May 2024 — Nov 2024
AI/ML PRODUCT ENGINEER
Architected and built GingrDistribution, a cloud-native, production-grade distribution management platform with end-to-end workflows for stock, dispatch, vendors, and real-time geo-tracking. Integrated AI/ML pipelines for ETA prediction, driver analytics, and route optimization. Designed a microservices, event-driven backend with secure authentication, RBAC, caching, and optimized data schemas. Hybrid databases (PostgreSQL, DynamoDB), contributing directly to company revenue through active SME deployments.
Year :
2025
Industry :
Product Based Company
Company :
Gingr Informatics Pvt Ltd, Tamil Nadu
Internship Duration :
Current
Problem :
Distribution businesses were struggling with fragmented operations, lack of real-time visibility, and manual coordination across stock management, dispatch, vendor workflows, and delivery tracking. Delays in shipments, inefficient route planning, lost or misplaced goods, and blind spots in driver activities created operational bottlenecks, revenue leakage, and poor customer satisfaction. Existing tools were disconnected, often relying on spreadsheets, phone calls, or manual tracking, making it impossible for managers to monitor operations or make data-driven decisions in real time.
Solution :
To address these challenges, I architected and developed GingrDistribution—a cloud-native, production-grade distribution management platform that unifies stock management, dispatch automation, vendor workflows, and real-time geo-tracking. I implemented AI/ML pipelines for ETA prediction, driver analytics, route anomaly detection, and operational forecasting, enabling data-driven decision-making and operational optimization. The system features a microservices, event-driven backend with secure authentication, RBAC, optimized data schemas, and distributed caching. I independently developed the full-stack platform using React/Next.js/Vite/shadcn for the frontend and Node.js/TypeScript/Python microservices for the backend, with hybrid databases (PostgreSQL, DynamoDB) to ensure high performance and scalability. GingrDistribution now empowers SMEs and distribution businesses with real-time visibility, streamlined operations, and measurable revenue impact, serving as a core product generating direct company income.
Challenge :
While building GingrDistribution, I faced multiple challenges: designing a fully integrated system to manage stock, dispatch, vendors, and real-time deliveries; implementing complex AI/ML pipelines for ETA prediction and driver analytics; architecting a scalable, event-driven, microservices backend with secure authentication, RBAC, and optimized data schemas; integrating hybrid databases (PostgreSQL, DynamoDB) for high performance; and developing the full frontend and backend independently while ensuring production-grade reliability, real-time responsiveness, and scalability for multiple SME clients.
Summary :
Architected and built GingrDistribution, a cloud-native, AI/ML-powered distribution platform with end-to-end workflows for stock, dispatch, vendor management, and real-time tracking—empowering SMEs with operational visibility, optimized routing, and direct revenue impact.
May 2024 — Nov 2024
AI/ML PRODUCT ENGINEER
Architected and built GingrDistribution, a cloud-native, production-grade distribution management platform with end-to-end workflows for stock, dispatch, vendors, and real-time geo-tracking. Integrated AI/ML pipelines for ETA prediction, driver analytics, and route optimization. Designed a microservices, event-driven backend with secure authentication, RBAC, caching, and optimized data schemas. Hybrid databases (PostgreSQL, DynamoDB), contributing directly to company revenue through active SME deployments.
Year :
2025
Industry :
Product Based Company
Company :
Gingr Informatics Pvt Ltd, Tamil Nadu
Internship Duration :
Current
Problem :
Distribution businesses were struggling with fragmented operations, lack of real-time visibility, and manual coordination across stock management, dispatch, vendor workflows, and delivery tracking. Delays in shipments, inefficient route planning, lost or misplaced goods, and blind spots in driver activities created operational bottlenecks, revenue leakage, and poor customer satisfaction. Existing tools were disconnected, often relying on spreadsheets, phone calls, or manual tracking, making it impossible for managers to monitor operations or make data-driven decisions in real time.
