Product Attribute Accelerator — Turning product pages into clean data

The problem

Before a product can go on a digital shelf, someone has to define its attributes — size, flavor, material, pack count, dozens of fields per product. Retail teams did this by reading product descriptions and squinting at photos, product by product, thousands of times. Slow, expensive, and inconsistent: two people would tag the same product differently.

What I built

A system where multi-modal AI reads both the product text and the product images, and extracts the attributes automatically. The design that made it reliable wasn’t a single clever model — it was a cascade: one stage validates the input, the next extracts, the next checks the output’s logic before anything is accepted.

I also built the framework that defines what gets extracted: a MECE structure (mutually exclusive, collectively exhaustive) so attribute categories can’t overlap or leave gaps — and an automated configuration generator, so setting up the pipeline for a new product category no longer meant hand-writing configs.

What changed

• –Manual pipeline setup time cut by 40% through automated configuration generation
• –Attribute extraction became consistent — the same product gets the same tags every time
• –New product categories onboard in a fraction of the previous effort

Under the hood

Python, cascading multi-modal LLM chains with staged validation, automated config generation. Team of 2; I designed the extraction framework and consulted on prompt strategy across the wider attribution pipeline.