E-commerce automation engine (10k+ products, SEO + images + feeds)

Note: This case study is intentionally anonymized. Company and product details are omitted.

TL;DR

I built a Node.js automation engine that processes 10,000+ products end-to-end — generating SEO descriptions, alt text, SEO-friendly filenames, product tags, bulk-processed images, and CSV exports for catalog feeds. What used to take weeks of manual work per product line now runs in hours.

Context

• A product catalog with 10,000+ SKUs across multiple brands
• Products had inconsistent or missing descriptions, no alt text, non-descriptive filenames, and incomplete tagging
• Manual content creation at that scale was impossible — and the catalog kept growing
• Feeds and exports were assembled by hand, creating bottlenecks for marketing and distribution

What I built

SEO product descriptions

Automated generation of structured product descriptions following a consistent template: what the product is, key attributes, use cases, and differentiators. Every product gets a description — no gaps, no inconsistencies.

Alt text generation (Google Vision)

Integrated Google Cloud Vision API to analyze product images and generate descriptive, accurate alt text. The automation processes images in bulk, producing alt text that’s both accessibility-compliant and SEO-relevant — not generic placeholders.

SEO-friendly filenames

Product images were renamed from generic uploads (IMG_4521.jpg) to structured, descriptive filenames that include product name, material, and key attributes. This improves image search visibility and keeps the asset library organized.

Product tagging and categorization

Automated tagging based on product attributes — type, material, use case, collection membership. Consistent tagging across 10k+ products means filters, collections, and navigation work correctly without manual curation.

Bulk image processing

Batch processing pipeline for product images: resizing, format optimization, and consistent output specs. Images are processed to web-ready standards automatically as they enter the pipeline.

CSV export for feeds and catalogs

Structured CSV exports for catalog feeds, marketplace listings, and distribution partners. The export pulls from the enriched product data (descriptions, tags, images) so every feed is complete and consistent.

Tech stack

• Node.js — core automation pipeline
• Google Cloud Vision API — image analysis for alt text generation
• Google API — data integration and processing
• Shopify API for product data sync

What it solved

• Scale: 10k+ products with complete SEO content — impossible to do manually
• Consistency: every product follows the same structure, tagging, and naming conventions
• Speed: new product lines are processed in hours, not weeks
• Feed quality: CSV exports are always complete — no missing fields, no manual assembly

What I took from it

• At scale, automation isn’t optional — it’s the only way to maintain quality. Manual processes break down long before you reach 10k products.
• Google Vision produces surprisingly useful alt text when you structure the prompts and post-process the output. It’s not perfect, but it’s dramatically better than empty alt attributes.
• The hardest part wasn’t building the pipeline — it was defining the content rules (what makes a good description, what tags matter, what filename structure works) before automating them. Bad rules at scale create 10k problems instead of solving them.