Solution :
To address these challenges, I architected and developed GingrDistribution—a cloud-native, production-grade distribution management platform that unifies stock management, dispatch automation, vendor workflows, and real-time geo-tracking. I implemented AI/ML pipelines for ETA prediction, driver analytics, route anomaly detection, and operational forecasting, enabling data-driven decision-making and operational optimization. The system features a microservices, event-driven backend with secure authentication, RBAC, optimized data schemas, and distributed caching. I independently developed the full-stack platform using React/Next.js/Vite/shadcn for the frontend and Node.js/TypeScript/Python microservices for the backend, with hybrid databases (PostgreSQL, DynamoDB) to ensure high performance and scalability. GingrDistribution now empowers SMEs and distribution businesses with real-time visibility, streamlined operations, and measurable revenue impact, serving as a core product generating direct company income.
Challenge :
While building GingrDistribution, I faced multiple challenges: designing a fully integrated system to manage stock, dispatch, vendors, and real-time deliveries; implementing complex AI/ML pipelines for ETA prediction and driver analytics; architecting a scalable, event-driven, microservices backend with secure authentication, RBAC, and optimized data schemas; integrating hybrid databases (PostgreSQL, DynamoDB) for high performance; and developing the full frontend and backend independently while ensuring production-grade reliability, real-time responsiveness, and scalability for multiple SME clients.
Summary :
Architected and built GingrDistribution, a cloud-native, AI/ML-powered distribution platform with end-to-end workflows for stock, dispatch, vendor management, and real-time tracking—empowering SMEs with operational visibility, optimized routing, and direct revenue impact.
May 2024 — Nov 2024
AI/ML PRODUCT ENGINEER
Architected and built GingrDistribution, a cloud-native, production-grade distribution management platform with end-to-end workflows for stock, dispatch, vendors, and real-time geo-tracking. Integrated AI/ML pipelines for ETA prediction, driver analytics, and route optimization. Designed a microservices, event-driven backend with secure authentication, RBAC, caching, and optimized data schemas. Hybrid databases (PostgreSQL, DynamoDB), contributing directly to company revenue through active SME deployments.
Year :
2025
Industry :
Product Based Company
Company :
Gingr Informatics Pvt Ltd, Tamil Nadu
Internship Duration :
Current
Problem :
Distribution businesses were struggling with fragmented operations, lack of real-time visibility, and manual coordination across stock management, dispatch, vendor workflows, and delivery tracking. Delays in shipments, inefficient route planning, lost or misplaced goods, and blind spots in driver activities created operational bottlenecks, revenue leakage, and poor customer satisfaction. Existing tools were disconnected, often relying on spreadsheets, phone calls, or manual tracking, making it impossible for managers to monitor operations or make data-driven decisions in real time.
Solution :
To address these challenges, I architected and developed GingrDistribution—a cloud-native, production-grade distribution management platform that unifies stock management, dispatch automation, vendor workflows, and real-time geo-tracking. I implemented AI/ML pipelines for ETA prediction, driver analytics, route anomaly detection, and operational forecasting, enabling data-driven decision-making and operational optimization. The system features a microservices, event-driven backend with secure authentication, RBAC, optimized data schemas, and distributed caching. I independently developed the full-stack platform using React/Next.js/Vite/shadcn for the frontend and Node.js/TypeScript/Python microservices for the backend, with hybrid databases (PostgreSQL, DynamoDB) to ensure high performance and scalability. GingrDistribution now empowers SMEs and distribution businesses with real-time visibility, streamlined operations, and measurable revenue impact, serving as a core product generating direct company income.
Challenge :
While building GingrDistribution, I faced multiple challenges: designing a fully integrated system to manage stock, dispatch, vendors, and real-time deliveries; implementing complex AI/ML pipelines for ETA prediction and driver analytics; architecting a scalable, event-driven, microservices backend with secure authentication, RBAC, and optimized data schemas; integrating hybrid databases (PostgreSQL, DynamoDB) for high performance; and developing the full frontend and backend independently while ensuring production-grade reliability, real-time responsiveness, and scalability for multiple SME clients.
Summary :
Architected and built GingrDistribution, a cloud-native, AI/ML-powered distribution platform with end-to-end workflows for stock, dispatch, vendor management, and real-time tracking—empowering SMEs with operational visibility, optimized routing, and direct revenue impact.