Jeevarathinam Velmurugan@jeevarathinam1930489
1
Events attended
1
Submissions made
India
1 year of experience
About me
I am an Electronics and Communication Engineering student with a strong interest in Artificial Intelligence, Edge AI, embedded systems, computer vision, and innovative technology solutions. I enjoy working on real-world problems and transforming ideas into practical, technology-driven solutions. My technical experience includes working with Python, C/C++, machine learning, computer vision, IoT, React Native, Node.js, cloud technologies, and AI models such as YOLO and MobileNet. I have also explored hardware platforms such as NVIDIA Jetson, sensors, drones, and embedded systems. I have worked on several projects involving drone-based AI systems, predictive maintenance, PCB defect detection, AI-powered applications, freight forecasting, and intelligent decision-support systems. Through hackathons and innovation challenges, I have gained experience in problem analysis, rapid prototyping, system architecture, product design, and presenting technical solutions. One of my key strengths is connecting hardware, software, and AI to build solutions for practical problems. I am particularly interested in developing systems that are not only technically innovative but also useful, scalable, and deployable in real-world environments. I consider myself a continuous learner who enjoys exploring emerging technologies, experimenting with new ideas, and working on challenging problems. My long-term goal is to become a technology professional capable of building AI-driven engineering solutions that create measurable real-world impact.
🤝 Top Collaborators
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🤓 Latest Submissions
SteelSync AI – Multi-Agent Logistics Workflow
SteelSync AI is a multi-agent logistics orchestration platform built for the Band of Agents Hackathon. The system demonstrates how specialized AI agents can communicate and collaborate to automate logistics workflows. The platform consists of six independent agents: Commander Agent, ERP Validator Agent, Router Agent, Inventory Agent, Demurrage Agent, and Scenario Agent. Instead of relying on a single AI model, each agent is responsible for a specific logistics function and communicates through the Band platform. The workflow begins when a shipment request is received. The ERP Validator Agent verifies shipment information and checks for missing or invalid fields. The Router Agent determines the transportation mode. The Inventory Agent checks stock availability and resource readiness. The Demurrage Agent estimates potential storage and detention risks. Finally, the Scenario Agent generates best-case, expected-case, and worst-case outcomes for the shipment. The Commander Agent orchestrates the complete workflow, tracks progress, receives outputs from all agents, and generates the final logistics report. The project demonstrates agent-to-agent communication, workflow coordination, task delegation, distributed decision-making, and multi-agent collaboration using Band of Agents, Gemini, LangChain, and LangGraph. SteelSync provides a foundation for future logistics automation systems where multiple intelligent agents cooperate to improve shipment planning, inventory visibility, operational efficiency, and supply chain decision support.
19 Jun 2026
