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Built a distributed data ingestion system for collecting listing, lead, and analytics data from multiple real estate data sources.
The system used Python, FastAPI, Celery, Redis, Selenium Grid, and Docker to coordinate authenticated scraping across concurrent workers.
I designed shared session management using Redis locks and pub/sub, parallelized scraping workloads with Celery, and implemented missing-page reconciliation to improve reliability.
The production pipeline was designed to process large ingestion workloads reliably without manual intervention.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Manufacturing facility managers were flying blind, relying on delayed reports and disconnected spreadsheets to monitor multi-line production status. A modern industrial operation needed a central nervous system. I designed and developed FABRIX AI — a high-performance, real-time command center that aggregates data across active production lines and delivers instant visibility into yield, OEE (Overall Equipment Effectiveness), and maintenance alerts.
The Challenge
Industrial environments generate massive amounts of data, but it's often siloed or presented in outdated, hard-to-read interfaces. The core challenge was twofold:
Data Aggregation: We needed to ingest live statuses, targets, and outputs from multiple concurrent production lines without latency.
Cognitive Load: Operators on the factory floor needed a UI that instantly communicated critical alerts (like yield drops or material shortages) without requiring them to dig through menus. The interface had to be deeply legible in low-light industrial environments while looking like a premium, modern SaaS product.
he FABRIX AI dashboard transformed raw factory data into an actionable, real-time narrative.
Facility managers now have a single pane of glass showing global OEE, total output, and granular line-by-line efficiency.
The high-contrast alert system dramatically reduces response times to yield drops and maintenance bottlenecks.
The fully responsive, single-page application structure allows the dashboard to be displayed on large factory floor monitors or accessed remotely by shift supervisors without losing visual hierarchy.
Developed VERITAS, a production-ready environmental intelligence platform analyzing 11,000+ environmental records for real-time indoor air quality monitoring and risk assessment. Built scalable backend services using FastAPI, designed efficient data processing pipelines with Python and Pandas, and implemented automated reporting workflows. Integrated XGBoost with SHAP for explainable AI-powered decision support, contributing across backend development, API design, deployment, and production optimization.
EIE — AI / Automation Project
Independent software project focused on AI-assisted workflows, automation and system integration. Built as part of broader experimentation with practical AI-enabled software systems and execution workflows.
