CategoryIQ
IceCream Labs built applied-ML products for retail. CategoryIQ used machine learning to structure and categorize product catalogs at a scale manual processes couldn’t touch.
Retailers had mountains of unstructured product data and no scalable way to turn it into clean, categorized catalog intelligence.
Applied ML only earns its keep when it slots into a real workflow.
Focused the model on the decisions retailers actually needed to make, not on accuracy for its own sake.
Designed the product so ML output was reviewable and trustable — a human could see why a categorization was made.
Packaged it as something a retail team could adopt without a data-science department.
Award-winning tech still has to be adoptable. The win came from making ML legible to non-technical retail teams — the model was good, but the product was what made it usable. An early lesson in what would later become the AI-product thesis behind A1FP.