for Intelligence, Surveillance, and Reconnaissance (ISR)
Configure unlimited datasets to meet your specific AI training needs. Generate diverse training images that with various sensors, lighting conditions, environments and weather condition. Choose objects and actors for your scenes
Synthetic data generation is more cost-efficient than traditional methods, reducing reliance on rare real-world images that are costly to collect and annotate.
Synthetic data generation is speeding up development by months! Traditional data collection and annotation methods take months. Synthetic images are generated in hours!
Achieve high accuracy of your AI with pixel-perfect labels. Our synthetic images provide accurate 2D and 3D labels available instantly when the images are generated.
Trusted by Defense Primes, organizations operating in Ukraine and tested by NATO, AI Verse synthetic images enhance the accuracy of Defence AI models for various use cases.
Enhanced training with diverse synthetic images leads to precise detection and classification of military vehicles.
Synthetic data reduces the time and cost associated with real-world data acquisition, enabling faster model development and innovation.
Rapidly generate extensive datasets and update models seamlessly for large and complex defense projects, ensuring readiness for any operational demand.
Safeguard sensitive information effectively — our use of synthetic data mitigates risks associated with exposing classified vehicle data.
There are 8 pixel-perfect labels included: Classes, Instances, Depth, Normals, 2D/3D Bounding Boxes, 2D/3D Keypoints, Skeletons, and Color.
Users select the desired parameters for the environment, scenes, objects, activities, lighting, and more. Based on these criteria, our engine can generate an unlimited number of diverse, varied, and labeled images ready for AI model training.
Yes, our automated system ensures that each generated image contains 8 pixel-perfect labels, reducing the risk of inaccuracies and guaranteeing the highest data quality.
Our proprietary procedural technology generates images based on human input. Users select various criteria for the image from a menu in a step-by-step process, rather than typing a prompt into a GenAI tool. This approach minimizes mistakes and ensures the highest possible realism in our images.
It takes 4s to generate one labelled image on 1 GPU. Generation can be spread across several GPUs (max 10).
AI Verse focuses specifically on high‑fidelity, physics‑aware synthetic imagery for defense instead of generic text‑to‑image generation. Our procedural engine gives user full control over scene parameters, sensor characteristics, and environments, giving teams repeatable, testable datasets.
In the development of a computer vision fall detection model, one of the biggest challenges is obtaining high-quality, well-annotated image datasets. Real-world fall datasets are scarce due to privacy concerns, ethical constraints, and the difficulty of capturing diverse fall scenarios in real life. We tackled this challenge by leveraging synthetic images to train a highly […]
Choosing between synthetic data and real-life data for AI model training is both a strategic and technical decision. Each option has its advantages and challenges, and the right choice depends on multiple factors such as data availability, quality, ethical considerations, complexity, and cost. Let’s explore how to make this decision effectively, navigating five critical questions. […]
In computer vision, developing robust and accurate models depends on the quality and volume of training data. Synthetic images, generated by procedural engine, have emerged as a transformative solution to the data bottleneck. They empower developers to overcome data scarcity, reduce biases, and enhance model performance in real-world scenarios. Here’s a detailed guide to training […]
