EduCare: AI EdTech Command Center for Personalized Learning - @ Google AiStudio - Live Demo

Project Overview

The EduCare AI Edtech SaaS Dashboard was developed to serve as the central intelligence hub for an adaptive learning platform. Built with React, TypeScript, and Tailwind CSS, the dashboard provides students, educators, and administrators with a 360-degree view of learning progression, behavioral habits, and AI-driven insights, replacing static reports with dynamic, actionable data visualizations.

Technology Stack: React (with TypeScript), Tailwind CSS, D3.js/Recharts, Internal AI Model API Integration 

My Role: Lead Front-End Architect, Data Visualization Specialist, UX/UI Designer

1. The Problem: Generic Learning Paths and Data Overload

In the modern education landscape, educators are drowning in raw data (quiz scores, time spent, assignment completion) without the tools to convert it into personalized learning strategies. The core challenges were:

• Lack of Contextual Insight: Existing systems provided grades but failed to identify the root cause of low performance (e.g., a specific knowledge gap, time management issues, or engagement fatigue).

• Inefficient Teacher Intervention: Teachers spent excessive time manually cross-referencing disparate data points, delaying timely intervention for at-risk students.

• Low Student Self-Efficacy: Students lacked a clear, digestible view of their own progress and next best steps, leading to passive consumption rather than proactive engagement.

The mandate was to leverage AI to distill complex learning data into three clear outputs: performance, predictive risk, and personalized tasks.

2. The Solution: Predictive and Prescriptive Learning Intelligence

The EduCare dashboard was architected as a fast, responsive Single Page Application (SPA) designed for multi-stakeholder use (Student, Teacher, Admin).

• AI-Driven Mastery Scoring: Implemented rich visualizations (e.g., radar charts and sunburst diagrams) to map performance against specific competencies, showcasing not just what a student scored, but their current Mastery Level and identified Knowledge Gaps.

• Study Habit Analysis: Integrated custom charts to visualize behavioral data (time-on-task, study session consistency, content consumption patterns), allowing the AI to flag potential burnout or disengagement early.

• Predictive Risk Flagging: A prominent KPI card uses the AI model's output to assign a "Performance Risk Index," enabling teachers to instantly view and filter students who are statistically likely to fall behind in the next assessment.

• Adaptive Recommendation Engine: The dashboard serves up the AI’s prescriptive recommendations (e.g., "Review Module 3.2 on Supply Chain Fundamentals" or "Schedule 15 minutes of spaced repetition practice"), turning data analysis into an actionable to-do list.

3. The Design Process: The 360-Degree User Perspective

Our design methodology focused on delivering specialized data views for each user group while maintaining a consistent design system (facilitated by Tailwind CSS).

• Persona-Specific Dashboards:

  • Student View: Focused on motivation and clear next steps ("What should I do now?").

  • Teacher View: Focused on class-wide segmentation and intervention ("Who needs help and why?").

  • Admin View: Focused on longitudinal course efficacy and platform usage metrics.

• Data Translation: We employed TypeScript for strict data typing to ensure the mathematical accuracy of all performance metrics. A key design challenge was translating complex statistical scores (like the "Z-Score of Engagement") into simple, intuitive visual elements.

• Mobile-First Responsiveness: Given the high likelihood of student access via tablets and phones, the Tailwind CSS framework was critical in ensuring the dense data visualizations remained clean, legible, and fully responsive across all device breakpoints.

4. The Final Product: A Lift in Engagement and Performance

The EduCare AI Dashboard successfully centralized and personalized the learning experience. The tool provided clarity to students about their learning journey and efficiency to educators, shifting intervention from reactive grading to proactive coaching. Within the pilot program, data showed a:

• 15% Increase in Student Platform Engagement due to clearer, personalized goals.

• 12% Lift in Average Module Completion Rates for at-risk student groups.

• 20% Reduction in Teacher Preparation Time spent analyzing individual performance reports.

Real-Time Supply Chain Command Center: Predictive Analytics Dashboard - @ Google AiStudio

Project Overview

This project involved developing a high-performance, interactive analytics dashboard—replicating the visual sophistication and data density of commercial tools like Power BI—specifically tailored for global supply chain management. The application provides end-to-end visibility, anomaly detection, and AI-powered prescriptive insights to mitigate bottlenecks and optimize logistics costs.

Technology Stack: React (with TypeScript), Tailwind CSS, Gemini API (for prescriptive modeling and risk scoring), Recharts (for data visualization) My Role: Lead Front-End Architect, Data Visualization Specialist, AI Integration & UX Design

1. The Problem: Data Fragmentation and Decision Latency

Traditional supply chain reporting relies on static, daily, or weekly reports, leading to delayed decision-making. Operational teams faced three main challenges:

• Lagging Indicators: Data was retrospective, meaning bottlenecks (e.g., customs delays, inventory shortages) were only identified after they occurred, leading to high expedited shipping costs.

