GOA: Shop from a Sentence

Shopping starts with a need, not always a product name. Alice is graduating in Cape Town. She wants a green, elegant, reusable outfit for a warm December day, but conventional keyword searches leave her bouncing between shops. Independent designers remain hard to discover, while merchants cannot see what customers tried to find. GOA: Shop from a Sentence is a working creative-commerce prototype built by Busi Ndlovu and Kholofelo Robyn Rababalela for the AMD Developer Hackathon Act III, Track 3: Reinvent Commerce. A shopper describes an outfit in natural language or supplies reference photos. Gemma-4-31B-IT through AMD Token Factory interprets the brief into garment, footwear and accessory needs. Semantic catalogue matching proposes items; deterministic Python rules verify size availability, stock, arrival before the event and budget including delivery. Shoppers can swap pieces, ask why they fit, save a look and explicitly send demo quote enquiries. Unmet needs are shown honestly as "not on offer yet" and recorded in a maker demand-gap view. We evaluated GOA using 20 frozen briefs, 20 live Gemma calls and the same synthetic catalogue for a keyword-search baseline. GOA produced 55% fully valid looks versus 30%, 100% first-shown item grounding versus 82%, and 80% sendable enquiries versus 35%. Median steps were 3 versus 8, and GOA captured 12 unmet parts versus none for the baseline. Keyword search filled more individual parts (93% versus GOA's 83%), so our claim focuses on verified outcomes, not superficial coverage. The prototype runs on Vercel. Merchants, product inventory and quote inbox are fictional demo data; no purchases, actual merchant fulfilment or sales uplift are claimed. GOA demonstrates a more accountable bridge between human imagination and available supply.