Train Autonomous AI in Real-World Digital Twins

🌍 Problem

Autonomous systems trained on structured, Western environments struggle in real-world conditions like India. Unstructured traffic, mixed vehicles, unpredictable pedestrians, and inconsistent road behavior create a significant gap between simulation and deployment.Collecting real-world data is expensive, slow, and limited in edge cases—making safe and scalable AI training a major challenge.

💡 Solution

We create high-fidelity digital twins of real cities using advanced Gaussian Splatting techniques and integrate them into simulation platforms like CARLA.Our platform enables AI systems to be trained and validated in environments that closely match real-world deployment conditions.

⚙️ Key Features

• Photorealistic Digital Twins

  Real-world city reconstruction with unmatched visual fidelity

• City-Specific Training

  Train AI for specific deployment locations

• Simulation Integration

  Seamlessly works with existing autonomy stacks

• Automated Labeling

  Generate AI-ready datasets with ground-truth annotations

• Indian Traffic Modeling

  Capture real-world complexity: mixed traffic, dense congestion, unstructured roads

🎯 Benefits

• Reduce Sim-to-Real Gap

  Train in environments that reflect actual deployment conditions

• Accelerate Development

  Minimize dependence on costly real-world data collection

• Improve Safety

  Validate models before real-world deployment

• Scale Across Cities

  Quickly adapt AI systems to new locations

🧠 Use Cases

• ADAS validation for automotive OEMs

• Autonomous vehicle perception training

• Robotics and delivery systems

• Smart city and urban mobility simulations

🏗️ How It Works

1. Capture real-world data from target city

2. Reconstruct environment using Gaussian Splatting

3. Integrate into simulation engine

4. Generate labeled datasets and training scenarios

5. Train and validate AI models

🌟 Vision

To become the foundational infrastructure for training and deploying autonomous systems in complex, real-world environments across India and emerging markets.