Software Architecture Projects in LahoreSoftware Architecture Projects in LahoreI just completed a production-ready AI Lead Generation Agent designed to automate how businesses discover, verify, and export qualified leads across multiple data sources.
This system replaces manual lead hunting with an intelligent, filter-driven pipeline that aggregates business data, verifies contact information, and delivers CRM-ready outputs in real time.
š What this agent does:
ā¢ Searches businesses by location, industry, and keywords
ā¢ Aggregates data from Yellow Pages, Google Maps (API-ready), and extensible sources
ā¢ Applies smart filters (company size, founding date, industry relevance)
ā¢ Automatically verifies emails, phones, and websites
ā¢ Deduplicates leads for clean datasets
ā¢ Exports structured CSVs for sales & marketing teams
ā¢ Supports real-time queries via a FastAPI backend
ā¢ Schedules daily exports and weekly reports
š§ Tech Stack Highlights:
ā¢ Python + FastAPI (async, high-performance backend)
ā¢ Selenium-based scraping with anti-bot handling
ā¢ Modular lead source orchestration
ā¢ Glassmorphism UI with real-time search
ā¢ CSV-based persistence (lightweight & scalable)
This project was built with real business use cases in mind ā sales pipelines, outreach automation, and scalable lead discovery ā not just experimentation.
š„ Full walkthrough video:
š
Iām actively building and sharing end-to-end AI systems focused on automation, data intelligence, and real-world impact.
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(https://www.linkedin.com/search/results/all/?keywords=%23buildinpublic&origin=HASH_TAG_FROM_FEED)hashtag#BusinessGrowth (https://www.linkedin.com/search/results/all/?keywords=%23businessgrowth&origin=HASH_TAG_FROM_FEED) hashtag#SalesTech (https://www.linkedin.com/search/results/all/?keywords=%23salestech&origin=HASH_TAG_FROM_FEED) hashtag#B2B (https://www.linkedin.com/search/results/all/?keywords=%23b2b&origin=HASH_TAG_FROM_FEED) AI Vision for Retail, Industrial & Monitoring Workflows
Overview
I have built and deployed multiple real-world computer vision systems for industrial inspection, retail automation, and monitoring workflows.
My responsibilities covered:
š¹ Dataset preparation and labeling
š¹ Object detection model training
š¹ Segmentation model training
š¹ YOLO-based detection and tracking
š¹ Image/video inference pipeline development
š¹ Model evaluation and threshold tuning
š¹ Production deployment support
š¹ Cloud server management and optimization
š¹ Building practical AI workflows for real-world operational environments
Fish Quality Inspection System - lythium.cl (http://lythium.cl)
I led the development of an advanced fish quality inspection solution for an industrial workflow.
The system used image analysis to monitor fish quality and support automated fish sorting based on AI predictions.
š¹ Led the development of an advanced AI-powered fish quality inspection system for an industrial workflow.
š¹ Built an image analysis pipeline to monitor fish quality from production-line images.
š¹ Trained object detection models to identify fish and relevant visual quality indicators.
š¹ Trained segmentation models to support more detailed visual inspection of fish regions.
š¹ Designed the AI workflow to support automated fish sorting based on model predictions.
š¹ Worked on inspection logic that could classify or route fish based on quality-related outputs.
š¹ Designed the system for conveyor-belt usage, where images need to be processed consistently and reliably.
š¹ Focused on production issues such as image quality, camera consistency, lighting variation, and model reliability.
š¹ Helped convert visual inspection from a manual/rule-based workflow into an AI-supported inspection pipeline.
š¹ Built the system to reduce manual inspection effort and improve production workflow efficiency.
Shelfr.ai (http://Shelfr.ai) - Retail Automation Platform
I developed AI image solutions for retail automation and execution. The system handled large-scale product detection across 10,575+ SKUs, price tag detection, shelf and display type detection, and gap detection for empty shelf spaces.
š¹ Developed large-scale AI image solutions for retail automation and execution.
š¹ Worked on product detection across 10,575+ SKUs, where each SKU represented a unique product.
š¹ Built object detection workflows to identify products from retail shelf images.
š¹ Developed price tag detection to locate and extract price label areas from store images.
š¹ Worked on shelf and display type detection to understand the retail environment layout.
š¹ Built gap detection logic to identify empty shelf spaces and out-of-stock areas.
š¹ Supported computer vision workflows for retail compliance, shelf monitoring, and store execution.
š¹ Worked with high-volume image data and production-level inference requirements.
š¹ Managed high-load production servers on Google Cloud Platform.
š¹ Implemented load balancing and autoscaling to improve system stability under production traffic.
š¹ Focused on scalable AI infrastructure capable of handling real-world retail image workloads.
š¹ Helped create AI systems for inventory visibility, shelf condition monitoring, and retail execution analytics.
lake-shield.com (http://lake-shield.com) - USA LAKES - Boat Detection & Inspection System
š¹ Worked on a YOLO-based boat detection, tracking, and monitoring system.
š¹ Labeled datasets for boat detection and inspection model training.
š¹ Prepared image/video data for object detection training workflows.
š¹ Trained YOLO object detection models to detect boats in monitoring footage.
š¹ Built a detection pipeline capable of identifying boats from visual data. š¹ Worked on boat tracking logic to monitor boat movement across frames. š¹ Supported inspection and monitoring workflows using computer vision predictions.
š¹ Developed an end-to-end pipeline from labeled data to trained model and inference output.
š¹ Focused on practical model performance in outdoor environments where lighting, distance, angle, and background can vary.
š¹ Helped build a monitoring system that could support automated detection and review instead of fully manual observation.
My Responsibilities Across These Projects
š¹ Led AI/computer vision system development
š¹ Designed labeling and dataset preparation workflows
š¹ Trained YOLO/object detection models
š¹ Trained segmentation models where needed
š¹ Built image and video inference pipelines
š¹ Evaluated models using practical production metrics
š¹ Improved model performance through dataset cleanup, retraining, and threshold tuning
š¹ Integrated AI models into backend or operational workflows
š¹ Supported production deployment and infrastructure optimization
š¹ Worked with real-world constraints such as lighting, camera angle, image quality, latency, and false detection rates
Technologies Used
š¹ Python š¹ YOLO / YOLOv8 š¹ Object Detection š¹ Image Segmentation š¹ OpenCV š¹ PyTorch š¹ FastAPI š¹ Google Cloud Platform š¹ Linux Servers š¹ Load Balancing š¹ Autoscaling š¹ Custom Data Labeling Workflows š¹ Model Training š¹ Model Evaluation š¹ Inference Pipeline Development š¹ Production AI Deployment A feature-rich golf companion app that enables players to manage side games, bets, and scoring in real-time, eliminating manual calculations and enhancing the overall golfing experience.
Key Features:
Real-time leaderboard with live score tracking
Automatic payout calculations for betting games
Support for multiple game formats (Nassau, Skins, Wolf, Match Play, etc.)
Multi-game tracking during a single round
Handicap integration for accurate scoring
Game history and performance tracking
My Role:
Led full-stack development using the MERN stack with React Native
Designed and implemented scalable mobile UI/UX
Developed backend APIs and handled data management
Built real-time game logic and scoring system
Tech Stack:
React Native, MongoDB, Express.js, Node.js (MERN Stack), REST APIs 
