FitGen Ai
FitGen AI is an AI-powered fitness and nutrition web application that helps users clearly understand what they eat and how it aligns with their fitness goals. The platform allows users to capture or upload a photo of their food, which is then analyzed using AI to identify the food items and extract nutritional information such as protein, calories, carbohydrates, fats, and supplement relevance like creatine intake. Based on this analysis, FitGen AI provides personalized diet plans, workout recommendations, protein requirements, and creatine dosage guidance tailored to the user’s goals. The application is built as a complete website solution with a Lovable-based frontend, n8n handling backend automation and workflows, and Supabase managing data storage and authentication. Prompt engineering is used to ensure accurate, context-aware AI responses. Additionally, FitGen AI includes an AI calling agent that automatically contacts users when they request assistance, offering voice-based guidance and support. Overall, FitGen AI combines computer vision, AI agents, automation, and voice intelligence to deliver a practical, real-world fitness and nutrition assistant.
Business Automation (for any website -web scraping)
I built an end-to-end Business Chat Automation system that connects website interactions and WhatsApp messaging into a single intelligent workflow. The automation starts by capturing user input from the website form, processing and cleaning the data through multiple logic steps such as URL splitting, content extraction, filtering, aggregation, and structured data formatting. This processed context is then passed into an AI chat model, where prompt structuring ensures accurate, business-ready responses. In parallel, I integrated a WhatsApp trigger that allows incoming messages to be handled by an AI agent equipped with tools and memory, enabling contextual conversations and automated replies in real time. The system is designed to manage customer queries, extract meaningful intent, respond instantly, and deliver final outputs back to users through both web and WhatsApp channels—reducing manual effort while enabling scalable, always-on customer communication.
Project: Amazon Prime Dashboard Tools Used: Power BI, SQL, Data Analysis
Created an insightful and interactive Power BI dashboard to analyze and visualize Amazon Prime membership data, providing key business metrics for decision-makers. 🔹 Performed data cleaning and transformation to ensure consistency and prepare the dataset for accurate analysis 🔹 Developed calculated columns and measures to track key metrics like new memberships, churn rate, retention rate, and average spend per member 🔹 Designed a variety of visualizations such as bar charts, pie charts, line graphs, and slicers to compare membership growth, spending patterns, and customer segments 🔹 Delivered actionable insights, including identification of high-value customers, trends in subscription renewals, and regional performance analysis 🔹 Utilized SQL queries to pull relevant data from relational databases and connect it seamlessly to the dashboard for real-time updates This project helped me strengthen my skills in Power BI, data modeling, and data visualization, as well as improve my understanding of customer behavior and business KPIs.
