Synthetic sensor data for physical AI

The training data layer for real-world AI.

Maati generates controlled LiDAR, camera, radar, and IoT data for rare physical-world conditions, so robotics and edge AI teams can train before deployment.

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API-first scenario generation Edge cases on demand Sensor formats ready for training

Grants and research support

Backed by research programs and early ecosystem support.

The problem

The moments that break physical AI rarely happen on schedule.

A reflection on wet pavement. A half-hidden pallet. Low sun through glass. These are the cases that decide whether a model works outside the lab, and they are the hardest ones to collect repeatedly.

Today

Teams wait for the world to reveal the gap.

Sensors go into the field. Data comes back unevenly. Rare conditions appear when they appear, often after weeks of collection, cleanup, labeling, and format work.

With Maati

The gap becomes a scenario you can recreate.

Define the condition, sensor, environment, and variation. Maati generates controlled synthetic data around the cases your system needs to understand before deployment.

Rare conditions Weather, glare, shadows, occlusion, motion, terrain, reflections, and sensor artifacts.

Repeatable scenarios Run the same physical event with controlled changes instead of waiting for it to happen again.

Earlier confidence Evaluate the cases that matter before the system meets them in production.

How it works

Describe the condition. Generate the sensor data.

Choose the physical scenario, sensor outputs, and variations. Maati returns synthetic datasets designed for training, validation, and regression testing.

Define the scenario

Start with the condition your system struggles with: heavy rain, night lighting, reflective surfaces, odd object placement, or unusual pedestrian behavior.

Generate variations

Programmatically vary environment, sensor view, weather, timing, object placement, and edge-case parameters to build coverage around the failure.

Train and evaluate

Use model-ready synthetic data for training, validation, and regression testing before sending your physical AI system into the field.

Generate synthetic LiDAR data Python

import maati

client = maati.Client(api_key="your_api_key")

dataset = client.generate(
    sensor_type="lidar",
    scenario="urban_intersection",
    parameters={
        "weather": "heavy_rain",
        "time_of_day": "night",
        "surface": "wet_reflective_pavement",
        "pedestrian_density": "high",
        "num_frames": 10000
    },
    output_format="pytorch"
)

# Returns training data and metadata for evaluation
print(dataset.download_url)

Use cases

Built for machines that operate outside the lab.

Maati helps teams cover conditions that are risky, slow, or expensive to collect physically.

Autonomous vehicles

Generate adverse weather, construction zones, unusual pedestrian behavior, glare, and sensor-specific driving edge cases.

Robotics

Train warehouse, delivery, inspection, and agricultural robots across synthetic surfaces, lighting, clutter, occlusion, and obstacles.

IoT systems

Create synthetic sensor readings for agriculture, industrial monitoring, environmental sensing, and device-scale model development.

Drones and UAVs

Produce aerial imagery, terrain mapping, navigation, and weather-condition datasets without relying on expensive flight hours.

Contact

Tell us the scenario you need to generate.

Share your sensor type, target environment, and desired dataset size. We’ll respond with a suggested generation setup.