Deployed Microservices Architecture for a Global Retail Group
Developed a deployment solution using Kubernetes to significantly reduce the customer's turnaround time
Key Metrics
90% Reduction in customer onboarding time
75% Reduction in pipeline deployment time
50% Reduction in infrastructure costs
24*7 monitoring of Kubernetes Cluster
The architecture built using Kubernetes helped the client to isolate the customer environments with a single deployment solution. The creation of clusters was automated based on customer categories and monitored 24*7 using Datadog for any alerts and notifications. CI/CD automation was achieved using Jenkins from Helm to achieve high availability in case of server failure. Leveraged Kubernetes for secrets management and end-to-end testing of the solution was performed with Pack broker.
Key Features
Microservices Architecture
High Availability for CI/CD Server
24*7 Monitoring
Sensitive Data Management
Integration Testing for Microservices
Key Metrics
90% Reduction in customer onboarding time
75% Reduction in pipeline deployment time
50% Reduction in infrastructure costs
24*7 monitoring of Kubernetes Cluster
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
Hey, we are still working on those cases, they will be up soon
Please check out our other case studies in the meanwhile.
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