Muhammad Faisal's Work | ContraWork by Muhammad Faisal
Muhammad Faisal

Muhammad Faisal

Web Developer | MERN Stack | React & Next.js

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Hafizabad, Pakistan

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Hafizabad, Pakistan

Nafees Jewellers is a premium jewellery brand with a legacy since 1980, and it needed an online store that looked as refined as its gold. I designed a complete e-commerce experience, from first impression to checkout, plus an admin dashboard so the owner can run the store day to day. The storefront opens with a warm, editorial hero banner and a live gold rate bar, so customers see current pricing right away. Shoppers can browse by category (Rings, Nosepins, Earrings, Topas) or open the full collection and narrow it down by price range and gold purity (18K, 21K, 22K). Each product page has an image gallery, weight and purity details, customer ratings, a wishlist, and one-tap add to cart, followed by a clean cart and checkout flow. On the back end, the admin dashboard gives the owner a clear view of the business: total products, orders, customers and revenue, a sales overview chart, and recent order tracking with status labels (Pending, Processing, Delivered). The design uses a soft cream and gold palette with elegant typography, and the layout is fully responsive across desktop, tablet, and mobile. Key features Live gold rate bar on every page for transparent pricing Smart filters by price range and purity, with sorting Rich product pages with gallery, specs, ratings, and wishlist Simple cart and checkout with clear totals and free shipping on orders above PKR 10,000 Admin dashboard with stats, sales chart, and order status tracking Responsive design that works on any screen
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Title: SmartAI Factory: AI-Powered Industrial Machine Monitoring Factory downtime is expensive, and most teams only find out about a problem after a machine fails. For my Final Year Project, I built SmartAI Factory, a platform that lets teams see problems coming and fix them early. It streams live temperature, vibration, and current data from industrial machines to a real-time dashboard that flags anomalies as they happen. An Autoformer-based AI model then forecasts machine behavior 15, 30, 45, and 60 minutes ahead, each with a confidence score. What I built: Live monitoring dashboard with anomaly-marked charts and status badges AI forecasting across 4 time horizons Secure device onboarding: unknown machines stay Pending until an admin approves them Per-machine alert thresholds with in-app and email notifications Role-based access, user management, and light/dark mode My role: Full-stack development, UI/UX design, and AI model integration, end to end. Result: A complete predictive-maintenance workflow, from onboarding a device to getting an alert, in one clean interface. Open to freelance work on AI dashboards, IoT monitoring systems, and full-stack web apps. Let's talk.
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