warehouse management
  • 20% reduction in manual effort through job automation

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