TensorGo is an enterprise-grade low code PaaS company for computer vision products. The platform enables users to build the most complex ML/DL applications in an easier manner by integrating our APIs. We custom build State-Of-The-Art neural networks to solve the most challenging problems in the world. We are shaping a smarter tomorrow through our deep learning, computer vision-powered products.
Offerings
Offering
Distracted driving is a leading cause of motor vehicle accidents and poses a significant liability for fleet management and ride-hailing companies that rely on their employees to drive safely. With a mission to improve driver safety in the transportation industry, TensorGo Software presents an active driver safety solution: the Advanced Driver Attention Metrics System (Adams). Adams leverages AI algorithms for eye gaze, head pose, and object detection to identify distracted driving cues, including drowsiness indicators like closed eyes or yawning, and dangerous diversions such as mobile phone usage, looking away, drinking, and multitasking like applying makeup. This product is installed directly on the vehicle dashboard to instantly alert drivers when they're distracted, so they can take corrective action to protect themselves and those around them every time they drive.
Offering
1. Description The TensorGo Hyperspace Platform is a highly secure, comprehensive AI solution, designed to streamline AI adoption for enterprises. It provides a modular, scalable, and flexible infrastructure for deploying, managing, and optimizing AI models across various environments, including cloud, on-premises, and edge devices. Importantly, the platform is deployed within the customer's infrastructure, ensuring full control over data and operations. 2. Market Problem Addressed a. Accessibility: Making deep learning technologies accessible to non-technical users within organizations. b. Time-to-Market: Reducing the time required to develop and deploy AI solutions from months or years to days or weeks. c. Cost Efficiency: Minimizing the expenses associated with AI implementation, including infrastructure, talent, and maintenance costs. d. Scalability: Enabling enterprises to easily scale their AI applications across different use cases and departments. e. Customization: Providing the flexibility to create custom neural networks tailored to specific business needs. 3. Data Collection The platform collects diverse types of data from various sources, including: a. Structured Data: From databases and data warehouses b. Unstructured Data: From text, images, and videos c. Real-Time Data: From IoT devices and sensors d. Proprietary Data: TensorGo's own data collected from various research and operational activities, enhancing the platform's capabilities 4. Data Analysis Data is analyzed through several stages: a. Automated Data Processing: Tools for data cleaning, transformation, and augmentation ensure data is optimized for analysis. b. Model Training and Optimization: Supports various machine learning frameworks and optimization algorithms to build and fine-tune AI models. c. Deployment and Scaling: Models can be deployed in different environments and scaled according to demand. d. Monitoring and Management: Real-time monitoring, logging, and alerting systems allow continuous tracking and performance optimization of deployed models. 5. Security and Deployment Security is a core component of the TensorGo Hyperspace Platform. Key features include: a. Data Encryption: Data is encrypted both in transit and at rest to prevent unauthorized access. b. Access Controls: Strict access controls and authentication mechanisms ensure only authorized personnel can access the platform and its data. c. Compliance: Adheres to industry standards and regulations such as GDPR and HIPAA. The platform is deployed within the customer's own tenacity, providing full control over data and operations. This on-premises deployment model enhances security by ensuring that sensitive data remains within the customer's environment, reducing exposure to external threats. 6. Business Value Provided to the End User The analyzed data provides substantial business value to end users: a. Enhanced Decision-Making: Offering insights derived from data analysis to help businesses make informed decisions. b. Operational Efficiency: Automating routine tasks and processes, reducing operational costs and increasing productivity. c. Personalized Customer Experiences: Enabling personalized recommendations and services, improving customer satisfaction and engagement. d. Innovation and Competitive Advantage: Facilitating rapid innovation by allowing businesses to experiment with and deploy new AI models quickly, staying ahead of competitors. e. Scalability and Flexibility: Providing a scalable and flexible infrastructure that can grow with the business and adapt to changing needs. The TensorGo Hyperspace Platform empowers enterprises to utilize the full potential of AI, driving growth and innovation while addressing common barriers to AI adoption, all within a highly secure and controlled environment.