Case Study

October 11, 2021

AI solution for automobile and travel recommendations

Artificial intelligence has advanced out of the labs

We understand that your business requires custom and robust solutions to build up a powerful artificial intelligence system for your business and you are able to achieve your goals. In this system we ensure your data to be safe and secure and security checks to be in place. Brisk Logic has developed AI solutions for the automobile industry and travel recommendations.

case Study

AI Solution for Automobile Industry

Brisk Logic has worked intimately with a Fortune 500 organization in the car space to make an AI racing assistant improve its racing track understanding.

The goal was to distinguish the most ideal way for the vehicle by examining the total racing track. We utilized the track utilizing sensor information and furthermore executed best in class Deep Q learning with Tensorflow. We utilized the most high-performing tech stack, for example, Python and PyTorch to guarantee speed and accuracy and constructed a profoundly successful AI racing assistant.

business challagne

AI Solution for Travel Recommendations

Brisk Logic was drawn closer by an organization to manufacture a movement proposal motor on AI and ML models for their locale-based travel mobile application. Through this application, clients could take outings to close by places, investigate the network and so on. Presently, they needed to make the application all the more captivating and customized by giving travel proposals to clients.

For this our experts of data scientist have implemented an efficient travel recommendation system where:

● Clients were prescribed spots to visit dependent on their past movement history

● Clients were shown a list of close-by vacation spots when they visit a specific spot.

● Clients were shown recommendations dependent on their preferences and inclinations and furthermore refreshes from different travelers who had comparable interests.

● We utilized Machine learning models to assemble recommendation engines and afterward prepared the calculation to investigate key information focuses.

● The tech stack we implemented in building these Machine Learning models was Python, Tensorflow, Sklearn, iOS CoreML, Elasticsearch.

As a result, we built a hybrid recommendation model that was both content-based and collaborative.


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