Looking for a trusted automotive software engineering partner?
Software development is a core competence of Intellias. Since 2002 we’ve been successfully delivering end-to-end software projects for in-vehicle systems, as well as we’ve been developing complex data platforms for autonomous driving ecosystem overall.
Software development is a core competence of Intellias. Since 2002 we’ve been successfully delivering end-to-end software projects for in-vehicle systems, as well as we’ve been developing complex data platforms for autonomous driving ecosystem overall.
Our quality control and testing methodology fully complies with industry standards and is never compromised. We test every single line of code, whether by setting up a simulated environment, on real hardware, or in the field using real cars. To increase the quality of software and shorten the development lifecycle, we implement cloud-based DevOps, which allows for automated testing and continuous delivery.
Our quality control and testing methodology fully complies with industry standards and is never compromised. We test every single line of code, whether by setting up a simulated environment, on real hardware, or in the field using real cars. To increase the quality of software and shorten the development lifecycle, we implement cloud-based DevOps, which allows for automated testing and continuous delivery.
Our driver-centric approach to designing and building human machine interface (HMI) systems used by millions of drivers has resulted in what Top Gear calls “remarkably stress-free navigation software.” We conduct meticulous user research to optimize all aspects of the driving experience for every user. Our target is to shorten driver response times, improve human–vehicle interactions, and augment the user experience with mobile features.
Our driver-centric approach to designing and building human machine interface (HMI) systems used by millions of drivers has resulted in what Top Gear calls “remarkably stress-free navigation software.” We conduct meticulous user research to optimize all aspects of the driving experience for every user. Our target is to shorten driver response times, improve human–vehicle interactions, and augment the user experience with mobile features.
Intellias has extensive expertise in building big data processing platforms for automotive companies. We know how to process massive loads of in-vehicle data, sensor data, and real-time traffic data. Our data visualization expertise allows us to create meaningful analytics dashboards for faster decision-making.
Intellias has extensive expertise in building big data processing platforms for automotive companies. We know how to process massive loads of in-vehicle data, sensor data, and real-time traffic data. Our data visualization expertise allows us to create meaningful analytics dashboards for faster decision-making.
At Intellias, we apply our DevOps approach to development to help automotive companies get the most out of high-performing cloud environments. We ensure scalability, continuous improvement, and greater efficiency for automotive software by using Amazon Web Services, Microsoft Azure, Google Cloud Platform, and other cloud services.
At Intellias, we apply our DevOps approach to development to help automotive companies get the most out of high-performing cloud environments. We ensure scalability, continuous improvement, and greater efficiency for automotive software by using Amazon Web Services, Microsoft Azure, Google Cloud Platform, and other cloud services.
Intellias uses machine learning (ML) and artificial intelligence (AI) services to build in-vehicle intelligence, provide smart routing, enable object and pedestrian recognition, and reinforce predictive decision-making. Adopting ML and AI ensures better environmental predictions, resulting in safer connected and autonomous vehicles.
Intellias uses machine learning (ML) and artificial intelligence (AI) services to build in-vehicle intelligence, provide smart routing, enable object and pedestrian recognition, and reinforce predictive decision-making. Adopting ML and AI ensures better environmental predictions, resulting in safer connected and autonomous vehicles.
