iVerse
Revolutionising AI vision systems by uncovering hidden AI errors through immersive virtual environments and simulating outliers that standard AI systems fail to detect, ensuring unparalleled accuracy and reliability
Train and test in the safety of simulation, conquer the challenges of the real world
Using VR and advanced physics-based simulations with sensor-mounted environments, we create highly realistic scenarios to replicate complex real-world challenges
We analyse AI vision models to identify failure conditions in extreme environments. Where AI systems fall short, the tool generates targeted synthetic training data to improve model performance and resilience.
We provide cutting-edge virtual test drive environments to accelerate autonomous vehicle development. Our platform enables safe, rapid, and cost-effective testing across complex traffic, extreme weather, and rare edge cases
Our mission is to perfect AI vision systems through cutting-edge simulation, setting new standards for precision and adaptability in the world of automotive and robotics
Unprecedented speed and precision
Enabling the creation of precise dataset of millions of images in a single day, revolutionizing the pace of AI development
Rapid prototyping
Iterate and refine AI vision models, streamlining the development process for quicker deployment and optimal performance.
Highly cost effective
Reduces costs by enabling AI vision systems to train and optimize in a virtual environment, sparing expensive real-world data collection and hardware expense
Data Privacy
we ensure that your sensitive information remains protected without compromise.
Safety
Ensures safety for AI vision systems by allowing them to learn and adapt in controlled virtual environments, minimizing the risk associated with real-world experimentation and potential harm
Scalabilty and usabilty
our simulation-driven approach empowers AI vision systems to effortlessly adapt to evolving demands, ensuring seamless integration and robust performance at any scale
Adaptabilty
Swift adjustments and learning across diverse scenarios, ensuring seamless adaptability to evolving challenges and environments
