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Artificial Intelligence is reshaping all areas of life and changing numerous businesses. In the automotive industry and mobility services, Machine Learning models improve and augment processes and operations, increasing customer satisfaction and unlocking new revenue streams for manufactures and service providers building the automotive landscape.
Empowering automotive enterprises to get insight from collected data, identify patterns and rules behind monitored behaviors and processes, and build the accurate model, Grape Up ensures a dedicated team of Data Scientists, Data Analysts, and Machine Learning Engineers.
Read the ebook and learn about technologies and solutions behind Software-Defined Vehicles. Follow the process from prototyping to changing the entire driving experience. Learn from the automotive experts how software impacts automotive, insurance, and other associated industries.
Using predictive maintenance allows for smart, cost-effective, and safe vehicle management. With AI-enabled solutions for monitoring vehicle state and predicting upcoming issues, both owners and professionals in charge of fleet management can reduce repair and exchange costs. Providing solutions to incoming problems and suggesting them in advance improves the driving experience and guarantees safety.
With the supply chain embracing AI-enabled analytics, manufactures can reduce operational costs and logistics time of manufacturing and distribution. Using AI algorithms to find peak efficiency, companies involved in the production process optimize their operations as well as operational costs.
Empowering the automotive industry to accelerate operations and improve processes with AI and Machine Learning systems, Grape Up ensures that every prediction and solution is interpretable for developers and business professionals. Explaining AI predictions to the customer is mandatory to comply with EU privacy law.
Predictions based on augmented testing enable OEMs to level up the manufacturing process. Numerous manual tasks and repetitive testing work can be reduced while using ML algorithms to predict potential failures and obstacles. Such solutions, along with reducing costs, improve manufacturing and increase assembly quality.
In such a competitive environment built by some of the world's most powerful enterprises, reducing time-to-market for innovative solutions can provide a crucial advantage. Automotive companies willing to productionize their Data Science and Machine Learning projects need to build a resilient and consistent development pipeline for used ML algorithms – from first experiments to production within days.
The future of the software-driven automotive industry depends on the capability to collect and leverage data from numerous resources. Complex, multi-layer models of Deep Learning Neural Networks empower automotive companies and mobility providers to effectively gain insights from gathered data. Equipped with such powerful solutions, automotive service providers can innovate and meet customer needs at a rapid pace.
In the modern automotive business, not only traditional dealerships or over-the-counter sales drive revenue for today's mobility providers, vehicle manufacturers, and OEMs. Today, recommendation engines and mobile applications generate an increasing amount of sales records. Acknowledging the business value of recommendation systems, Netflix estimated that their recommendation engine is worth $1bln yearly.
Rental car companies use these solutions in upselling to encourage customers for additional insurance, a higher grade vehicle, and other additional features. Leveraging recommendation systems vehicles enterprises increase sales and improve customer experience.
Our data science and data engineering departments have a proven track record of creating machine learning algorithms and combining them with e-commerce systems allowing automotive companies to take benefit from recommendation engines based on previous purchases.
Artificial Intelligence has proven to be a good way to tackle problems, which seemed impossible before. For the general audience, most of the ML is a black box, which accepts data and responds with prediction or identification. Algorithms are complex and hard to understand for non-data scientists. With explainable AI, the problem resolution path can be exposed to customers and stakeholders, making the bottlenecks and reasons for wrong reasoning visible.
Grape Up Data Scientists can help you build an ML system allowing stakeholders, developers, and customers to comprehend the prediction process and, as a result, have more trust in the results.
The Volkswagen Group presented publicly their “One Digital Platform” concept allowing to connect in-car services as well as run additional services for vehicles like charging or parking. Especially in the current era of people being always online, there are challenges to satisfy customer needs and expectations, which require a reliable IT infrastructure as a base […]
The Volkswagen Group presented publicly their “One Digital Platform” concept allowing to connect in-car services as well as run addition...
Artificial Intelligence seems to be a quite overused term in recent years, yet it is hard to argue that it is definitely the great