• Low Interactivity: Existing platforms were slow to filter and query, preventing users from quickly drilling down into specific regions, suppliers, or product SKUs.

• Lack of Foresight: Analysts spent excessive time manually correlating disparate metrics (weather data, geopolitical news, carrier performance) to forecast risk, a task unsuited for human processing speed.

The core challenge was to transform passive data viewing into active, real-time operational control.

2. The Solution: Real-Time, Prescriptive Analytics

The Supply Chain Command Center Dashboard was built to provide superior performance and integrate deep AI intelligence directly into the user workflow.

• Custom Power BI Replication: Engineered a highly performant front-end using React and TypeScript to handle massive data sets and complex visualizations (Sankey diagrams, heatmaps, geo-spatial tracking) with zero noticeable rendering lag.

• Gemini Predictive Risk Score: Instead of generic alerts, the Gemini API analyzes live telemetry, global news feeds, and historical patterns to generate a "Shipment Risk Score" (0-100) for all in-transit inventory, predicting the probability of delay or loss within the next 72 hours.

• Prescriptive Recommendations: The AI feature includes a natural language summary that offers prescriptive actions for high-risk shipments (e.g., "Recommend shifting Supplier X components from Port A to Port B due to predicted labor strike activity").

• Tailwind UI Flexibility: Utilizing Tailwind CSS allowed for a highly responsive, modular design, letting users save customized views and pin essential KPI cards (e.g., OTIF: On-Time, In-Full rate) based on their specific roles (e.g., procurement vs. logistics).

3. My Design Process: Actionability Over Density

The design methodology focused on synthesizing complex data into actionable steps, prioritizing a modern, customizable aesthetic.

• UX Research: Role-Based Dashboards: Conducted interviews with supply chain managers and operational staff to map specific information requirements by role, which informed the creation of distinct, interchangeable widget sets.

• Data Hierarchy & Visual Language: Established a clear visual hierarchy where the most important metric (Risk Score) is prominent, followed by geographical context (map) and then granular detail (tables). Used a consistent, intuitive color palette (Green for efficiency, Yellow for moderate risk, Red for critical) to communicate status at a glance.

• Interactivity & Performance: Optimized data fetching and rendering logic (using memoization and debouncing in React) to ensure filters and drill-downs executed sub-second response times, crucial for real-time operations.

• Information Design: Used the Recharts library to build custom, highly readable charts that dynamically update the visual context based on user-defined filters, mimicking the smooth interaction of enterprise BI tools.

4. The Final Product: Optimized Logistics and Cost Savings

The Supply Chain Command Center Dashboard delivered an enterprise-grade analytics experience with the agility of a custom application. It provided logistics teams with the predictive power needed to move from reactive mitigation to proactive optimization, leading to an estimated 15% reduction in expedited shipping costs and a significant improvement in on-time delivery metrics during the pilot phase. The product sets a new standard for performance and intelligence in supply chain visibility.

Product Title, Description, and Core Features

• Product Title: AI Smart TV Remote Pro+

• Description: A responsive, AI-driven universal touch TV remote application designed to streamline media consumption. It replaces physical remotes and fragmented app experiences by consolidating control, leveraging predictive intelligence, and prioritizing extensive accessibility features for a truly personalized and effortless viewing journey.

• Technology Stack:

  • AI/ML: Google AI Studio (for content recommendation models and NLP-based predictive search).

  • Front-End: React Native / TypeScript (for cross-platform compatibility and responsiveness).

  • Connectivity: Wi-Fi Direct and Bluetooth Low Energy (BLE) for reliable device pairing.

  • Data Storage: Firestore (for storing user preferences, accessibility profiles, and shortcut history).

1. The Problem: Fragmentation and User Fatigue

The modern viewing experience is cluttered. Users juggle multiple physical remotes, navigate complex on-screen interfaces, and switch between numerous OTT (Over-The-Top) streaming apps to find content. The problems identified were:

• Discovery Friction: Users spend more time searching than watching due to disconnected platforms.

• Physical Barrier: The reliance on physical remotes offers poor input mechanisms (slow typing) and lacks the contextual intelligence modern users expect.

• Accessibility Gap: Standard remote designs often overlook users with visual, motor, or cognitive impairments, creating an exclusive experience.

The goal was to create an intelligent, inclusive, and friction-free remote experience.

2. The Solution: Predictive, Unified, and Accessible Control

The AI Smart TV Remote Pro+ unifies control and intelligence into a single mobile application, significantly simplifying content access and navigation.

