API Economy • Health Care

API-led Connectivity Approach for an Online Medical Device Retailer

Developed an e-commerce website and simplified complex, time-consuming data transformations from disparate systems using reusable API components

Key Metrics

2.5x Faster Project Delivery
12+ Dependency Systems Integrated
10% time spent on API Innovation
42% efficiency gains

Built a CMS-based e-commerce platform with all Cloud, ERP, CRM, and supply chain ecosystem integrations to streamline everything from ordering, shipping to user communication. Exposed dependant systems and data assets as self-service APIs and eliminated unnecessary coding. Enabled payment gateways, real-time tax calculation, premium subscription models, and wallet functionality for seamless checkouts. Designed transactional and marketing automatic email workflows.

Key Features

SAP ERP & Salesforce CRM
OMS, TMS, and WMS
Payment Gateways
Google Analytics
SendGrid Mail Delivery

Key Metrics

2.5x Faster Project Delivery
12+ Dependency Systems Integrated
10% time spent on API Innovation
42% efficiency gains

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.