Project Highlights:
- UI/UX: minimum actions to operate
- Machine learning-based demand forecasting
- A Python-based recommendation module
- Continuous integration for scalability and customization
- A team devoted to the product
Venue Management and Point Of Sale for Nightclubs and Bars
Client and Business Goals:
The client is a San Francisco-based company and a provider of premium venue management and POS software to high volume bars, restaurants, and nightclubs.
Product Description:
SmartTab is a premium hardware and software that streamlines venue management operations and POS terminals. It covers the needs of bars and nightclubs — from inventory and taxes to serving the crowd on a busy night.
Applied Technologies:
- AWS (EC2, EBS, S3, Route 53, VPC, EKS)
- Python, Flask, PHP, PostgreSQL, Nginx, JavaScript, Vue.js, Composer, Hazelcast, Redis, REST services, WebSockets, Swift (iOS) and Java (Android)
- UI/UX design, Agile management
- Manual, automated UI, stress and performance testing
- Continuous Integration/Continues Delivery (Jenkins, Ansible)
- Machine learning: Time Series and Gradient Boosting models
- Virtualization: Vagrant, VirtualBox
- Containerization: Docker, Kubernetes, Helm
How To Apply Machine Learning To Demand Forecasting
UI/UX: Minimum Actions to Operate
- Speed and quality of service is the winning combination when the crowd is simultaneously looking to order drinks. The goal was to make all POS operations — including ordering and payments — as quick and precise as possible.
Machine Learning-Based Demand Forecasting
- By applying Time Series and Gradient Boosting ML models, we predicted how many drinks, meals, and snacks would be sold within the next period for each venue. This type of predictive technology provide immense value to customers and venue owners.
A Python-Based Recommendation Module
We have implemented a recommendation system that provides pricing and discount recommendations to help venue owners maximize their profit. With Python under the hood, we handle big data processing and custom DS model implementation. Furthermore, considering Python frameworks for our tasks, we have chosen Flask since it will allow us to add built-in modules as a project scales.
Continuous Integration for Scalability and Customization
- The architecture design made it possible to quickly deploy the system at venues. It was even able to automatically deliver unique features to certain locations. Additionally, the update process is seamless for operating venues.
A Team Devoted to the Product
- Back in 2014, the journey started with an investigation on how to build a POS printer driver. From there, the team scaled up to 30+ people. Even today, the person responsible for the very first run is still part of the crew. We’re proud to be a core development team and to support our client with a reliable product and fresh ideas.
250+
Venue Chains
24/7
Failsafe Operations
Offline+Cloud
Architecture
In this video, Daniel Baron, founder of SmartTab US shares his impressions and experience working with MobiDev.
SmartTab is an extremely complex software solution that performs heavy processes from multiple different hardware and software touch points in close to real-time (within milliseconds) as it gets. Our clients demand high performance and we stand firmly as the most robust and reliable software product in our space. The skilled and competent engineers provided by MobiDev have consistently delivered on my expectations.
I cannot recommend MobiDev enough. I went from 5 engineers to over 20 of them. We have a reputation of having the best tech when compared to companies like Square, Micros (owned by Oracle), and Aloha (owned by NCR). These are all multi-billion dollar companies. And thanks to my team at MobiDev, I'm standing on stable ground in making these very bold claims.