• AI-Powered Predictive Search: Utilizes NLP and user history to offer contextual content suggestions directly on the remote screen, often predicting the user's intended movie or show after just one or two keystrokes.

• Unified OTT Shortcuts: Customizable, one-tap shortcuts for launching favorite streaming services (e.g., Netflix, Hulu, YouTube) directly from the home screen, bypassing multiple menu layers.

• Adaptive Touch Layout: The interface is responsive and features dynamic zones (D-pad, swipe control, keyboard) that adapt based on the currently controlled device and application state (e.g., a numeric keypad appears automatically during live TV viewing).

• Extensive Accessibility Suite: Features designed for inclusivity, including High-Contrast Mode, Large Tap Targets, Voice Control, and Tactile Feedback customization.

3. My UX Process: Designing for Diverse Needs

The design methodology was centered on reducing cognitive load and maximizing inclusivity.

• UX Research Methods:

  • Ethnographic Studies: Observing users struggling with existing physical and app remotes in their home environments to identify pain points (typing, changing inputs).

  • Accessibility Audits: Testing early wireframes with users who rely on assistive technologies (e.g., screen readers) to ensure core navigation and features were fully compliant and usable.

• Design & Prototyping:

  • Prioritization of Inputs: The interface prioritizes touch and voice commands over on-screen typing. Key functions (Volume, Channel, Power) were placed in fixed, thumb-reachable zones.

  • Predictive Search Flow: Designed an input field that immediately triggers AI content suggestions upon the first character input, drastically cutting down search time.

  • Accessibility Profile State: Developed a user flow allowing users to save and instantly switch between accessibility profiles (e.g., "Dad's High Contrast Mode" vs. "Kid's Simplified Layout").

4. The Final Product: Intuitive Command and Enhanced Personalization

The AI Smart TV Remote Pro+ is more than a remote; it's a personalized control center. It delivers an intuitive, hyper-responsive, and inclusive user experience that adapts to individual preferences and accessibility requirements. The application successfully leverages AI to anticipate user needs, transforming the formerly frustrating act of content discovery and control into a seamless, enjoyable interaction. This project demonstrates expertise in combining complex AI features with compassionate, accessibility-first design principles.

AI Turbine Weather Application: Predictive Intelligence for Renewable Energy - @ Google AiStudio - Live Demo

Project Overview

The AI Turbine Weather Application is a responsive, AI-powered dashboard designed for the comprehensive monitoring and analysis of utility-scale renewable energy assets, including wind turbines and solar plants. It integrates real-time data on energy production, localized weather conditions, system performance, and operational metrics. By merging these datasets, the platform provides predictive intelligence to optimize energy output and minimize maintenance costs.

My Role: UX Designer, Data Visualization Specialist, Front-End Developer

1. The Problem: Unpredictability and Inefficiency

Renewable energy production is inherently dependent on highly variable weather conditions, leading to significant challenges in accurate forecasting, grid management, and operational scheduling. Asset managers struggled with:

• Reactive Maintenance: Scheduling maintenance based on historical data or generic timeframes, often leading to unnecessary downtime or, worse, catastrophic failure due to unforeseen weather stress.

• Inaccurate Forecasting: Generic weather data often fails to capture the microclimates of remote plant locations, resulting in poor energy production forecasts and compliance penalties.

• Data Overload: The sheer volume of telemetry data from turbines and solar arrays, combined with meteorological inputs, was overwhelming and difficult to translate into actionable decisions.

The core challenge was to transform vast, complex data into a clear, predictive tool that enables proactive asset management.

2. The Solution: A Unified, Predictive Intelligence Platform

The AI Turbine Weather Application provides a single source of truth for all renewable asset performance data, enhanced by AI-driven predictive modeling.

• Real-Time & Predictive Visualization: The dashboard unifies energy production metrics with hyper-local weather conditions (wind speed, solar irradiance, temperature, precipitation). Crucially, it provides AI-powered forecasts for the next 24-72 hours, enabling predictive resource allocation.

• System Health & Anomaly Detection: The application uses operational metrics to instantly flag anomalies in system performance that correlate with adverse weather events (e.g., turbine vibration at high wind speeds).

• Geospatial Asset Mapping: A dynamic map view displays all assets, color-coded by performance or alert status. Users can instantly see which plants are underperforming due to localized weather issues.

• Responsive Design: Built as a responsive web application, the dashboard ensures energy managers can access critical data and respond to alerts efficiently, whether they are in the office or in the field via a tablet or mobile device.

3. My Design Process: Focusing on Forecast and Alert Hierarchy

The design process prioritized clarity and actionable information, focusing on presenting complex time-series data in a digestible format.

• UX Research: I conducted needs assessments with renewable energy operators, revealing that the wind speed forecast and inverter temperature were the most critical data points for immediate action. This informed the primary layout, placing these key metrics and associated AI forecasts front-and-center.

