Discover how helps organizations in the mobility industry quickly build and deploy predictive models for real-world use cases.


Exploiting Mobility Data at Scale

Companies in the transport and human flow areas deal with huge amounts of mobility data across different contexts and resolution scales. The use of big data in tourism, international trade, and public transport offers new perspectives but also real challenges for executives to effectively exploit all this information. Deploying predictive modeling at scale can bring vital benefits to the mobility industry.


Operationalizing Machine Learning

Thanks to the spreading of machine learning technology, mobility actors can now reach such scale and deploy machine learning in transportation management or to predict human flows. This allows administrators to better plan their business and improve teams’ operational efficiency. Whether it is for process optimization, predictive maintenance, or autonomous logistics, smart mobility is already a source of competitive advantage in the mass transportation and automotive industries


Why for Mobility Industry?

We believe in building highly efficient predictive models that are immediately deployable, scalable, and accessible by users without a data science background. helps mobility companies accelerate their data science projects by empowering executives and teams with machine learning models ready to be used in business processes and logistics.

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Predictive Maintenance

Predictive Maintenance

Predictive maintenance empowers companies to reduce equipment failure through the collection of data from sensors and the detection of failure patterns. automatically builds machine learning models based on data from your operations to help you increase the longevity of equipment, optimize your production, and augment maintenance teams. View the use case

Yield Management

Yield Management

Effective yield or revenue management is critical for companies dealing with continuous demand and multiple traffic flows. Our platform allows for automating the decision-making process, resulting in faster optimization and more performant simulation methods. This helps executives to better manage occupancy and maximize revenue opportunities. View the use case

Internet of Things

Internet of Things

Only a few companies succeed in applying machine learning models to IoT data to improve process efficiency. facilitates access to actionable insights by automating the creation of such models and providing executives with an operational advantage. With the right models, systems can be gradually taught to recognize factors and improve the efficiency of the production process. View the use case

Thanks to, we have moved from traditional preventive maintenance to proactive and connected maintenance. We can now learn about train failures before the time and act accordingly.

– Project Manager at the Material Department, Transport and Logistics Company –

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