Hi, I'm Waleed Butt
I craft scalable web and mobile apps using modern technologies.
Passionate about crafting cutting-edge solutions. Proficient in both Back-end and Front-end technologies. React Native to the Apple App Store & Google Play. NodeJS & NextJS to the cloud. I make it all work together.
Technologies
My technical expertise spans across modern web and cloud technologies.
Web Technologies
React
React Native
NextJS
NodeJS
PostgreSQL
MongoDB
Redux
Zustand
TailwindCSS
Material UI
Bootstrap
Ant Design
Cloud Technologies
AWS
S3
Lambda
EC2
DynamoDB
Google Cloud
App Engine
Cloud Run
Compute Engine
Cloud Build
Cloud Storage
Firebase
Featured Projects
Here are some of the projects I've worked on.
Smooth Pay
We built a versatile loyalty and rewards platform designed to support three distinct user roles: Admins, Merchants, and Customers. Customers interact through a mobile app, where they can earn and redeem loyalty points, make payments via QR, NFC, or cash, and keep track of their reward history. Merchants use a web dashboard to manage products, branches, and staff, and issue points to customers using their phone numbers. A built-in analytics module provides merchants with real-time business insights. Meanwhile, platform admins manage merchant onboarding, monitor user activity, and configure system-wide settings through a dedicated administrative panel.
Neuravue - Threat Detection
NeuraVue is a tech company based in Canada, we were brought on to help build a smart security analytics platform powered by computer vision. The goal was to create a system that could monitor live video feeds and detect threats like weapons, fights, falls, or people entering restricted areas, all in real time. We used the PERN stack (PostgreSQL, Express, React, Node.js) along with Firebase to push instant alerts. For cloud we used both the Google Cloud & AWS, mostly Cloud Run and Compute Engine, and we integrated video streaming tech to handle the live feeds. The final system helped improve threat detections across schools, retail spaces, and other public environments. We also built a user-friendly dashboard so both admins and users could monitor events and manage alerts easily.
PPE Violation Detections
We built a multi-level platform designed to manage safety compliance across industrial sites using smart video analytics. The system supports a hierarchy of super-admins, admins, and users, each with access to specific sites and smart-boxes (edge devices) that process RTSP camera feeds locally. These smart-boxes connect to standard IP or network cameras and use computer vision models to detect missing PPE, like helmets, jackets, safety shoes, or glasses, in real time. When a violation is detected, the system sends out immediate alerts to the relevant users. The platform is flexible enough to work with any existing camera infrastructure and includes a web dashboard where users can monitor sites, manage devices, and track safety incidents across multiple locations.
Bikia Health
BikiaHealth is an AI-driven medical platform designed to support both doctors and patients with intelligent tools and automation. On the clinical side, it offers a DDX module for symptom-based differential diagnosis, an RX module that recommends medications based on patient profiles, and Smart Documentation that automatically generates clinical summaries and medical forms. It also includes an AI-powered image analysis feature to help detect eye conditions such as Diabetic Retinopathy, Glaucoma, and Cataracts. For patients, the app provides a simple way to view visit history and access AI-generated reports, helping them better understand and manage their health.
TAYSE - DRONE BASED RUGS COUNTING
TAYSE is an AI-powered warehouse automation project we built for Tayse, a leading rug manufacturer in the U.S. The system uses smart drones to autonomously scan bin locations across the warehouse, capturing images that are analyzed by a set of AI models. These models handle tasks like bin detection, inventory counting, rug type classification, OCR, and QR code reading. The processing pipeline runs on AWS, optimized for batch operations. We also built a web application that integrates with their Warehouse Management System (WMS), and added a human-in-the-loop mechanism to validate and improve results over time. The end result is a streamlined, highly accurate inventory management system that saves time and reduces errors.
Retail Inventory Detection
We developed a real-time retail inventory detection system for a Dubai-based client, designed to work directly from mobile phones. The platform maps products within a hierarchical structure of warehouses, racks, and shelves, allowing precise location-based tracking. Using custom-trained AI models on thousands of product images, the system can accurately detect and log items as users scan shelves with their phones. Each scan updates inventory counts and timestamps in real time, helping maintain accurate stock records across the entire retail network.
Visivia - Facial Recognition Locks
We built a complete in-house facial recognition access control system, combining custom-designed PCBs with 3D depth camera hardware to ensure spoof-resistant authentication. End users enroll through a mobile app, while administrators can manage devices, user roles, and access permissions from a centralized web dashboard. The face recognition engine is optimized to handle thousands of identities with minimal latency, enabling fast, secure, and scalable door access for high-traffic environments.
INVENTORY MANAGEMENT (MATERIALS)
We developed a web-based management platform for a leading doughnut kiosk chain in Islamabad, focused on streamlining their daily operations. The system covers everything from material procurement and vendor coordination to recipe management and packaging workflows. It automates routine supply chain tasks, reducing manual overhead, and provides real-time analytics to help optimize stock levels and boost sales performance. The result is a more efficient backend that supports faster decision-making and improved revenue tracking.
Steri Smart
We built a cross-platform mobile app using React Native for iOS and Android that performs on-device image processing to identify surgical tray components. Trained on thousands of annotated images, the app uses a custom AI model to automatically recognize each item in a tray and alerts medical staff if anything is missing. This ensures surgical trays are always complete and ready, reducing preparation time and minimizing the risk of human error in critical environments.