AI and data tools for e-commerce sellers
Selleryar
Automated analytics pipelines, scraping systems, APIs, and scalable infrastructure for seller performance tools.
Overview
Context
Selleryar focused on seller performance optimization, requiring reliable data acquisition, analytics pipelines, and performant seller-facing features.
Challenge
Problem Space
The product needed broader catalog coverage, lower reporting effort, lower data acquisition costs, and scalable infrastructure during traffic spikes.
Solution Architecture
What shipped
Built analytics pipelines over 1M+ data points using Python, Pandas, and NumPy.
Created automated scraping pipelines and REST APIs with Scrapy, BeautifulSoup, Selenium, FastAPI, Flask, and Django.
Improved throughput through Docker/Kubernetes scaling, Nginx load balancing, server-side caching, PostgreSQL query tuning, and MongoDB schema optimization.
Expanded product data acquisition reach.
Reduced average latency from 900ms to 300ms.
Handled concurrent load without downtime.
Technology Stack