06 / Independent project
Can Buy Lah
An AI-assisted product-understanding and price-comparison MVP for in-store shopping in Singapore.
Project background
Chinese-speaking shoppers in Singapore often switch between translation, search, marketplaces and social platforms to understand unfamiliar packaging, prices and reviews.
Goal & role
I independently moved from user-problem mapping and product flow to AI output rules, error-case review and an accessible MVP.
What I did
I rebuilt the journey as capture → identify → standardise → search → understand → compare → decide. For failures involving size, flavour, discounts and vouchers, I created exact-match, near-match and material-difference rules, then adjusted price priority and information hierarchy.
Result & review
Current CV metrics: 90%+ product-image recognition accuracy, 85%+ effective price-match success and 30 invited test users. The MVP has been tested across beauty, pharmacy, food and daily necessities, with rules iterated through error-case review.