Industries and usecase

Industries such as healthcare, finance, retail, and more are increasingly adopting Artificial Intelligence and Machine Learning technologies to enhance their operations. AI and ML can be used for predictive analysis, data classification, automation, and more. These technologies have the potential to improve efficiency, reduce costs, and provide a competitive advantage. At Roora, we offer AI and ML solutions to help businesses leverage these technologies in various use cases, such as fraud detection, customer experience, and supply chain optimization. Our solutions are tailored to meet the specific needs of each industry, and we work closely with our clients to ensure the successful implementation of these cutting-edge technologies.

Autonomous Vehicle

Data labeling is a crucial process in autonomous vehicles that involves annotating data to help machines recognize patterns and make accurate decisions. This involves labeling images, videos, and other data inputs to help AI algorithms understand and react to their environment. Proper data labeling ensures reliable and safe autonomous driving.


Data labelling in healthcare is the process of assigning descriptive and meaningful tags to medical data such as images, reports, and electronic health records. These tags are used for various purposes, including training machine learning algorithms, clinical decision-making, and disease surveillance. Data labelling ensures the accuracy and reliability of healthcare data, making it easier to analyze and interpret. It is a crucial step towards building effective healthcare systems and improving patient outcomes.


Data labelling is crucial in agriculture as it involves the process of adding informative labels to different types of agricultural data, such as crop type, plant growth stage, soil moisture level, and pest infestations. This helps to create a comprehensive understanding of the data, enabling farmers to make informed decisions about crop management, soil health, and pest control. Accurate data labelling can significantly improve crop yield, reduce crop loss, and increase the overall efficiency of the agricultural process.


Data labeling in Lidar refers to the process of adding semantic meaning to raw point cloud data. This is typically done by assigning labels to individual points based on their position and attributes. The resulting labeled data can then be used for a variety of applications, including object detection, terrain mapping, and autonomous driving. Accurate data labeling is critical for the success of these applications, and requires careful consideration of factors such as sensor calibration, data quality, and the specific use case.


Data labelling in retail involves assigning accurate and relevant tags to data sets for effective analysis. It helps businesses identify patterns, trends, and customer behavior to enhance sales and customer experience. Data labeling is crucial in retail as it enables better decision-making and understanding of customer preferences.


Data labelling in sports is the process of identifying and categorizing data points within sports datasets. This process helps sports analysts to understand patterns and trends within the data, and make informed decisions. Data labelling can also be used to create more accurate predictive models, which can help sports teams to make better decisions on the field. Examples of data labelling in sports include identifying player positions, categorizing plays, and tracking the movement of the ball or puck. Overall, data labelling is an essential part of sports analytics, and is used by coaches, scouts, and analysts to gain a competitive advantage.


Aerial annotation is the process of adding descriptive information to images and videos captured from above using drones or other aerial platforms. This can include details about the location, features, and characteristics of the area being captured, providing valuable insights for industries such as agriculture, surveying, and real estate.


Data annotation in insurance refers to the process of labeling and categorizing data to enable effective analysis and decision making. This allows insurers to identify patterns, detect fraud, and improve risk assessment accuracy.