- 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
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.
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.
How To Apply Machine Learning To Demand Forecasting
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.
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.
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