The platform using artificial intelligence to create predictive pricing models.
The company was founded in 2015 in Switzerland by 4 passionate individuals. This is a revolutionary platform which allows leverage AI and ML technologies to produce highly accurate and explainable models. The company is developing a unique, dynamic pricing engine to improve business performance. Also, it suggests a price’s frame to turn the proposal into a recommendation system for the sales team.
Dynamically changing prices and the need to forecast them is a major challenge, e.g. at airports and other public transport systems. These forecasts are needed to optimize and service fleets as well as passengers. Thanks to artificial intelligence and machine learning technology, it is possible to predict them more accurately than before. On this basis, it is also possible to solve complex business problems in industries such as energy, trade and telecommunications.
FINGO's latest challenge was to create a design that could be extended to their existing application. The team made a frontend for a new application - Genius Forecaster. The newly offered frontend by our team was also an example of design that may be extended to other applications in the future. However, our people had to overcome some challenges. First of all, they had to understand the design, stylistic preferences of the client and potential extensions or adaptation to the other applications.
We have developed an innovative web application for AI forecasts used in the financial and energy market.
Our team has developed an excellent user experience to access dynamic price data, which means modifying the data and presenting it to the user with the most modern forecasts (the data is processed with the ML tool).
We have created a new application design that may be extended to other applications in the future.
The whole technology stack was on our side; it was up to us to choose the right technologies and tools.
It was also our responsibility to run the project and create tasks in Jira, describe them (the client only verified them) and get the functional requirements.