GenGenSense Reinvents How AI Learns to See the World

Artificial intelligence thrives on data, but not just any data. For AI systems that need to “see” the world—like self-driving cars, surveillance cameras, or drone guidance systems—the challenge is collecting enough visual data to cover all the strange, rare, and dangerous scenarios that might occur. Gathering this in the real world is slow, expensive, and sometimes impossible.

For years, the industry has leaned on 3D simulations as a substitute. These digital twins offer excellent control of the environment—engineers can place objects exactly where they want and control lighting, weather, or camera angles. But there’s a catch: most 3D graphics simulations often look artificial. The result is a “realism gap” that makes AI models trained on such data up to 30% less accurate compared to those trained with real-world footage.

Building actual ultra-realistic 3D scenes is possible, but is extremely costly and time-consuming, which limits how much data can be produced.

GenGenAI, a startup founded in 2022, is taking a different approach. Instead of pushing 3D graphics to their limits, the company specializes in domain-specific generative AI that produces synthetic data designed explicitly for training other AI systems. Their generative models don’t just create photo-realistic images and videos—they can also transform existing content to simulate entirely new conditions. A sunny road scene, for example, can instantly become rainy, snowy, or foggy. Vehicles, pedestrians, or even wild animals can be added to introduce rare and risky scenarios that would be nearly impossible to capture in real life.

This ability to transform scenes is a breakthrough. Engineers don’t need to build perfectly realistic 3D worlds from scratch. A moderately realistic simulation or even a basic input scene can be enhanced into ultra-realistic training footage by GenGenAI’s transformation models. Importantly, every generated scene comes with labeled and segmented data, making it ready for AI training out of the box. Beyond visuals, the platform also generates infrared, thermal, and heat sensor data, enabling multi-sensor AI training crucial for autonomous vehicles and defense applications.

The results are impressive. In one test, models trained entirely on GenGenAI’s synthetic data achieved up to 98% accuracy in traffic sign recognition, rivaling or even surpassing those trained with real-world footage.

Military partners are particularly interested in off-road scenarios—terrains that are notoriously difficult to model with polygons but can be generated convincingly with AI. Maritime simulations are another area of demand, driven by the rise of naval drones and unmanned surface vehicles.

GenGenAI sidesteps the complexity of building digital twins by focusing narrowly on generating data for vertical domains like automotive safety and defense, where specialized generative models can outperform general-purpose AI in both realism and efficiency.

Founded by image-processing expert Hen Cho, who previously worked on Adobe Photoshop’s imaging filters and automotive driver-assist systems, GenGenAI has built a research-driven organization with more than 20 specialists and dozens of patents. The company has already partnered with South Korea’s top defense firms and secured strategic investment from Korea Aerospace Industries and Hanwha Systems. About half of its early revenues already come from U.S. customers, including autonomous driving startups.

At CES 2025, GenGenAI showcased GenGenStudio, a platform for generating synthetic datasets. The company plans to return to CES 2026 with GenGenSense, a next-generation system capable of generating synchronized multi-sensor data (RGB, LiDAR, infrared) at scale, as well as GenGenInsight, a tool that analyzes datasets to uncover hidden biases and blind spots before training.

By focusing on specialty rather than generality, GenGenAI positions itself as a lead supplier of synthetic data at a time when many agree the industry is reaching the limits of real-world collection. Whether for smart cars, defense systems, or intelligent surveillance, their technology offers faster, cheaper, and safer ways to create the “what if” scenarios AI must master before it can be trusted in the real world.