Food Ordering App For Restaurants Network

Ordering applications made with delivery, geolocation, built-in customer behavior analytics, and personalized suggestions. Made to up-sale.
Food Ordering App For Restaurants Network

Project Overview

IndustryFood and Beverages
Duration9 months
  • Android, iOS
Technology Stack Used
React Native


Our client wanted to create an ordering application that would cover 3 major scenarios: in-venue order, preorder, and delivery. The system was also to be fully integrated with the client's POS system in order to receive the order receipt confirmation and fetch and keep synchronized menu data in order to optimize the management effort.

The client granted us quite a lot of freedom not only in technological decisions but also in the overall project approach and management. There were two major goals for us to keep in mind: provide an opportunity for upselling and cut down the operating costs and staff routine for the venues' administration.


We started with a very thorough and careful business analysis in order to get a detailed understanding of all the operations in the customer’s venues. We also interviewed local managers and analyzed current POS and its integration possibilities. This allowed us to build a very accurate flow of the application that not only eliminated the extra load on the staff but also unleashed quite a number of resources which had a serious impact on the overall restaurant ROI.

The system comprises a React Native app for mobile and Android, a Web interface for system management and system backend, and is integrated with a payment gateway and POS system. We used the Django framework for the web interface in which the system administrator can add new venues to the system, manage venue details, view client's info and orders, send promo messages, and view analytics. The usage of Django standard mechanisms helped us to save a lot of resources so we could better focus on the mobile user interface.

The most exciting thing about this project was that, by coincidence, at that time, our R&D department had been working on extracting value from the datasets that were very similar to those the customer could build with our system. So we offered to build a personalized suggestions feature that learns frequent users' tastes and offers personalized additions. We took our model as a basis and customized it according to the customer's needs. The smart suggestions machine learning model proved to be able to upsell up to 20% for the frequent user segment.

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