Training Data Layer
for modern physical AI

We're democratizing AI by letting anyone utilize the training data only tech giants could afford to collect.
Generate LiDAR, camera, radar, and IoT data via API. Pay per scenario.

Demo Request Access
Grants
The Problem

Real-world data collection is prohibitively expensive

Training ML models requires massive datasets only tech giants can collect. Tesla has billions of miles. Google has petabytes of searches. Startups can't wait years to collect data. They need to ship fast.


Traditional Approach
  • Deploy physical sensors in target environments
  • Wait weeks/months for data collection
  • Clean, label, and format raw data
  • Can't simulate edge cases or rare events
  • Limited environmental diversity
Time to production: 3-6 months
Maati API
  • Generate 1M data points via single API call
  • Instant generation in hours, not months
  • Pre-formatted for PyTorch/TensorFlow
  • Simulate any edge case programmatically
  • Infinite environmental variations
Time to production: < 24 hours
Ask about custom sensors
How It Works
Patent-pending framework generates physically accurate sensor data. Built on DASM (Dynamic Adversarial Modeling), proven in agricultural applications with 19% performance improvements.
Generate synthetic LiDAR data
Python
import syntheticedge

# Initialize client
client = syntheticedge.Client(api_key="your_api_key")

# Generate synthetic sensor data
response = client.generate(
    sensor_type="lidar",
    scenario="urban_intersection",
    parameters={
        "weather": "heavy_rain",
        "time_of_day": "night",
        "pedestrian_density": "high",
        "num_frames": 10000
    }
)

# Returns: 10K frames of LiDAR point clouds
# Format: PyTorch tensors, ready for training
print(response.data_url)  # S3 download link
print(response.cost)       # $1.20
Use Cases

Built for edge AI companies

Autonomous Vehicles

Simulate rare driving scenarios

Generate synthetic sensor data for edge cases like adverse weather, construction zones, or unusual pedestrian behavior. Train models on scenarios that would take months to collect naturally.

Robotics

Test in infinite environments

Create training data for warehouse robots, agricultural drones, or inspection bots across thousands of synthetic environments. Validate performance before physical deployment.

IoT Devices

Scale without hardware costs

Generate synthetic sensor readings for smart agriculture, industrial monitoring, or environmental sensing applications. Train models without deploying thousands of physical sensors.

Drones & UAVs

Synthetic flight testing

Create aerial imagery, terrain mapping, and navigation data across diverse landscapes and conditions. Test perception systems without flight hours.

Tell us your use case
Contact

Let’s talk about your scenarios

Share your sensor type, target environment, and how many frames you need. We’ll respond with a suggested setup + pricing.