SAMATA BAG

Frontend Developer

IIEST,Shibpurfull_time, internship, freelance

Open to roles

ReactHTML/CSSTailwind CSSTypeScript

ExpiryIQ AI

Thank you for reviewing ExpiryIQ AI. ExpiryIQ AI is an enterprise-grade AI-powered Business Record Lifecycle Management platform designed to solve a common problem faced by organizations—tracking critical business documents before they expire. Instead of functioning as a simple document tracker, the platform acts as an Executive Decision Intelligence System, providing real-time business visibility, predictive analytics, AI-powered recommendations, and risk assessment. Key Highlights: • AI Executive Dashboard with real-time business health monitoring • Intelligent expiry classification (Active, Expiring Soon, Critical, Expired) • Enterprise Digital Twin for organization-wide document visibility • AI Scenario Simulator to predict business impact of missed renewals • Executive Boardroom Mode for leadership presentations • Vendor Intelligence & Department Risk Analytics • Compliance Heat Maps & Risk Matrix • AI Copilot capable of filtering, searching, generating reports, and assisting decision-making • Predictive renewal forecasting and compliance analytics • Beautiful enterprise-grade UI with premium animations, glassmorphism, and responsive design The project was designed with scalability and real enterprise workflows in mind, focusing on usability, executive decision support, and business intelligence rather than simple CRUD operations. No additional setup is required. The application is fully deployed and ready for evaluation. Thank you for your time and consideration. GITHUB:https://github.com/2024itb047samata/ExpiryIQ-AI

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ExpenseIQ-Smart Student Expense Analyzer

College students frequently spend money on food, travel, recharges, and daily necessities but often struggle to track their expenses. This makes budgeting difficult and leads to poor financial awareness. The objective of this project was to build a simple offline expense management system that not only records expenses but also provides spending analytics and financial insights to help students make better financial decisions. Process I started by designing a menu-driven command-line application using Python. First, I implemented the core expense tracking features, including adding expenses, viewing records, calculating total spending, and identifying the highest spending category. After completing the basic tracker, I enhanced the system with budget monitoring, category-wise spending reports, daily average spending calculations, largest expense detection, and a financial insights dashboard. Finally, I tested all features with sample data to ensure accurate calculations and smooth user interaction. Results Successfully developed a Smart Student Expense Analyzer that helps students track and analyze their spending habits. The application provides expense management, budget tracking, category-wise analysis, daily spending averages, largest expense identification, and financial insights. The project demonstrates practical use of Python data structures, functions, loops, conditionals, and analytical problem-solving techniques. Reflection In future versions, I would add data persistence using CSV or JSON files so expenses remain saved between sessions. I would also include monthly reports, graphical spending summaries and search/filter functionality to provide deeper insights into spending patterns.

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PulseOS: AI-Powered Hospital Command Center

Hospitals often struggle with long patient waiting times, inefficient queue management, and limited visibility into real-time operations. Staff must manually track patient flow, doctor availability, emergency cases, and resource utilization across multiple systems. This leads to delayed care, overcrowded waiting areas, poor patient experience, and inefficient resource allocation. Existing solutions provide fragmented data but lack a centralized intelligence platform that enables hospital administrators to make fast, data-driven decisions. Process We began by analyzing common operational challenges faced by hospitals, including patient congestion, emergency prioritization, and inefficient doctor allocation. We designed a centralized command-center dashboard to provide real-time visibility into hospital operations. Multiple dashboard layouts were prototyped before selecting a data-dense yet intuitive interface. We initially experimented with simple queue monitoring, but it lacked actionable insights. The solution evolved to include AI-powered health scoring, patient prioritization, queue analytics, doctor availability tracking, and operational performance metrics. Continuous iterations focused on improving usability, responsiveness, and information hierarchy to ensure administrators could identify bottlenecks within seconds. Results PulseOS successfully provides a unified operational view of hospital activities through a real-time command center. Administrators can monitor patient queues, doctor workloads, emergency cases, average wait times, and overall operational health from a single interface. The platform improves visibility, enables faster response to bottlenecks, supports data-driven resource allocation, and enhances hospital efficiency. The system demonstrates how modern analytics and intelligent monitoring can streamline healthcare operations while improving patient experience. Reflection Given additional development time, I would integrate real hospital datasets and live patient management systems to validate the platform at scale. I would also add predictive patient inflow forecasting, automated staff scheduling recommendations, and mobile notifications for operational alerts. Further usability testing with healthcare administrators would help refine workflows and improve decision-support capabilities for different hospital environments.

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