Cigar Seek
The ultimate cigar search engine.
Project overview
Cigar Seek is The ultimate cigar search engine.. This innovative platform allows you to effortlessly browse through a vast selection of cigars from top brands and retailers. Built with high standards for performance, security, and intuitive user experiences, the platform is engineered to support mission-critical workflows and seamless scale.
What was the problem or pain point
Client Dilemma & Technical Bottleneck
Cigar Seek was founded to create the ultimate search, price comparison, and inventory tracking engine for premium handmade cigars and rare vintage tobacco releases. Cigar enthusiasts faced extreme price disparities across dozens of online cigar merchants, fragmented stock availability, and frequent out-of-stock disappointments on limited-edition releases. Scraping and normalizing cigar data was difficult due to inconsistent naming conventions, varying ring gauges, vitolas, and country of origin metadata across retailer sites.
What we suggested to the client
Solid Sphere Architectural Recommendation
Solid Sphere proposed developing an automated cigar aggregation and price comparison engine. We recommended deploying a distributed web scraping cluster using Scrapy and Python to continuously monitor dozens of cigar retailers, coupled with an NLP normalization pipeline that reconciles disparate product titles to a master cigar taxonomy, and an Elasticsearch-powered faceted search portal with instant price drop and stock alerts.
What made this project challenging
Aggregating live cigar inventory, auction bids, and pricing across dozens of major international retailers
Standardizing disparate vitola dimensions, wrapper origins, and flavor profiles into a canonical database
Handling high-frequency price updates and instant alert triggers when rare cigars restock
Building an ultra-fast faceted search filtering by strength, wrapper, country, and price range
What was implemented and how it was implemented
Step-by-Step Technical Execution
We built a scalable data extraction architecture using Scrapy and Python, deployed on AWS workers that crawl online cigar merchants multiple times daily. We engineered an NLP taxonomy reconciliation algorithm that parses product titles, extracting vitola, wrapper type, binder, filler, factory origin, and pack counts, mapping them to a standardized database of over 15,000 cigar SKUs. We developed the consumer search platform using React, delivering sub-30ms faceted search across brand, strength, ring gauge, and price range powered by Elasticsearch. We built a user watchlist feature where collectors can set target price alerts and receive instant notifications via email and push when rare cigars restock or drop below their threshold. Monetization was integrated through automated affiliate tracking links and retailer advertising spots.
How we delivered
Built distributed web scraping cluster collecting daily inventory and price updates from 40+ retailers
Engineered Elasticsearch index supporting fuzzy search, flavor notes, and vitola classification
Developed price drop and back-in-stock notification system alerting subscribed collectors instantly
Created community tasting notes and ratings system with verified purchase badges
What did we use to implement it
Technology Stack & Technical Rationale
React provided an intuitive, fast-loading search interface with interactive filtering and price history charts. Python and Scrapy formed the automated web crawling and data extraction engine. Elasticsearch enabled ultra-fast faceted search and autocomplete across complex multi-attribute cigar catalogs. PostgreSQL stored structured user profiles, alert rules, and historical pricing trends. AWS provided reliable cloud hosting for scheduled scraping workers and web servers.
Key outcomes
Over 25,000 unique cigar vitolas indexed and compared
Largest dedicated cigar price comparison engine online
Saved collectors an average of 22% per box purchase
Sub-100ms search query response across millions of price data points
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