Price Comparison Is More of a Data Problem Than a Pricing Problem
This story was originally published on HackerNoon at: https://hackernoon.com/price-comparison-is-more-of-a-data-problem-than-a-pricing-problem. Discover how AI & data normalization make product price comparisons more accurate by comparing unit costs, package sizes, & active ingredients across retailers. Check more stories related to undefined at: https://hackernoon.com/c/undefined. You can also check exclusive content about #ai-price-comparison, #unit-price-comparison, #product-data-normalization, #llm-product-data-extraction, #popgot-price-comparison, #product-price-comparison, #e-commerce-price-comparison, #good-company, and more. This story was written by: @sanya_kapoor. Learn more about this writer by checking @sanya_kapoor's about page, and for more stories, please visit hackernoon.com. Comparing product prices across retailers requires more than collecting numbers. Different package sizes, inconsistent product titles, and varying ingredient concentrations can make the cheapest listing a poor value. This article explores how Popgot uses data extraction, unit normalization, LLM-assisted parsing, and deterministic validation to compare products fairly, from cost per diaper and paper towel area to price per gram of protein. The goal is to rank products by actual value, not just sticker price.