SCAILAB provides a perception infrastructure platform for Physical AI that removes the data bottleneck faced by robotics teams, enabling them to build, train, and deploy autonomous systems much faster and at scale. The platform lets users drag‑and‑drop procedural environments, actors, and sensor configurations—including RGB‑D, LiDAR, and thermal cameras—to generate synthetic training data for logistics, mining, inspection, and surveillance applications. By automating simulation, labeling, and deployment tooling, SCAILAB compresses experimentation cycles from months to days.
Funding
Funding not disclosed
Founders
Product
Problem
Robotics teams often spend months building simulation environments, labeling data, and creating deployment pipelines before their perception models can be tested in real-world scenarios. This data collection and simulation bottleneck slows development cycles and increases the risk of deploying models trained on outdated or insufficient data.
Solution
SCAILAB offers a cloud‑based perception infrastructure that lets users create synthetic environments—such as warehouses, mines, and urban settings—through a drag‑and‑drop interface. The platform supports a range of actors, lighting conditions, and sensor modalities (RGB, RGB‑D, LiDAR, thermal, event cameras), enabling the generation of diverse, labeled datasets on demand. Users can train autonomous perception models directly on this synthetic data and deploy them to physical robots without the need for separate simulation or labeling tools. By automating environment creation and data synthesis, SCAILAB compresses experimentation cycles from months to days, facilitating faster, safer, and scalable development of Physical AI systems.
Target Audience
Primary customers are robotics and automation teams developing perception stacks for logistics, mining, inspection, surveillance, and other industrial applications that require large‑scale, safety‑critical deployments.
Features
- Procedural generation of realistic backgrounds (warehouses, mines, urban zones, interiors) with configurable weather, time‑of‑day, and HDRI lighting sweeps
- Actor library including humans, vehicles, machines, and scripted rare‑event behaviors for scenario diversity
- Multi‑sensor output support (RGB, RGB‑D, LiDAR, thermal, event‑camera) with adjustable sensor noise models
- Integrated cloud pipeline for dataset generation, model training, and deployment without external tooling
- Real‑time rendering powered by Unreal Engine 5.4, delivering high‑resolution (e.g., 1920×1080 RGB‑D) frames at low latency
- Drag‑and‑drop interface for rapid configuration of scenes, actors, and sensor setups, reducing setup time from weeks to minutes