SKAI Intelligence

Synthetic Data Solution

We transform real-world environment into digital twins and create simulation environments where AI can learn.

Train Physical AI with synthetic data grounded in reality.

For Physical AI to operate reliably in real industrial environments, it needs large-scale data about the real world. But collecting real data is slow and costly, and the more critical the exception, the harder it is to obtain. SKAI Intelligence recreates industrial sites as precise digital twins, enabling AI systems to learn, adapt, and operate reliably in the real world.

The most challenging part of Physical AI
isn't the model.
It's the data.

  • Lighting Variation

    Factory lighting varies throughout the day and across prodution zones. It's the primary reason models that perform well in the lab struggles in production.

  • Material & Surface Properties

    A model trained on limited material conditions often fails when exposed to real-world variability.

  • Edge Cases

    Defects are unpredictable. Rare edge cases that are difficult to capture often become the leading cause of failure after deployment.

From Real-World Data to AI-Ready Simulation Data

In industrial AI, acquiring sufficient high-quality data remains one of the greatest challenges. Capturing every edge case, material variation, and environmental condition is both time-consuming and costly.

Through an NVIDIA Omniverse-based digital twin pipeline, SKAI Intelligence recreates real-world environments with physical accuracy and generates synthetic data at scale, enabling AI to learn from reality before deployment.

Physically Accurate Data Inputs

We transform real-world inputs required for AI training, including products, parts, CAD models, sensor parameters, and camera and lighting conditions, into a digital environment that accurately reflects real materials, surface properties, and industrial operating conditions.

Digital Twin Simulation

We recreate real-world operating conditions through physically accurate simulation of lighting, cameras, viewpoints, and environmental variables, generating the diverse scenarios and edge cases required for robust AI training.

Synthetic Data Generation for AI Training

We generate high-fidelity synthetic datasets with automated annotations, enabling robotics and AI vision systems to train, validate, and perform reliably in real-world conditions.

99% Vision Task Success Rate

Measured on an active production line rather than in a controlled test environment, these results demonstrate the real-world effectiveness of AI trained on SKAI Intelligence synthetic data.

  • Up to 99%

    Computer Vision Accuracy

  • Up to 30%

    Reduction in Sim-to-Real Gap

  • Under 1mm

    Geometric Precision

Material & Surface Modeling

We model material and surface properties with physical accuracy, enabling AI to learn how real-world materials are perceived by industrial sensors.

Lighting & Camera Simulation

We simulate real-world lighting and camera conditions, enabling AI to learn from diverse industrial environments before deployment.

Controlled Randomization

We generate diverse scenarios and edge cases through controlled simulation, improving AI robustness in real-world environments.

Automatic Labeling & Structuring

Precise annotations are generated automatically and delivered as AI-ready datasets, eliminating the need for manual labeling and data preparation.

The price of the last 1%.

Even small gains in vision accuracy can reduce manual intervention and improve operational efficiency.

99% Accuracy Changes the Economics of Automation. Even a 1% gain in vision accuracy can reduce failures, improve efficiency, and accelerate automation.

A Data Flywheel That Continuously Closes the Reality Gap.

Real → Simulation → Real

SKAI Intelligence closes the Real-to-Sim-to-Real loop, continuously refining digital twins, generating new edge cases, and improving AI performance through real-world feedback.

  • 01.Real-World Capture

    We capture and structure real-world assets, materials, lighting, and camera conditions as the foundation of a digital twin environment.

  • 02.Digital Twin Simulation

    Omniverse-native digital twin simulation enables AI training across diverse industrial conditions and edge cases.

  • 03.Synthetic Data Generation

    We generate automatically annotated synthetic datasets at scale for AI training and validation.

  • 04.Real-World Deployment

    We enable AI systems to perceive and operate reliably in real-world industrial environments.

Designed for Real Factory Environments

From shifting lights to material reflections to the unpredictable variables of the floor. Grounded in real factory environments, SKAI Intelligence builds simulation and synthetic data pipelines that let AI operate reliably in the real world.

  • Robotic Assembly

    Pose estimation, part recognition, and verification under real production lighting and cluttered environments.

  • Surface Inspection

    Defect detection across reflective, machined, and coated surfaces, including rare conditions often missing from real-world datasets.

  • Object Detection under Variable Lighting

    Reliable object detection across varying lighting conditions and real-world environmental changes.

  • Precision Grasping

    Sub-millimeter digital twin precision enables reliable transfer from simulation to real-world grasping.

FAQ

  • A

    It transforms real-world environments into digital twins and generates automatically annotated training data at scale, eliminating the need for extensive real-world data collection.

  • A

    No. Because it is built from real-world scans, it has reduced domain gap by up to 30% and achieved up to 99% vision task success in live production environments.

  • A

    As AI-ready images and 3D datasets with automated annotations included.

  • A

    Yes. We scan your equipment and environment at sub-millimeter precision to generate data optimized for your operational conditions.

  • A

    We have validated our technology through various PoCs with a global robotics company.