u/Classic-Ad-8318

I audit distributor product data and recently found a search-enrichment rule that treated any number as a possible wire gauge.

A screw listed as #8-32 was tagged as “8 gauge wire.” A reference such as REF#259286 became “259286 AWG.” Once the rule ran across the catalog, thousands of unrelated products started appearing in electrical searches.

The search engine wasn’t really the source of the problem. Raw identifiers and inferred attributes had been mixed together without recording where the inference came from or checking whether it made sense for that product family.

I wrote up the failure and how distributors can prevent it:

https://subramanya.ai/2026/08/06/fixing-b2b-commerce-search-in-the-age-of-ai/

For people working with distributor or manufacturer catalogs: where does search quality usually break for you supplier feeds, taxonomy, cross-references, or ranking?

The idea is pretty straightforward: instead of searching Amazon and checking every label manually, 180cal filters products to ones that list at least 8g protein and fewer than 180 calories per serving.

You can compare protein, calories, serving size, price, and then open the Amazon listing if something looks useful.

https://180cal.com

Would love feedback.