LKSPOD Scraper is a structured data extraction project focused on print-on-demand T-shirt products, styles, and customization attributes. It helps teams organize POD catalog data, analyze product offerings, and standardize apparel information for downstream systems.

Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for lkspod you've just found your team — Let’s Chat. 👆👆

This project collects and structures detailed information about print-on-demand T-shirts, including materials, print methods, sizing, personalization options, and fulfillment details. It solves the problem of unstructured product descriptions by converting them into consistent, machine-readable data. It is built for e-commerce teams, POD sellers, analysts, and developers working with apparel product catalogs.

• Focuses on POD T-shirts and related apparel products
• Normalizes product, material, and print specifications
• Supports personalization and size-range analysis
• Designed for scalable catalog and content workflows

[
  {
    "productName": "Lion King Statement Tee",
    "style": "Premium T-Shirt",
    "material": "4.2 oz 100% ringspun cotton",
    "printMethod": "DTG",
    "availableSizes": ["S", "M", "L", "XL", "2XL", "3XL", "4XL", "5XL"],
    "personalizationOptions": ["name", "date", "custom quote"],
    "careInstructions": "Wash cold inside-out, tumble dry low",
    "productionTime": "2-4 business days",
    "shippingEstimate": "3-5 business days (US)"
  }
]

LKSPOD/
├── src/
│   ├── main.py
│   ├── parsers/
│   │   ├── product_parser.py
│   │   └── care_parser.py
│   ├── models/
│   │   └── product_schema.py
│   └── utils/
│       └── text_normalizer.py
├── data/
│   ├── sample_input.txt
│   └── sample_output.json
├── requirements.txt
└── README.md

• E-commerce sellers use it to structure POD T-shirt listings, so they can maintain consistent product catalogs.
• Data analysts use it to analyze materials, sizing, and print methods across apparel lines.
• Marketing teams use it to extract clean product attributes for SEO and content generation.
• Developers use it to feed standardized apparel data into stores, APIs, or analytics pipelines.

Does this project support multiple apparel types? Yes. While optimized for T-shirts, the structure supports hoodies, sweatshirts, tanks, and similar POD apparel.

Can it handle personalized products? Yes. Custom names, dates, quotes, and messages are captured as structured personalization fields.

Is sizing fully standardized? Sizes are normalized across common ranges, including extended sizes up to 5XL.

How accurate are care instructions? Care data is extracted and normalized from product descriptions and optimized for long-term print durability.

Primary Metric: Processes an average of 400–600 product descriptions per minute on standard configurations.

Reliability Metric: Maintains a 99% successful extraction rate across varied POD content formats.

Efficiency Metric: Lightweight parsing logic keeps memory usage stable under continuous workloads.

Quality Metric: Produces highly consistent product records with minimal manual cleanup required.

"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington Marketer ★★★★★

"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza SEO Affiliate Expert ★★★★★

"Exceptional results, clear communication, and flawless delivery. Bitbash nailed it." Syed Digital Strategist ★★★★★