• Information Architecture: The structure was designed around a "Global Overview" leading to "Asset-Specific Deep Dives." The main dashboard uses a traffic light system (green, yellow, red) for asset health, with clear navigation to detailed performance charts.

• Data Visualization: I used highly effective data visualization techniques, preferring line charts for trends and heatmaps for geographical performance. I ensured a high contrast, professional color palette suitable for long-term monitoring. The key innovation was the integration of weather forecast lines directly over energy production graphs—a visual correlation that immediately validates the AI's predictions and aids user trust.

4. The Final Product: Maximized Output, Minimized Risk

The AI Turbine Weather Application successfully translates raw data and complex AI models into a clean, actionable user experience. It provides asset managers with the foresight necessary to move from reactive decision-making to a proactive, predictive operational strategy. This results in maximized energy output, extended equipment lifespan, and reliable contribution to the power grid. This project showcases proficiency in developing sophisticated, data-heavy applications that deliver tangible financial and operational value in the critical renewable energy sector.

CineAI: A Modern Entertainment Booking Experience - @ Google AiStudio - Live Demo

Project Overview

CineAI is a responsive React application that redefines the entertainment booking experience. Inspired by platforms like BookMyShow, it allows users to browse movies, events, and sports, view detailed information, and simulate a seamless seat selection process. The platform's standout feature is an integrated AI-powered chat assistant that provides personalized recommendations and instant information.

My Role: UX Designer, Front-End Developer, and Product Designer (as a solo project)

1. The Problem: Information Overload & The Static Experience

Traditional entertainment booking websites often present a static, overwhelming grid of content. Users have to sift through endless lists to find something they might be interested in, and the process of finding specific information or recommendations can be clunky and slow. The key challenge was to address this by creating a dynamic, interactive, and intelligent platform that personalizes the booking journey and makes finding the perfect entertainment simple and enjoyable.

2. The Solution: An AI-Powered, User-Centric Platform

The solution was to build a modern web application that combines a beautiful user interface with the power of artificial intelligence. Key features include:

• AI-Powered Chat Assistant: The core of CineAI is a conversational AI assistant. Users can ask questions like, "What are the top-rated action movies playing this weekend?" or "What's a good movie for a family with young kids?" The AI provides instant, relevant, and personalized recommendations, transforming a tedious search into a fluid conversation.

• Seamless Browsing Experience: The platform features a clean, responsive design with clear categories for movies, events, and sports. Users can easily browse through content carousels, and a quick click or tap reveals detailed information about each title, including synopses, cast details, and trailers.

• Interactive Seat Selection: To simulate a real-world booking experience, I created a simulated seat selection module. Users can select their preferred seats on a virtual theater map, providing a preview of the booking process.

• Responsive Design: The application is built to be fully responsive, ensuring a consistent and high-quality experience across all devices, from desktops to mobile phones.

3. My Design Process: Focusing on Intuitive Interaction

My design process was centered on making the AI chat assistant feel like a natural part of the booking experience, not just a gimmick.

• UX Research: I conducted a competitive analysis of entertainment booking platforms and conversational AI interfaces. I paid close attention to the way users ask questions and the types of information they seek, which helped me design a conversational flow that feels intuitive and helpful. I also studied the most effective visual design patterns for presenting a large amount of content in a digestible, engaging way.

• Information Architecture: I designed a simple, flat information architecture that prioritizes quick access to core features. The AI chat is persistent and always available, acting as a shortcut to any information the user needs.

• Prototyping & Visual Design: I began with low-fidelity wireframes to map out the user flow, particularly for the AI chat and the seat selection process. The visual design focuses on a clean, dark theme with vibrant, eye-catching posters. I used React for its powerful component-based architecture, which allowed me to build modular UI elements for the content cards, the chat bubble, and the interactive seating chart, ensuring a scalable and consistent design.

4. The Final Product: A Glimpse into the Future of Booking

The final product is a modern, responsive, and intelligent application that showcases how AI can be seamlessly integrated into a traditional booking process. CineAI is a testament to the idea that technology should simplify and personalize the user experience. It demonstrates a forward-thinking approach to product design and development, proving that AI is not just a feature but a powerful tool for creating truly delightful and efficient digital experiences.

Key Learnings & Outcomes:

• The Power of Conversational AI: The project demonstrated that AI can be a powerful tool for enhancing user experience, transforming a static search into a dynamic, personalized conversation.

• Integration is Key: The seamless integration of the AI chat assistant into the main UI was crucial for its success, making it feel like a natural part of the application rather than an add-on.

• Building for the Future: CineAI is a glimpse into the future of entertainment booking, where technology anticipates user needs and provides a smarter, more efficient, and more enjoyable experience.