ShopBuddy

Making every mall visit smarter for shoppers and retailers at the same time

Shoppers walking into a large mall without a plan routinely waste time going back and forth between floors, miss deals that are happening in stores they walked right past, and leave either overspent or underspent relative to what they actually wanted. The problem is not a lack of options. It is a lack of intelligent curation at the moment it matters most.

Retailers face the mirror image of this problem. They run promotions that never reach the right audience. A shopper interested in fashion walks past a live fashion sale without ever knowing it exists because there is no intelligent layer connecting what the shopper wants with what the store is offering right now.

Building a solution that worked for both sides simultaneously was the core design challenge. The shopper experience needed to feel effortless: three inputs, instant plan, no friction. The retailer experience needed genuine data utility: footfall intent, offer performance, and the ability to reach high-intent visitors at exactly the right moment.

An AI-powered planner that works like a personal shopper in your pocket

We designed and developed the full ShopBuddy platform for iOS and Android. The experience starts with three simple inputs: budget, categories of interest, and time available. From there the app takes over. The recommendation engine analyses the user's preferences against live store data, ongoing offers, and floor layouts to generate a fully optimised visit plan telling the user exactly which stores to visit, in which order, using same-floor navigation logic that minimises walking time.

As the user moves through the mall they can check off stores, adjust their time or budget mid-visit, and receive updated suggestions in real time. The more a user interacts with the app the smarter it gets, learning preferences over time through the OpenAI-powered layer that interprets natural language inputs and translates them into increasingly precise recommendations.

The retailer side of the platform was built as a proper product in its own right. Mall operators and retail partners access a dashboard with footfall intent data, offer performance analytics, and a promotion tool that pushes targeted deals to users whose stated preferences match their store category. Both sides of the marketplace have something genuinely useful.

Built for real-time personalisation, smart routing, and dual-sided value

React Native handled the cross-platform iOS and Android frontend. Python and FastAPI powered the recommendation engine backend. OpenAI API provided natural language preference understanding and dynamic suggestion generation. Elasticsearch handled real-time store and offer search and ranking. PostgreSQL stored user profiles and long-term preference history. Redis cached live offer and floor plan data for instant response times. Firebase managed push notifications and real-time in-visit updates. AWS handled cloud infrastructure and scalability across all services.

Smarter visits for shoppers and measurable uplift for retail partners

Shoppers using the app reported a significantly more organised and satisfying mall experience, with less time wasted and more relevant stores discovered per visit. The same-floor navigation logic in particular received strong feedback. Users responded to the fact that the route felt thoughtful rather than just a generic list of recommendations.

Retail partners saw a measurable uplift in footfall to stores whose offers were surfaced through the recommendation engine. The platform created a genuine win on both sides: shoppers got clarity and a better experience, and retailers got a direct channel to reach high-intent visitors at exactly the moment they were ready to buy.